{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T16:52:19Z","timestamp":1783183939804,"version":"3.54.6"},"reference-count":79,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T00:00:00Z","timestamp":1670457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers &amp; Geosciences"],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1016\/j.cageo.2022.105284","type":"journal-article","created":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T03:40:14Z","timestamp":1670470814000},"page":"105284","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":135,"special_numbering":"C","title":["Impact of dataset size and convolutional neural network architecture on transfer learning for carbonate rock classification"],"prefix":"10.1016","volume":"171","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-5362-4058","authenticated-orcid":false,"given":"Harriet L.","family":"Dawson","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olivier","family":"Dubrule","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C\u00e9dric M.","family":"John","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.cageo.2022.105284_bib1","author":"Adobe Inc"},{"key":"10.1016\/j.cageo.2022.105284_bib2","series-title":"12th {USENIX} Symposium on Operating Systems Design and Implementation (OSDI)","first-page":"265","article-title":"Tensorflow: a system for large-scale machine learning","author":"Abadi","year":"2016"},{"key":"10.1016\/j.cageo.2022.105284_bib3","doi-asserted-by":"crossref","first-page":"2419","DOI":"10.1007\/s00170-017-1039-x","article-title":"On the use of machine learning methods to predict component reliability from data-driven industrial case studies","volume":"94","author":"Alsina","year":"2017","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"10.1016\/j.cageo.2022.105284_bib4","first-page":"279","article-title":"Application of a neural network to the problem of mineral identification from well logs","volume":"3","author":"Baldwin","year":"1990","journal-title":"Log. Anal."},{"key":"10.1016\/j.cageo.2022.105284_bib5","doi-asserted-by":"crossref","DOI":"10.1016\/j.cageo.2019.104330","article-title":"Deep convolutions for in-depth automated rock typing","volume":"135","author":"Baraboshkin","year":"2020","journal-title":"Comput. Geosci."},{"issue":"2","key":"10.1016\/j.cageo.2022.105284_bib6","doi-asserted-by":"crossref","first-page":"1828","DOI":"10.1093\/mnras\/stab325","article-title":"CNN architecture comparison for radio galaxy classification","volume":"503","author":"Becker","year":"2021","journal-title":"Mon. Not. Roy. Astron. Soc."},{"key":"10.1016\/j.cageo.2022.105284_bib7","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1126\/science.aau0323","article-title":"Machine learning for data-driven discovery in solid Earth geoscience","volume":"363","author":"Bergen","year":"2019","journal-title":"Science"},{"key":"10.1016\/j.cageo.2022.105284_bib8","series-title":"A Machine Learning Approach to Facies Classification Using Well Logs","first-page":"2137","author":"Bestagini","year":"2017"},{"key":"10.1016\/j.cageo.2022.105284_bib9","doi-asserted-by":"crossref","unstructured":"Betzler, C., Eberli, G.P., Alvarez Zarikian, C.A., And the expedition 359 scientists (2017) Maldives monsoon and sea level. Proceedings of the International Ocean Discovery Program: College Station, TX (International Ocean Discovery Program). doi: 10.14379\/iodp.proc.359.","DOI":"10.14379\/iodp.proc.359.2017"},{"key":"10.1016\/j.cageo.2022.105284_bib10","first-page":"122","article-title":"The OpenCV library","volume":"120","author":"Bradski","year":"2000","journal-title":"Dr. Dobb's J. Softw. Tools Prof. Program."},{"issue":"4","key":"10.1016\/j.cageo.2022.105284_bib11","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1046\/j.1440-0952.2000.00807.x","article-title":"Artificial neural networks: a new method for mineral prospectivity mapping","volume":"47","author":"Brown","year":"2000","journal-title":"Aust. J. Earth Sci."},{"key":"10.1016\/j.cageo.2022.105284_bib12","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1007\/s42452-021-04656-8","article-title":"Lithology classification of whole core CT scans using convolutional neural networks","volume":"3","author":"Chawshin","year":"2021","journal-title":"SN Appl. Sci."},{"key":"10.1016\/j.cageo.2022.105284_bib14","first-page":"1","article-title":"Efficient classification of seismic textures","volume":"2018","author":"Chevitarese","year":"2018"},{"key":"10.1016\/j.cageo.2022.105284_bib15","author":"Chollet"},{"key":"10.1016\/j.cageo.2022.105284_bib16","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1002\/cem.1290","article-title":"Evaluation of model predictive ability by external validation techniques","volume":"24","author":"Consonni","year":"2010","journal-title":"J. Chemometr."},{"key":"10.1016\/j.cageo.2022.105284_bib17","first-page":"73","article-title":"Principal results and summary","volume":"133","author":"Davies","year":"1991"},{"issue":"1","key":"10.1016\/j.cageo.2022.105284_bib18","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1190\/1.2392789","article-title":"Unsupervised seismic facies analysis using wavelet transform and self-organizing maps","volume":"72","author":"de Matos","year":"2007","journal-title":"Geophysics"},{"key":"10.1016\/j.cageo.2022.105284_bib19","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"248","article-title":"ImageNet: a large-scale hierarchical image database","author":"Deng","year":"2009"},{"key":"10.1016\/j.cageo.2022.105284_bib20","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1007\/s10596-021-10033-6","article-title":"Deep learning for lithological classification of carbonate rock micro-CT images","volume":"25","author":"dos Anjos","year":"2021","journal-title":"Comput. Geosci."},{"issue":"5","key":"10.1016\/j.cageo.2022.105284_bib21","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1016\/j.cageo.2006.08.011","article-title":"Comparison of four approaches to a rock facies classification problem","volume":"33","author":"Dubois","year":"2007","journal-title":"Comput. Geosci."},{"key":"10.1016\/j.cageo.2022.105284_bib22","series-title":"Classification of Carbonate Rocks","first-page":"108","article-title":"Classification of carbonate rocks according to depositional texture","volume":"vol. 1","author":"Dunham","year":"1962"},{"key":"10.1016\/j.cageo.2022.105284_bib23","first-page":"730","article-title":"A late devonian reef tract on northeastern banks island, NWT","volume":"19","author":"Embry","year":"1971","journal-title":"Bull. Can. Petrol. Geol."},{"key":"10.1016\/j.cageo.2022.105284_bib24","series-title":"Earth Observation Open Science and Innovation","first-page":"69","article-title":"Citizen science for observing and understanding the Earth","volume":"vol. 15","author":"Haklay","year":"2018"},{"issue":"10","key":"10.1016\/j.cageo.2022.105284_bib25","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1190\/tle35100906.1","article-title":"Facies classification using machine learning","volume":"35","author":"Hall","year":"2016","journal-title":"Lead. Edge"},{"issue":"3","key":"10.1016\/j.cageo.2022.105284_bib26","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1190\/tle36030267.1","article-title":"Distributed collaborative prediction: results of the machine learning contest","volume":"36","author":"Hall","year":"2017","journal-title":"Lead. Edge"},{"key":"10.1016\/j.cageo.2022.105284_bib27","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s11004-019-09838-0","article-title":"Value of geologically derived features in machine learning facies classification","volume":"52","author":"Halotel","year":"2020","journal-title":"Math. Geosci."},{"key":"10.1016\/j.cageo.2022.105284_bib28","series-title":"IEEE Conference on Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"issue":"2","key":"10.1016\/j.cageo.2022.105284_bib29","doi-asserted-by":"crossref","first-page":"108","DOI":"10.3390\/info11020108","article-title":"Fastai: a layered API for deep learning","volume":"11","author":"Howard","year":"2020","journal-title":"Information"},{"key":"10.1016\/j.cageo.2022.105284_bib30","series-title":"Densely Connected Convolutional Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"2261","author":"Huang","year":"2017"},{"key":"10.1016\/j.cageo.2022.105284_bib31","series-title":"Regional Eagle Ford Modeling: Integrating Facies, Rock Properties, and Stratigraphy to Understand Geologic and Reservoir Characteristics","author":"Hull","year":"2015"},{"issue":"3","key":"10.1016\/j.cageo.2022.105284_bib32","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: a 2D graphics environment","volume":"9","author":"Hunter","year":"2007","journal-title":"Comput. Sci. Eng."},{"key":"10.1016\/j.cageo.2022.105284_bib33","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.petrol.2018.11.023","article-title":"Lithological facies classification using deep convolutional neural network","volume":"174","author":"Imamverdiyev","year":"2019","journal-title":"J. Petrol. Sci. Eng."},{"issue":"2","key":"10.1016\/j.cageo.2022.105284_bib34","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1111\/sed.12168","article-title":"Advanced classification of carbonate sediments based on physical properties","volume":"62","author":"Insua","year":"2014","journal-title":"Sedimentology"},{"key":"10.1016\/j.cageo.2022.105284_bib35","series-title":"Proceedings of the Ocean Drilling Program","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2973\/odp.proc.ir.194.2002","article-title":"Leg 194 summary","author":"Isern","year":"2002"},{"key":"10.1016\/j.cageo.2022.105284_bib36","series-title":"IEEE Northwest Russia Conference on Mathematical Methods in Engineering and Technology","first-page":"425","article-title":"Core photo lithological interpretation based on computer analyses","author":"Ivchenko","year":"2018"},{"key":"10.1016\/j.cageo.2022.105284_bib37","doi-asserted-by":"crossref","first-page":"250","DOI":"10.2118\/204216-PA","article-title":"Deep-learning-based vuggy facies identification from borehole images","volume":"24","author":"Jiang","year":"2021","journal-title":"SPE Reservoir Eval. Eng."},{"issue":"6","key":"10.1016\/j.cageo.2022.105284_bib38","first-page":"750","article-title":"Geological feature prediction using image-based machine learning","volume":"59","author":"Jobe","year":"2018","journal-title":"Petrophysics"},{"key":"10.1016\/j.cageo.2022.105284_bib39","series-title":"AI to Improve the Reliability and Reproducibility of Descriptive Data: a Case Study Using Convolutional Neural Networks to Recognize Carbonate Facies in Cores","author":"John","year":"2019"},{"key":"10.1016\/j.cageo.2022.105284_bib40","series-title":"TGS Salt Identification Challenge","author":"Kaggle","year":"2019"},{"issue":"8","key":"10.1016\/j.cageo.2022.105284_bib41","doi-asserted-by":"crossref","first-page":"1544","DOI":"10.1109\/TKDE.2018.2861006","article-title":"Machine learning for the geosciences: challenges and opportunities","volume":"31","author":"Karpatne","year":"2019","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"3","key":"10.1016\/j.cageo.2022.105284_bib42","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1093\/biosci\/biz010","article-title":"Using semistructured surveys to improve citizen science data for monitoring biodiversity","volume":"69","author":"Kelling","year":"2019","journal-title":"Bioscience"},{"key":"10.1016\/j.cageo.2022.105284_bib43","series-title":"Adam: A Method for Stochastic Optimization","author":"Kingma","year":"2014"},{"key":"10.1016\/j.cageo.2022.105284_bib44","series-title":"Geology","first-page":"IN51A","article-title":"Synthetic geology - exploring the \"what if?","author":"Klump","year":"2015"},{"key":"10.1016\/j.cageo.2022.105284_bib45","doi-asserted-by":"crossref","DOI":"10.1016\/j.marpetgeo.2020.104687","article-title":"Fully automated carbonate petrography using deep convolutional neural networks","volume":"122","author":"Koeshidayatullah","year":"2020","journal-title":"Mar. Petrol. Geol."},{"issue":"1561","key":"10.1016\/j.cageo.2022.105284_bib46","article-title":"A neural network framework for cognitive bias","volume":"9","author":"Korteling","year":"2018","journal-title":"Front. Psychol."},{"key":"10.1016\/j.cageo.2022.105284_bib47","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.cageo.2015.11.006","article-title":"Automatic classification of seismic events within a regional seismograph network","volume":"87","author":"Kortstr\u00f6m","year":"2016","journal-title":"Comput. Geosci."},{"key":"10.1016\/j.cageo.2022.105284_bib48","doi-asserted-by":"crossref","first-page":"1843","DOI":"10.1111\/sed.12293","article-title":"The petrographic description of carbonate facies: are we all speaking the same language?","volume":"63","author":"Lokier","year":"2016","journal-title":"Sedimentology"},{"key":"10.1016\/j.cageo.2022.105284_bib49","series-title":"Seeking a Better Way to Find Web Images","author":"Markoff","year":"2012"},{"key":"10.1016\/j.cageo.2022.105284_bib50","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1007\/s11004-019-09832-6","article-title":"Stochastic seismic waveform inversion using generative adversarial networks as a geological prior","volume":"52","author":"Mosser","year":"2020","journal-title":"Math. Geosci."},{"key":"10.1016\/j.cageo.2022.105284_bib51","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-019-45748-1","article-title":"CRED: a deep residual network of convolutional and recurrent units for earthquake signal detection","volume":"9","author":"Mousavi","year":"2019","journal-title":"Sci. Rep."},{"key":"10.1016\/j.cageo.2022.105284_bib52","series-title":"IEEE International Symposium on Circuits and Systems (ISCAS)","first-page":"1","article-title":"Deep CNNs for microscopic image classification by exploiting transfer learning and feature concatenation","author":"Nguyen","year":"2018"},{"key":"10.1016\/j.cageo.2022.105284_bib53","series-title":"Machine Learning in Petroleum Geoscience: Constructing EarthNET","author":"Oikonomou","year":"2017"},{"key":"10.1016\/j.cageo.2022.105284_bib54","first-page":"85","article-title":"A guide to NumPy","volume":"1","author":"Oliphant","year":"2006","journal-title":"Methods"},{"issue":"10","key":"10.1016\/j.cageo.2022.105284_bib55","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.cageo.2022.105284_bib56","first-page":"2825","article-title":"Scikit-learn: machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.cageo.2022.105284_bib57","doi-asserted-by":"crossref","first-page":"SF27","DOI":"10.1190\/INT-2018-0245.1","article-title":"Convolutional neural networks as aid in core lithofacies classification","volume":"7","author":"Pires de Lima","year":"2019","journal-title":"Interpretation"},{"key":"10.1016\/j.cageo.2022.105284_bib58","doi-asserted-by":"crossref","DOI":"10.1016\/j.cageo.2020.104481","article-title":"Petrographic microfacies classification with deep convolutional neural networks","volume":"142","author":"Pires de Lima","year":"2020","journal-title":"Comput. Geosci."},{"key":"10.1016\/j.cageo.2022.105284_bib59","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/S0893-6080(98)00116-6","article-title":"On the momentum term in gradient descent learning algorithms","volume":"12","author":"Qian","year":"1999","journal-title":"Neural Network."},{"issue":"3","key":"10.1016\/j.cageo.2022.105284_bib60","doi-asserted-by":"crossref","first-page":"A39","DOI":"10.1190\/geo2017-0524.1","article-title":"Unsupervised seismic facies analysis via deep convolutional autoencoders","volume":"83","author":"Qian","year":"2018","journal-title":"Geophysics"},{"key":"10.1016\/j.cageo.2022.105284_bib61","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","article-title":"Deep learning and process understanding for data-driven Earth system science","volume":"566","author":"Reichstein","year":"2019","journal-title":"Nature"},{"key":"10.1016\/j.cageo.2022.105284_bib63","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.beproc.2018.01.004","article-title":"Evaluation of the confusion matrix method in the validation of an automated system for measuring feeding behaviour of cattle","volume":"148","author":"Ruuska","year":"2018","journal-title":"Behav. Process."},{"key":"10.1016\/j.cageo.2022.105284_bib64","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.jappgeo.2018.06.012","article-title":"Machine learning approaches for petrographic classification of carbonate-siliciclastic rocks using well logs and textural information","volume":"155","author":"Saporetti","year":"2018","journal-title":"J. Appl. Geophys."},{"issue":"5","key":"10.1016\/j.cageo.2022.105284_bib66","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","article-title":"Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning","volume":"35","author":"Shin","year":"2016","journal-title":"IEEE Trans. Med. Imag."},{"issue":"8111","key":"10.1016\/j.cageo.2022.105284_bib67","first-page":"1","article-title":"Classification of volcanic ash particles using a convolutional neural network and probability","volume":"8","author":"Shoji","year":"2018","journal-title":"Sci. Rep."},{"key":"10.1016\/j.cageo.2022.105284_bib68","series-title":"International Conference on Learning Representations","first-page":"1","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014"},{"key":"10.1016\/j.cageo.2022.105284_bib69","series-title":"Introduction to Deep Learning: from Logical Calculus to Artificial Intelligence","first-page":"191","author":"Skansi","year":"2018"},{"key":"10.1016\/j.cageo.2022.105284_bib70","series-title":"IEEE Winter Conference on Applications of Computer Vision","first-page":"464","article-title":"Cyclical learning rates for training neural networks","author":"Smith","year":"2017"},{"key":"10.1016\/j.cageo.2022.105284_bib71","series-title":"A Disciplined Approach to Neural Network Hyper-Parameters: Part 1--learning Rate, Batch Size, Momentum, and Weight Decay","author":"Smith","year":"2018"},{"key":"10.1016\/j.cageo.2022.105284_bib72","series-title":"Advances in Intelligent Data Analysis XV. IDA 2016. Lecture Notes in Computer Science 9897","article-title":"On the impact of data set size in transfer learning using deep neural networks","author":"Soekhoe","year":"2016"},{"key":"10.1016\/j.cageo.2022.105284_bib73","series-title":"IEEE Conference on Computer Vision and Pattern Recognition","first-page":"2818","article-title":"Rethinking the inception architecture for computer vision","author":"Szegedy","year":"2016"},{"key":"10.1016\/j.cageo.2022.105284_bib74","article-title":"A survey on deep transfer learning","volume":"vol. 11141","author":"Tan","year":"2018"},{"key":"10.1016\/j.cageo.2022.105284_bib75","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.biocon.2014.10.021","article-title":"Global change and local solutions: tapping the unrealized potential of citizen science for biodiversity research","volume":"181","author":"Theobald","year":"2015","journal-title":"Biol. Conserv."},{"key":"10.1016\/j.cageo.2022.105284_bib76","article-title":"Detecting volcanic ash plume components from space using machine learning techniques","author":"Torrisi","year":"2021","journal-title":"AGU 2021 Fall Meeting"},{"key":"10.1016\/j.cageo.2022.105284_bib77","series-title":"Facies classification from well logs using an inception convolutional network","first-page":"1","author":"Tschannen","year":"2017"},{"issue":"10","key":"10.1016\/j.cageo.2022.105284_bib78","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.1190\/1.1518444","article-title":"Interactive seismic facies classification using textural attributes and neural networks","volume":"21","author":"West","year":"2002","journal-title":"Lead. Edge"},{"key":"10.1016\/j.cageo.2022.105284_bib79","series-title":"Expedition 303\/306 Scientists","first-page":"1","article-title":"Data report: digital core images as data: an example from IODP Expedition 303","volume":"303\/306","author":"Wilkens","year":"2009"},{"key":"10.1016\/j.cageo.2022.105284_bib80","first-page":"611","article-title":"Convolutional neural networks: an overview and application in radiology","volume":"9","author":"Yamashita","year":"2018","journal-title":"Insight Image."},{"key":"10.1016\/j.cageo.2022.105284_bib81","first-page":"3320","article-title":"How transferable are features in deep neural networks?","volume":"2","author":"Yosinski","year":"2014"},{"key":"10.1016\/j.cageo.2022.105284_bib82","series-title":"Paper Presented at the EAGE Conference and Exhibition, Paris, France, 12-15 June","article-title":"Deep learning method for lithology identification from borehole images","author":"Zhang","year":"2017"}],"container-title":["Computers &amp; Geosciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S0098300422002333?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S0098300422002333?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T18:34:35Z","timestamp":1758825275000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/linkinghub.elsevier.com\/retrieve\/pii\/S0098300422002333"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2]]},"references-count":79,"alternative-id":["S0098300422002333"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.cageo.2022.105284","relation":{},"ISSN":["0098-3004"],"issn-type":[{"value":"0098-3004","type":"print"}],"subject":[],"published":{"date-parts":[[2023,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Impact of dataset size and convolutional neural network architecture on transfer learning for carbonate rock classification","name":"articletitle","label":"Article Title"},{"value":"Computers & Geosciences","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.cageo.2022.105284","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"105284"}}