{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":769,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":769,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"164eed1457d5","filters":{"topic":"Remote-Sensing Image Classification"}},"results":[{"id":"W2157026765","doi":"10.1016/j.isprsjprs.2013.03.006","title":"Change detection from remotely sensed images: From pixel-based to object-based approaches","year":2013,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":1478,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"","keywords":"Change detection; Computer science; Remote sensing; Pixel; Land cover; Context (archaeology); Earth observation; Object detection; Data mining; Land use; Artificial intelligence; Satellite; Geography; Pattern recognition (psychology)","retraction":null,"screen_n_in":null,"score":{"opus":0.03794654556780053,"gpt":0.2246144915920411,"spread":0.1866679460242406,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003410903,0.0004039506,0.0005486525,0.0005915238,0.0001598169,0.0003239193,0.0001468973,0.000273221,0.00001297855],"category_scores_gemma":[0.0002745914,0.0003805316,0.000216194,0.0006619476,0.0001088973,0.000328485,0.00002338426,0.0006255728,0.00003038641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001884352,"about_ca_system_score_gemma":0.00004248768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00339043,"about_ca_topic_score_gemma":0.0002020516,"domain_scores_codex":[0.9978406,0.0001937733,0.0007239185,0.0003730442,0.000418969,0.0004496332],"domain_scores_gemma":[0.9981688,0.0004065664,0.0003189264,0.0004297924,0.0002943751,0.0003815677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005117652,0.00001258704,0.00001346744,0.00002693011,0.00005118766,0.00003039229,0.00022567,0.001326173,0.4952198,2.710659e-8,0.00004502627,0.5029976],"study_design_scores_gemma":[0.0007265094,0.0000898654,0.003402312,0.0003383363,0.00009899533,0.00005686609,0.0002726049,0.590849,0.4031242,0.0001064682,0.0006160545,0.0003187607],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5097589,0.0004022988,0.4884842,0.0002267262,0.0006104819,0.0002925438,0.000007734395,0.000122116,0.00009497426],"genre_scores_gemma":[0.8216839,0.00002706027,0.1772828,0.0003125691,0.0005878402,1.448836e-7,0.00001412893,0.0000852313,0.000006310431],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5895228,"threshold_uncertainty_score":0.9998646,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3207200417","doi":"10.1109/igarss47720.2021.9553499","title":"Global land use / land cover with Sentinel 2 and deep learning","year":2021,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":1249,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Impact","funders":"National Geographic Society","keywords":"Geospatial analysis; Land cover; Remote sensing; Computer science; Deep learning; Satellite imagery; Big data; Cloud computing; Data science; Geomatics; Earth observation; Land use; Artificial intelligence; Satellite; Cartography; Geography; Data mining; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01002611946323105,"gpt":0.2014934866961274,"spread":0.1914673672328963,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002711728,0.00007163838,0.0000735635,0.00001181362,0.00002893342,0.0001219884,0.00001580507,0.00003304932,0.00002758368],"category_scores_gemma":[0.00003897503,0.00006137502,0.00000950583,0.0001052501,0.00001589683,0.0001259765,0.00001332508,0.00006070315,0.00002443093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002301498,"about_ca_system_score_gemma":0.000007047141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002080463,"about_ca_topic_score_gemma":0.00009619193,"domain_scores_codex":[0.9996302,0.00001364936,0.00006378728,0.0001146764,0.00007221475,0.000105494],"domain_scores_gemma":[0.999779,0.00002843968,0.00001021676,0.0001004091,0.00004239805,0.00003954278],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000008291786,0.000008470493,0.9441684,0.00003932263,0.00004402922,0.0000760098,0.00005836211,0.04728587,0.00326388,0.00004873359,0.0004624031,0.00453617],"study_design_scores_gemma":[0.0004511281,0.000006910132,0.5533133,0.00002117767,0.00002028994,0.0001541155,0.00002645001,0.4349713,0.0008531373,0.00001162356,0.01001655,0.000154036],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9452817,0.0001278796,0.04338425,0.00006840592,0.00004461265,0.00003067647,9.99089e-7,0.0002066748,0.01085481],"genre_scores_gemma":[0.993178,0.00005196059,0.004989003,0.00003237944,0.00002911904,2.958008e-7,0.00001506806,0.00001429188,0.001689892],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3908551,"threshold_uncertainty_score":0.2502801,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3088162569","doi":"10.1109/jstars.2020.3026724","title":"Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review","year":2020,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":1055,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval; Institut National de la Recherche Scientifique; Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"Innovation and Networks Executive Agency; European Commission","keywords":"Random forest; Support vector machine; Computer science; Contextual image classification; Remote sensing; Artificial intelligence; Pattern recognition (psychology); Statistical classification; Machine learning; Image (mathematics); Data mining; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.07531220790302398,"gpt":0.271330975073877,"spread":0.196018767170853,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007790878,0.000278141,0.001436664,0.0002252543,0.0001308506,0.0001345184,0.00009147802,0.0001227167,0.00000270009],"category_scores_gemma":[0.0007438011,0.0002413275,0.0002895334,0.001332467,0.00006010991,0.0001504791,0.00001333285,0.0003925331,8.518389e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006965607,"about_ca_system_score_gemma":0.00006568625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001696937,"about_ca_topic_score_gemma":0.0001796213,"domain_scores_codex":[0.997786,0.0001123168,0.001262169,0.000270909,0.0003218602,0.000246755],"domain_scores_gemma":[0.9979556,0.0004444901,0.0005337403,0.0002712302,0.0006465447,0.0001484311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001670879,0.00009797338,0.00005349653,0.2156864,0.1031316,0.0003058902,0.004683728,0.03281091,0.4459367,0.0004122181,0.001667291,0.1935429],"study_design_scores_gemma":[0.001421815,0.00005546419,0.0005099038,0.0009549048,0.02730495,0.00008191938,0.0000554761,0.9676862,0.001305315,0.0001017556,0.0002508463,0.0002714961],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01652883,0.005104394,0.9720381,0.004427311,0.0002278444,0.001395044,0.00001718197,0.0001001745,0.0001610972],"genre_scores_gemma":[0.3693424,0.004145006,0.6253882,0.0005977746,0.0003414705,3.585564e-7,0.00005625704,0.00008585223,0.00004271221],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9348752,"threshold_uncertainty_score":0.984105,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2735042947","doi":"10.1016/j.isprsjprs.2017.06.013","title":"Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications","year":2017,"lang":"en","type":"review","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":843,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Wageningen University and Research; U.S. Geological Survey; University of British Columbia","keywords":"Change detection; Computer science; Algorithm; Thresholding; Preprocessor; Pixel; Series (stratigraphy); Univariate; Artificial intelligence; Time series; Remote sensing; Segmentation; Multivariate statistics; Image (mathematics); Machine learning; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.07904503923903336,"gpt":0.3333358998317321,"spread":0.2542908605926988,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008929148,0.0004234415,0.001709304,0.0004562785,0.0002545019,0.0001871436,0.0001664988,0.0003360265,0.000001726427],"category_scores_gemma":[0.0002275184,0.0003629952,0.0002932022,0.0004328971,0.0002606596,0.0003689242,0.00004860245,0.0006160323,0.00000161893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001424195,"about_ca_system_score_gemma":0.0001323203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009574537,"about_ca_topic_score_gemma":0.000009809624,"domain_scores_codex":[0.9978952,0.0001336295,0.001147436,0.0002889739,0.0002763683,0.0002583736],"domain_scores_gemma":[0.9972404,0.0001103586,0.001666401,0.000464812,0.0003629861,0.0001550147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003058716,0.000004726537,1.549774e-7,0.05995204,0.0001220313,0.00001823883,0.00005265599,0.000001115641,0.0006341639,9.607144e-8,0.00000678714,0.9392049],"study_design_scores_gemma":[0.0001807549,0.00006112993,0.000002787434,0.1953331,0.001708805,0.008218301,0.00002760675,0.01603104,0.0004992523,0.00004394495,0.7774207,0.0004725543],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00002525209,0.9350021,0.0639345,0.000008725024,0.0002363216,0.0006302705,0.00001072405,0.00005958141,0.00009252287],"genre_scores_gemma":[0.00007013177,0.9699535,0.02932338,0.00001454199,0.0005068291,3.150022e-7,0.000008195329,0.0001039136,0.00001923611],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9387324,"threshold_uncertainty_score":0.9998822,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3046027728","doi":"10.1109/mgrs.2020.2979764","title":"Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox","year":2020,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Magazine","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":519,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Hyperspectral imaging; Toolbox; Curse of dimensionality; Pattern recognition (psychology); Feature extraction; Feature (linguistics); Dimensionality reduction","retraction":null,"screen_n_in":null,"score":{"opus":0.02396372032736484,"gpt":0.2504469646480963,"spread":0.2264832443207315,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001821061,0.0001807583,0.0001766477,0.00005518798,0.0002264343,0.0001974556,0.00008809518,0.00008475967,0.00000104734],"category_scores_gemma":[0.0001761157,0.0001435187,0.00004373564,0.000344201,0.0001193841,0.0002675629,0.00001787365,0.0001890944,0.00001828432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008497579,"about_ca_system_score_gemma":0.00002193565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007482679,"about_ca_topic_score_gemma":0.000104448,"domain_scores_codex":[0.9989642,0.00002869359,0.0001437141,0.0004032821,0.0001769055,0.0002832294],"domain_scores_gemma":[0.9994113,0.0001080956,0.00004358278,0.0002117413,0.0000795569,0.0001457643],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002077311,0.000002424446,0.000007149477,0.0000384987,0.000008797543,0.000006158258,0.0005711052,0.0005600736,0.7491832,0.00001482955,0.001777343,0.2478097],"study_design_scores_gemma":[0.0002246188,0.00005213443,0.01174375,0.00007489095,0.00004444037,0.00006937703,0.0002572382,0.9730816,0.005315638,0.0003205222,0.008579,0.0002367349],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3092275,0.001519956,0.6706528,0.01674715,0.0007624849,0.0004709095,0.00001507986,0.0002470602,0.0003570188],"genre_scores_gemma":[0.7674274,0.0004579222,0.229707,0.0013247,0.0007541378,3.054718e-7,0.0000146,0.00004960231,0.0002642625],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9725216,"threshold_uncertainty_score":0.5852522,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2170410701","doi":"10.3233/ifs-141378","title":"Image segmentation by generalized hierarchical fuzzy C-means algorithm","year":2015,"lang":"en","type":"article","venue":"Journal of Intelligent & Fuzzy Systems","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":450,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Fuzzy logic; Image (mathematics); Algorithm; Segmentation","retraction":null,"screen_n_in":null,"score":{"opus":0.02695344713763347,"gpt":0.2648634887363607,"spread":0.2379100415987272,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009929022,0.0002605062,0.0004559791,0.0002757685,0.00004802798,0.0002359467,0.0002767969,0.0001408475,0.000009267194],"category_scores_gemma":[0.0001300181,0.0002297033,0.0001725451,0.0002629347,0.0000615945,0.000426132,0.0000213717,0.0004185088,0.0001460099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006249324,"about_ca_system_score_gemma":0.00008432983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003814629,"about_ca_topic_score_gemma":0.000001731506,"domain_scores_codex":[0.9973436,0.0002168487,0.001181112,0.0001754054,0.0007634033,0.0003196321],"domain_scores_gemma":[0.9982852,0.00008238757,0.0003869171,0.0002823399,0.0005894123,0.0003737615],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001434993,0.0002742312,0.0002358311,0.0003169059,0.0006373225,0.0002328803,0.003239071,0.06057948,0.482638,0.0002228854,0.3492248,0.1022551],"study_design_scores_gemma":[0.00211978,0.0005309265,0.0001273213,0.0004984884,0.0001914419,0.001695546,0.003298806,0.8301898,0.07979364,0.0007356332,0.07993753,0.000881143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06593368,0.004588486,0.9166275,0.0003636724,0.006218354,0.0004984212,0.00003383463,0.0002043053,0.005531743],"genre_scores_gemma":[0.7495027,0.001750889,0.2414868,0.0001615986,0.004349081,0.00002010067,0.0001599047,0.0003456563,0.002223314],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7696103,"threshold_uncertainty_score":0.936703,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2884821113","doi":"10.3390/rs10071119","title":"Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery","year":2018,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":430,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true},"ca_institutions":"University of New Brunswick; Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"Research and Development Corporation of Newfoundland and Labrador; Natural Sciences and Engineering Research Council of Canada; Department of Environment and Conservation, Government of Newfoundland and Labrador; Government of Canada","keywords":"Multispectral image; Convolutional neural network; Random forest; Computer science; Artificial intelligence; Support vector machine; Remote sensing; Deep learning; Pattern recognition (psychology); Satellite imagery; Contextual image classification; Image (mathematics); Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.04236704489195767,"gpt":0.2613133717486997,"spread":0.2189463268567421,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004269488,0.00051005,0.0005428983,0.0002933946,0.000571583,0.0002694383,0.0001344752,0.000279803,0.000008964275],"category_scores_gemma":[0.0002356048,0.0006049064,0.0002393759,0.0004611071,0.0003402969,0.000350873,0.00006977452,0.0004402902,0.00004289017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006186712,"about_ca_system_score_gemma":0.00005233245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001467689,"about_ca_topic_score_gemma":0.00005703827,"domain_scores_codex":[0.9971546,0.0001216087,0.0006968968,0.0006327892,0.0003455257,0.001048613],"domain_scores_gemma":[0.9982342,0.0003674116,0.0002021753,0.0005746317,0.0004253955,0.0001962206],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008469095,0.000005799805,0.00002002796,0.0001162753,0.0001279968,0.00006935883,0.0002861361,0.3346566,0.410783,0.000004355901,0.0006578126,0.2531879],"study_design_scores_gemma":[0.0008202447,0.0000262203,0.0007087421,0.0002446712,0.00007168466,0.0006722971,0.0000490137,0.9921163,0.003198458,0.0001891227,0.001253697,0.0006495977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2303117,0.0001552276,0.7663158,0.00009617643,0.001619773,0.000403417,0.000007779613,0.000577699,0.0005123552],"genre_scores_gemma":[0.6034508,0.00001234991,0.3943041,0.0001666155,0.001805958,4.820156e-9,0.00008162351,0.000140674,0.00003782549],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6574596,"threshold_uncertainty_score":0.9996402,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2165922980","doi":"10.1145/1102351.1102482","title":"Learning from labeled and unlabeled data on a directed graph","year":2005,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":404,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Cluster analysis; Spectral clustering; Directed graph; Graph; Undirected graph; Artificial intelligence; Theoretical computer science; Comparability graph; Clustering coefficient; Labeled data; Algorithm; Pattern recognition (psychology); Line graph; Voltage graph","retraction":null,"screen_n_in":null,"score":{"opus":0.02841037829395239,"gpt":0.2330074743013104,"spread":0.204597096007358,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008219072,0.0001110417,0.0001133996,0.0000682561,0.00004587814,0.00005765513,0.0001225142,0.00005611793,0.00005859263],"category_scores_gemma":[0.000125914,0.000106166,0.000009832118,0.0001484969,0.00002026118,0.0001705812,0.00003825102,0.0001722277,0.0001187688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000271609,"about_ca_system_score_gemma":0.000004575676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004010242,"about_ca_topic_score_gemma":0.00007201098,"domain_scores_codex":[0.9993606,0.00002995891,0.0001317305,0.0002377085,0.0000986786,0.0001413909],"domain_scores_gemma":[0.9993473,0.000115767,0.00001887725,0.0004453082,0.00002144675,0.00005128649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002995382,0.00005573206,0.001865705,0.00002301779,0.0001492404,0.000008391334,0.0004592184,0.0142264,0.6049077,0.0001432342,0.01606573,0.3620657],"study_design_scores_gemma":[0.0004179996,0.00001558983,0.01225582,0.00002410251,0.00001764748,0.000001750504,0.00002729152,0.960315,0.01304031,0.00003642991,0.01367694,0.0001710668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9835224,0.0002033736,0.005343913,0.0003805554,0.00009582578,0.0001090266,0.000008167985,0.001673002,0.008663696],"genre_scores_gemma":[0.9809558,0.0001000804,0.01796491,0.00005351736,0.00008974867,8.342777e-7,0.0001681474,0.00003387153,0.0006330443],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9460887,"threshold_uncertainty_score":0.4329324,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2894165434","doi":"10.1109/mgrs.2018.2854840","title":"New Frontiers in Spectral-Spatial Hyperspectral Image Classification: The Latest Advances Based on Mathematical Morphology, Markov Random Fields, Segmentation, Sparse Representation, and Deep Learning","year":2018,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Magazine","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":323,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Foothills Medical Centre; University of Calgary","funders":"Department of Science, Information Technology and Innovation, Queensland Government; University of Houston; Università degli Studi di Pavia; European Commission; Department of Science and Technology, Ministry of Science and Technology, India; Purdue University","keywords":"Hyperspectral imaging; Remote sensing; Pattern recognition (psychology); Computer science; Artificial intelligence; Image resolution; Spatial analysis; Mathematical morphology; Markov chain; Segmentation; Land cover; Contextual image classification; Image processing; Geography; Image (mathematics); Machine learning; Land use; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01177608000025162,"gpt":0.2451802987250059,"spread":0.2334042187247543,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004128516,0.0002036722,0.0002274767,0.0001803107,0.0002564461,0.0001672278,0.0001080966,0.00009008261,0.00001319131],"category_scores_gemma":[0.0002614046,0.0001637574,0.00003357324,0.0004345475,0.0005775803,0.0002610848,0.00001431381,0.0002894031,0.00002350719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006274189,"about_ca_system_score_gemma":0.00003233969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007248432,"about_ca_topic_score_gemma":0.0001260905,"domain_scores_codex":[0.9985643,0.0001160764,0.0003270787,0.0004335074,0.0002355548,0.0003235058],"domain_scores_gemma":[0.9992232,0.0002108183,0.0000925575,0.0002908286,0.00008181212,0.0001007933],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002451861,0.00002967331,0.000887807,0.00007374681,0.00002056806,0.00007111486,0.002294898,0.007852527,0.1779298,0.00002133222,0.002419217,0.8081541],"study_design_scores_gemma":[0.0009590641,0.00007728211,0.01578516,0.00007088221,0.00002227685,0.0001072539,0.0004794117,0.9743385,0.00651775,0.001206685,0.0002337365,0.0002019674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1505298,0.0001625287,0.8439725,0.002534401,0.0006771619,0.0003045368,9.95782e-7,0.0001252918,0.001692788],"genre_scores_gemma":[0.6665315,0.0003937261,0.3322809,0.0001706647,0.0002979146,4.465924e-7,0.000008228296,0.00002817275,0.0002884131],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.966486,"threshold_uncertainty_score":0.6677834,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2156220628","doi":"10.1016/j.rse.2007.02.019","title":"Integration of spatial–spectral information for the improved extraction of endmembers","year":2007,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":243,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Geological Survey of Canada; University of Alberta","funders":"","keywords":"Endmember; Hyperspectral imaging; Pattern recognition (psychology); Contrast (vision); Pixel; Artificial intelligence; Spectral signature; Projection (relational algebra); Mathematics; Computer science; Basis (linear algebra); Singular value decomposition; Set (abstract data type); Remote sensing; Computer vision; Algorithm; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.0114947609014279,"gpt":0.2250849863342493,"spread":0.2135902254328214,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004189789,0.0001022773,0.0001463469,0.00009621264,0.00002754723,0.000006127448,0.00004848079,0.00007202665,0.000003212172],"category_scores_gemma":[0.00008072704,0.00008884822,0.00007926879,0.00006061689,0.00008018417,0.0001521036,0.000007735408,0.00008502234,0.000001551336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001453294,"about_ca_system_score_gemma":0.000008421063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001918245,"about_ca_topic_score_gemma":0.00003095367,"domain_scores_codex":[0.9990991,0.00001283734,0.0005157801,0.00007397262,0.0001728605,0.0001253882],"domain_scores_gemma":[0.999212,0.0001804339,0.0002859961,0.0002579986,0.00004226185,0.0000212557],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002639659,0.000004896859,0.000001747938,0.00004042702,0.00001653208,5.74314e-8,0.0002654865,0.01346047,0.6389139,0.000009834252,0.00001063504,0.3472496],"study_design_scores_gemma":[0.0001417521,0.00003261161,0.004505669,0.00002884701,0.00002692295,0.000003062573,0.0001595281,0.4846196,0.5099803,0.00004035789,0.0004152136,0.00004611034],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1851016,0.00003184676,0.8138685,0.0000426126,0.0001973717,0.0003144558,0.000003653742,0.00002276142,0.000417264],"genre_scores_gemma":[0.9152045,0.00006677364,0.08464321,0.00000450758,0.00003632849,5.919834e-8,0.00001858684,0.00001425643,0.00001182635],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7301028,"threshold_uncertainty_score":0.3623126,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2811244448","doi":"10.1109/jstars.2018.2846178","title":"Deep Convolutional Neural Network for Complex Wetland Classification Using Optical Remote Sensing Imagery","year":2018,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":241,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland; University of New Brunswick","funders":"Research and Development Corporation of Newfoundland and Labrador","keywords":"Convolutional neural network; Computer science; Remote sensing; Land cover; Random forest; Artificial intelligence; Deep learning; Satellite imagery; Contextual image classification; Thematic map; Pattern recognition (psychology); Feature extraction; Land use; Image (mathematics); Cartography","retraction":null,"screen_n_in":null,"score":{"opus":0.06331301919100238,"gpt":0.2662183573009876,"spread":0.2029053381099852,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005234441,0.000241348,0.0003940794,0.0002612153,0.0003170802,0.0001419512,0.00008575006,0.0002042513,0.000001691738],"category_scores_gemma":[0.0001905955,0.0002533105,0.00007071652,0.0007274834,0.0002084957,0.0001744378,0.00001510815,0.0004591457,9.48809e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001895184,"about_ca_system_score_gemma":0.0001228448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000187872,"about_ca_topic_score_gemma":0.0001470929,"domain_scores_codex":[0.9980839,0.00005972101,0.0008782325,0.0002460224,0.0003015517,0.0004306118],"domain_scores_gemma":[0.9981021,0.0002512501,0.0003117906,0.00021006,0.001021314,0.0001034433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000893404,0.00001013848,0.00007599113,0.00007007422,0.00006925404,0.00001094311,0.000181347,0.07929461,0.6403949,0.0001525513,0.0003013915,0.2793494],"study_design_scores_gemma":[0.0006770323,0.00004028475,0.02241878,0.0001465705,0.00005832533,0.0003039692,0.00005066903,0.9697185,0.003752674,0.001301071,0.001288962,0.0002432028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3571979,0.00006331892,0.6414006,0.0002693293,0.0005829542,0.0002135064,0.000002075936,0.00005178234,0.0002185747],"genre_scores_gemma":[0.4297324,0.00004313919,0.5686232,0.00009125554,0.001445581,1.503455e-8,0.00001609324,0.00003630629,0.00001199766],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8904238,"threshold_uncertainty_score":0.9999919,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3186478549","doi":"10.3390/rs13152869","title":"A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth","year":2021,"lang":"en","type":"review","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":230,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"Natural Resources Canada","keywords":"Change detection; Remote sensing; Earth observation; Cloud computing; Scopus; Computer science; Data science; Trend analysis; Geography; Satellite; Political science","retraction":null,"screen_n_in":null,"score":{"opus":0.1286599980993635,"gpt":0.3448903786845038,"spread":0.2162303805851403,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008900361,0.0002519957,0.001690881,0.0001318586,0.00004508359,0.00003088551,0.0003301269,0.0001609526,3.600753e-7],"category_scores_gemma":[0.0006517168,0.0002098802,0.0002957481,0.000575002,0.00003368591,0.00008697651,0.00008480078,0.0002235053,0.0000053583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008866013,"about_ca_system_score_gemma":0.00005191555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001340824,"about_ca_topic_score_gemma":0.000003249269,"domain_scores_codex":[0.998018,0.0001981128,0.001048182,0.0003144716,0.000244749,0.0001764602],"domain_scores_gemma":[0.9965168,0.0006061733,0.0006612853,0.001989265,0.0001952595,0.00003125325],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[1.343567e-7,0.000001124372,2.22715e-9,0.4866019,0.00008007272,4.306715e-7,0.00001266824,0.000001965909,0.00006155398,5.009499e-7,0.00000497649,0.5132346],"study_design_scores_gemma":[0.00003949541,0.000005268206,2.882339e-7,0.8305467,0.002056759,0.00008846245,0.00002348509,0.02260878,0.0001231736,0.000003040964,0.1443196,0.0001848812],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000001118127,0.9205742,0.07568061,0.000007326498,0.000274499,0.003311531,0.00004651499,0.00007620516,0.00002794499],"genre_scores_gemma":[0.00001753216,0.9871356,0.01220326,0.000005107564,0.0003298336,0.00000257094,0.0001960663,0.00009552808,0.00001445631],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.5130497,"threshold_uncertainty_score":0.8558665,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2964287450","doi":"10.3390/rs11141713","title":"Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":229,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Autoencoder; Support vector machine; Computer science; Pattern recognition (psychology); Land cover; Random forest; Classifier (UML); Contextual image classification; Artificial neural network; Boosting (machine learning); Machine learning; Land use; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.03103516287443537,"gpt":0.2397041795807175,"spread":0.2086690167062821,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002919256,0.000378517,0.0004741526,0.0001794021,0.0001679273,0.0003092982,0.00009694981,0.0002255507,0.000005311182],"category_scores_gemma":[0.00009147201,0.0004027298,0.0001086636,0.0002442041,0.00007087312,0.0003136072,0.00002890293,0.0003426459,0.00002507107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002348183,"about_ca_system_score_gemma":0.0000300548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006995242,"about_ca_topic_score_gemma":0.00007073871,"domain_scores_codex":[0.9981105,0.00007836006,0.0004639101,0.0005377878,0.0002433288,0.000566164],"domain_scores_gemma":[0.9986286,0.0003751034,0.0001554361,0.0005352516,0.0001392387,0.0001664092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003014299,0.00003556116,0.02486498,0.0005472214,0.0002047141,0.00003618679,0.000370027,0.5952082,0.1336446,0.00002955168,0.001689173,0.2430684],"study_design_scores_gemma":[0.00120952,0.0000568359,0.01764727,0.00007831697,0.00006962149,0.00005784178,0.00001573825,0.9767897,0.001555422,0.00001192783,0.002065298,0.0004425414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3759631,0.000242541,0.6208213,0.0001344565,0.00101017,0.0007431948,0.00001000652,0.0004831325,0.0005921096],"genre_scores_gemma":[0.9692416,0.00002808756,0.02973314,0.0001016953,0.0002875876,1.504987e-7,0.0002646919,0.0001411552,0.0002018537],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5932785,"threshold_uncertainty_score":0.9998425,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2042806874","doi":"10.1016/j.isprsjprs.2009.10.002","title":"Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge","year":2009,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":223,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Change detection; Spatial database; Computer science; Segmentation; Scale (ratio); Image resolution; Knowledge base; Spatial analysis; Artificial intelligence; Remote sensing; Data mining; Computer vision; Database; Geography; Cartography","retraction":null,"screen_n_in":null,"score":{"opus":0.02509057868734473,"gpt":0.2473921770408487,"spread":0.222301598353504,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000490253,0.0001929427,0.0003552799,0.0004395171,0.00008837682,0.00005390982,0.00004763953,0.0001181458,0.000001197658],"category_scores_gemma":[0.0001376533,0.0001966105,0.0000574212,0.0002416305,0.00008579034,0.0002982619,0.0000249665,0.0003253348,3.371321e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001845069,"about_ca_system_score_gemma":0.00001325285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001467362,"about_ca_topic_score_gemma":0.00006231868,"domain_scores_codex":[0.9987038,0.0001016102,0.0006006476,0.0001834259,0.0001826087,0.0002278931],"domain_scores_gemma":[0.9992409,0.0001258596,0.0003364042,0.0001466322,0.00005716714,0.0000930356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002212516,0.00001205356,0.00003378019,0.00005009853,0.00001226239,0.00002694837,0.0003907885,0.000176394,0.4578802,1.792427e-7,7.698107e-7,0.5413944],"study_design_scores_gemma":[0.0006386476,0.00008967539,0.0206032,0.001000983,0.00008192049,0.0002677052,0.0001420756,0.7374932,0.2393401,0.0001064093,0.0000586324,0.0001774514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7612599,0.001840223,0.2365094,0.00001694353,0.000214393,0.0001206219,0.000003009728,0.00002543077,0.00001004833],"genre_scores_gemma":[0.9083679,0.0003141319,0.09104972,0.000008417855,0.0002339175,1.531941e-8,0.000002730147,0.00002227737,9.204423e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7373168,"threshold_uncertainty_score":0.8017545,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2121490093","doi":"10.1109/tip.2008.920761","title":"Segmentation by Fusion of Histogram-Based $K$-Means Clusters in Different Color Spaces","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":212,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"","keywords":"Artificial intelligence; Segmentation; Scale-space segmentation; Histogram; Image segmentation; Computer science; Segmentation-based object categorization; Pattern recognition (psychology); Cluster analysis; Computer vision; Minimum spanning tree-based segmentation; Partition (number theory); Region growing; Mathematics; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.01456037304867612,"gpt":0.2304219958023646,"spread":0.2158616227536885,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000716208,0.0002018864,0.0002121469,0.0002978913,0.0001368571,0.00003822655,0.00008533608,0.00008594163,0.00001703761],"category_scores_gemma":[0.000005208276,0.0002045238,0.00006486219,0.0003667095,0.0001298136,0.0003061933,4.986694e-7,0.0002110004,0.00000906298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003078429,"about_ca_system_score_gemma":0.0000383175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002536539,"about_ca_topic_score_gemma":0.0000293386,"domain_scores_codex":[0.9989325,0.00003929051,0.000352278,0.000220405,0.0002462456,0.0002092341],"domain_scores_gemma":[0.9995691,0.00005250045,0.00008859216,0.0001639138,0.00007133448,0.00005455098],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004462305,0.0001938375,0.00004834039,0.0002361867,0.000008215648,0.000004596426,0.0007540923,0.1042096,0.8468335,8.728131e-8,0.0001966784,0.04747019],"study_design_scores_gemma":[0.000571596,0.00003276215,0.0002553797,0.0001324387,0.00001802386,0.000005126642,0.000151061,0.5014843,0.4971523,0.000001971866,0.00004560561,0.0001494632],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.351449,0.0000736181,0.6477578,0.00009718201,0.0001418978,0.000177918,0.000009085115,0.0001589157,0.0001345854],"genre_scores_gemma":[0.9845334,0.0000470954,0.01522207,0.00002741319,0.00001215304,0.00002075948,0.00001451116,0.00004656032,0.00007601187],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6330844,"threshold_uncertainty_score":0.8340238,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2579656072","doi":"10.3390/rs9010088","title":"The Need for Accurate Geometric and Radiometric Corrections of Drone-Borne Hyperspectral Data for Mineral Exploration: MEPHySTo—A Toolbox for Pre-Processing Drone-Borne Hyperspectral Data","year":2017,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":196,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Rio Tinto; Česká geologická služba","keywords":"Hyperspectral imaging; Drone; Remote sensing; Computer science; Terrain; Photogrammetry; Toolbox; Computer vision; Artificial intelligence; Geology; Geography; Cartography","retraction":null,"screen_n_in":null,"score":{"opus":0.1046317570439365,"gpt":0.3335407806333736,"spread":0.2289090235894371,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.001131638,0.0003934452,0.0005540166,0.0006002969,0.001425733,0.001014703,0.00101603,0.0001796907,4.836274e-7],"category_scores_gemma":[0.004054264,0.000367588,0.0001167009,0.0008965666,0.0002633593,0.002337379,0.0001947304,0.0002340372,0.000001103907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002294438,"about_ca_system_score_gemma":0.0001342837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001078004,"about_ca_topic_score_gemma":0.0001043444,"domain_scores_codex":[0.997412,0.00004450988,0.0006918778,0.0008644086,0.0002981136,0.0006890954],"domain_scores_gemma":[0.9949411,0.001006781,0.0004994926,0.00293761,0.0004789305,0.0001360629],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003898269,0.00004970913,0.000009166768,0.0006616066,0.0003337818,0.000003428738,0.0006343562,0.006722501,0.2166289,0.0001618615,0.005597152,0.7688077],"study_design_scores_gemma":[0.001563772,0.0001059585,0.0007959724,0.0001303391,0.0002963674,0.00006218314,0.0007565657,0.9825101,0.01021309,0.0002854232,0.002860482,0.0004197189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1045901,0.002124199,0.8868317,0.002105435,0.001553615,0.001947533,0.0003945092,0.0002590345,0.0001938655],"genre_scores_gemma":[0.7833878,0.0003045541,0.21417,0.00001522888,0.000942645,9.935236e-7,0.0007892685,0.0001441089,0.0002454116],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9757876,"threshold_uncertainty_score":0.9998776,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2177188361","doi":"10.1016/j.asoc.2015.09.045","title":"Gray Wolf Optimizer for hyperspectral band selection","year":2015,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":195,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Hyperspectral imaging; Computer science; Artificial intelligence; Pattern recognition (psychology); Differential evolution; Curse of dimensionality; Pixel; Benchmark (surveying); Selection (genetic algorithm); Heuristic; Optimization problem; Spectral bands; Algorithm; Remote sensing","retraction":null,"screen_n_in":null,"score":{"opus":0.02445991997740743,"gpt":0.2360742637030447,"spread":0.2116143437256372,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000245112,0.0001413723,0.0001513632,0.00006689873,0.00009012868,0.00007190194,0.00008271536,0.00007940746,0.000001788459],"category_scores_gemma":[0.00004980063,0.000156184,0.00004057603,0.0001699326,0.00002261925,0.00005675509,0.00001117035,0.0001350282,0.00003254556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001638417,"about_ca_system_score_gemma":0.00002291272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003445095,"about_ca_topic_score_gemma":0.000001348908,"domain_scores_codex":[0.9991975,0.000008578182,0.0001886638,0.0002083528,0.0001174969,0.0002793818],"domain_scores_gemma":[0.9995637,0.00009655516,0.0000403374,0.0001365607,0.00007847994,0.00008435946],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002871196,0.0000213039,0.0000817602,0.00004595264,0.00004663718,8.186933e-7,0.001106309,0.8184758,0.138501,0.0009886969,0.008068089,0.03263494],"study_design_scores_gemma":[0.0006581635,0.0000173323,0.0001830622,0.00001076782,0.00001822982,0.00001104076,0.0001565733,0.9643989,0.03100977,0.0006322058,0.002682942,0.0002210137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1352145,0.00004280666,0.8552396,0.00005990612,0.0003904308,0.0003022407,7.948066e-7,0.0008317936,0.007917905],"genre_scores_gemma":[0.8243211,8.387017e-7,0.1752615,0.0000382456,0.0002705966,0.000004466251,0.00001182502,0.00004671909,0.00004470374],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6891066,"threshold_uncertainty_score":0.6368997,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3217586766","doi":"10.3390/s21238083","title":"Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative Study","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":186,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Transfer of learning; Computer science; Artificial intelligence; Convolutional neural network; Deep learning; Contextual image classification; Land cover; Remote sensing; Residual; Pattern recognition (psychology); Machine learning; Clipping (morphology); Artificial neural network; Land use; Image (mathematics); Geography; Algorithm; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.06235099648868551,"gpt":0.2757454833334606,"spread":0.2133944868447751,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008052656,0.0001175462,0.0001727115,0.00004167398,0.00008447133,0.0001174914,0.00002776413,0.00005128516,0.000009972665],"category_scores_gemma":[0.0000729051,0.0001199416,0.00002981605,0.000119238,0.00003028897,0.0001120406,0.000005619073,0.0001297936,0.00001854985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003196,"about_ca_system_score_gemma":0.00001047262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006154696,"about_ca_topic_score_gemma":0.00008208455,"domain_scores_codex":[0.9993307,0.00007145514,0.0001558565,0.0002086753,0.0000945244,0.0001388056],"domain_scores_gemma":[0.9994425,0.0002199819,0.00001393382,0.0001556327,0.0001192876,0.00004870459],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001462901,0.0002493361,0.1985335,0.0002105519,0.0004585614,0.00006590073,0.02123042,0.7073261,0.06369728,0.0001558826,0.0006526525,0.00727352],"study_design_scores_gemma":[0.0008809901,0.00004814341,0.176401,0.00001504042,0.00005209644,0.00001678512,0.001160079,0.8139043,0.002377415,0.00001120199,0.004953428,0.000179488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9841524,0.00008853216,0.01465715,0.00007408299,0.00009824089,0.0003141221,0.000004101182,0.0001355891,0.000475822],"genre_scores_gemma":[0.9978516,0.00002669898,0.001434412,0.00001121811,0.00005118881,0.00000748561,0.00003243498,0.00002671374,0.0005582832],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1065782,"threshold_uncertainty_score":0.4891075,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3209733456","doi":"10.3390/rs13214405","title":"Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":186,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Institut National de la Recherche Scientifique; Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"","keywords":"Boosting (machine learning); AdaBoost; Computer science; Artificial intelligence; Multispectral image; Hyperspectral imaging; Random forest; Gradient boosting; Classifier (UML); Decision tree; Statistical classification; Contextual image classification; Machine learning; Pattern recognition (psychology); Ensemble learning; Remote sensing; Image (mathematics); Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.1489573791084855,"gpt":0.3438513717279834,"spread":0.1948939926194979,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005275601,0.0001901574,0.0003130519,0.0001173764,0.0001465237,0.00009916142,0.00006245975,0.0001026039,9.432638e-7],"category_scores_gemma":[0.0004813191,0.0002237427,0.00003426721,0.0002185978,0.0001110394,0.0003106167,0.00004233642,0.0001548795,9.948822e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001562146,"about_ca_system_score_gemma":0.00007799185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002263378,"about_ca_topic_score_gemma":0.00006270613,"domain_scores_codex":[0.998566,0.0001055214,0.0003729392,0.0004648055,0.0002264689,0.0002642608],"domain_scores_gemma":[0.9986665,0.0003322938,0.0001338738,0.0004701257,0.0003213942,0.00007582231],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001218113,0.000005198672,0.00001483545,0.0001074074,0.00004502548,0.000002560023,0.0008644664,0.001053827,0.8347448,0.00008089882,0.0001033356,0.1629655],"study_design_scores_gemma":[0.0006418184,0.00001540118,0.001926795,0.0001755358,0.0001119682,0.00008162106,0.001758474,0.9109095,0.08366197,0.0003370451,0.0001856373,0.0001941893],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7720673,0.001322479,0.2242311,0.0003210134,0.0002018058,0.0004398137,0.00001583695,0.0001300623,0.001270532],"genre_scores_gemma":[0.830493,0.0001226103,0.1691152,0.00001295636,0.00008969546,6.15098e-8,0.0001142306,0.00003374724,0.00001846818],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9098557,"threshold_uncertainty_score":0.9123961,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2125721005","doi":"10.1016/j.rse.2008.01.016","title":"Continuous wavelets for the improved use of spectral libraries and hyperspectral data","year":2008,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":174,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Networks of Centres of Excellence of Canada","keywords":"Endmember; Hyperspectral imaging; Wavelet; Context (archaeology); Spectral signature; Computer science; Remote sensing; Spectral line; Sample (material); Pattern recognition (psychology); Artificial intelligence; Geology; Chemistry; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.05406645263418813,"gpt":0.2127123995321084,"spread":0.1586459468979202,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001039342,0.000139102,0.0002230132,0.0000371936,0.00006702273,0.00001917219,0.0001179425,0.00005606292,0.000001785822],"category_scores_gemma":[0.000092809,0.0001169464,0.00004385203,0.00003896026,0.0003306944,0.0001674129,0.00006543055,0.00009195142,9.482262e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003716707,"about_ca_system_score_gemma":0.00001276266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005096167,"about_ca_topic_score_gemma":0.000003042798,"domain_scores_codex":[0.9991863,0.00001896435,0.0002706729,0.0002126808,0.0001250918,0.0001862773],"domain_scores_gemma":[0.998873,0.0002572317,0.00009005306,0.0007306933,0.00001165273,0.00003738814],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004463367,0.00001833405,0.00004038388,0.00006797418,0.0001185913,0.000007606811,0.0005783291,0.003171329,0.8872379,0.00003324579,0.0005877484,0.1080939],"study_design_scores_gemma":[0.0003103929,0.00004846665,0.008541388,0.00002981489,0.00005965012,0.00007788367,0.00007423996,0.8620515,0.1255191,0.00006521987,0.003083821,0.0001385018],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8232726,0.0007031847,0.1748806,0.0003441325,0.0001469684,0.0004545895,0.00003936599,0.00006447842,0.00009403219],"genre_scores_gemma":[0.7608219,0.0006589454,0.2383153,0.0000121088,0.00005607,3.37499e-8,0.00002066002,0.0000349287,0.00008000216],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8588802,"threshold_uncertainty_score":0.4768938,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2170406089","doi":"10.1109/tgrs.2006.881123","title":"Iterative Spectral Unmixing for Optimizing Per-Pixel Endmember Sets","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":169,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Endmember; Hyperspectral imaging; Pixel; Data set; Algorithm; Pattern recognition (psychology); Computer science; Set (abstract data type); Iterative method; Mathematics; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.0148886535835158,"gpt":0.2383794328275657,"spread":0.2234907792440499,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001683297,0.000218585,0.000179232,0.0002094161,0.0004961755,0.0001931215,0.00006207079,0.00009455567,0.000003145891],"category_scores_gemma":[0.000006091721,0.0002177211,0.00008520637,0.000269342,0.0001550315,0.0003255327,8.92961e-7,0.0002189068,0.000009074059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000117314,"about_ca_system_score_gemma":0.00002460217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000929729,"about_ca_topic_score_gemma":0.00008520237,"domain_scores_codex":[0.9987738,0.00002512541,0.000249697,0.0003708358,0.0001784809,0.0004021241],"domain_scores_gemma":[0.9994908,0.0001162966,0.00004170746,0.0002020761,0.00007369354,0.00007543041],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001529693,0.00001670917,3.25772e-7,0.00004328515,0.00001420858,0.000006380672,0.0006547967,0.07981621,0.5044494,0.00001327306,0.00004818628,0.414922],"study_design_scores_gemma":[0.0002329789,0.00003236638,0.00007701544,0.00009144264,0.00002433514,0.0001059209,0.0001996816,0.8727302,0.1256479,0.0002143204,0.0003989868,0.0002449075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1171594,0.00003352495,0.8803447,0.000160199,0.0007101774,0.000217777,0.000008560542,0.000246599,0.001119035],"genre_scores_gemma":[0.5864823,0.00002824511,0.4130214,0.00003779222,0.00007914957,1.773956e-7,0.000002587302,0.00003124058,0.0003170967],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.792914,"threshold_uncertainty_score":0.8878408,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2104673234","doi":"10.1109/tgrs.2003.817218","title":"Comparing Cooccurrence Probabilities and Markov Random Fields for Texture Analysis of SAR Sea Ice Imagery","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":167,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Synthetic aperture radar; Segmentation; Computer science; Pattern recognition (psychology); Markov random field; Image segmentation; Image texture; Texture (cosmology); Feature (linguistics); Computer vision; Consistency (knowledge bases); Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.01897825191831121,"gpt":0.2401398014572638,"spread":0.2211615495389526,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002400049,0.0001518655,0.0003029077,0.0003076947,0.0002244536,0.00007165048,0.00005712605,0.00008345526,7.966319e-7],"category_scores_gemma":[0.00002514801,0.0001435217,0.00009164104,0.0006149959,0.0002895427,0.0001994337,0.000001418258,0.0001642743,3.797163e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004717442,"about_ca_system_score_gemma":0.00002895004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000198689,"about_ca_topic_score_gemma":0.0002146057,"domain_scores_codex":[0.9990725,0.00002261453,0.0002558731,0.0002842158,0.000149351,0.0002154645],"domain_scores_gemma":[0.9993637,0.0002101497,0.00005337691,0.0002100416,0.00009613291,0.00006660364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000863855,0.00002721726,0.00001644811,0.000287872,0.0001664188,0.000003289088,0.001732465,0.2478557,0.03073751,0.000006643426,0.00001210287,0.719068],"study_design_scores_gemma":[0.000496691,0.00003175992,0.0009768424,0.0001347142,0.0002480117,0.00001975427,0.0002247557,0.9796981,0.01782857,0.0001431228,0.00003393112,0.0001637328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2912168,0.00006965614,0.7081097,0.0001063152,0.000181877,0.0001693702,0.00001173838,0.00006288094,0.00007163139],"genre_scores_gemma":[0.9383346,0.0001308482,0.06144163,0.00003100234,0.00001427771,1.309185e-7,0.00000249405,0.00001066945,0.00003436278],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7318424,"threshold_uncertainty_score":0.5852643,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4400613682","doi":"10.1016/j.rse.2024.114290","title":"Deep learning for urban land use category classification: A review and experimental assessment","year":2024,"lang":"en","type":"review","venue":"Remote Sensing of Environment","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":166,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Remote sensing; Land use; Computer science; Environmental science; Artificial intelligence; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.05394558127974756,"gpt":0.3101542184369268,"spread":0.2562086371571792,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003323175,0.0005686192,0.001409188,0.0001753532,0.00008008358,0.00009160417,0.0001080536,0.0002568552,0.000007701853],"category_scores_gemma":[0.0000464952,0.0005284676,0.0003321022,0.0001284546,0.0001232313,0.00009832922,0.0000753526,0.000498328,0.00003147562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000654084,"about_ca_system_score_gemma":0.00004372691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008275601,"about_ca_topic_score_gemma":8.162046e-7,"domain_scores_codex":[0.9978039,0.0001433782,0.0008825487,0.0006053541,0.0002640641,0.0003007926],"domain_scores_gemma":[0.998769,0.0001968766,0.0003076019,0.0005928982,0.0000214101,0.0001121973],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001275552,0.00001442242,4.278298e-7,0.04622068,0.0002449717,0.000008723435,0.00006034979,0.000299399,0.0003141846,0.00001090962,0.0007694592,0.9520552],"study_design_scores_gemma":[0.000119167,0.00005027049,0.0000121209,0.01827288,0.002481422,0.00009935861,0.00002103913,0.1423456,0.00003592716,0.000008634725,0.8360533,0.0005003385],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00001169443,0.9677619,0.03012882,0.00003778013,0.000232989,0.001403735,0.000008922492,0.0001585981,0.0002555053],"genre_scores_gemma":[0.000280418,0.9703847,0.02840276,0.00001474783,0.0001195409,0.000003309976,0.0002374519,0.0002099829,0.0003470943],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9515548,"threshold_uncertainty_score":0.9997167,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2944413439","doi":"10.1109/tcyb.2019.2915094","title":"Generative Adversarial Networks and Conditional Random Fields for Hyperspectral Image Classification","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":164,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Discriminative model; Conditional random field; Artificial intelligence; Pattern recognition (psychology); Softmax function; CRFS; Computer science; Hyperspectral imaging; Machine learning; Deep learning","retraction":null,"screen_n_in":null,"score":{"opus":0.0135549603455905,"gpt":0.2282175832358199,"spread":0.2146626228902294,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008196742,0.0001580972,0.0001583628,0.00007784131,0.00008784456,0.00006400051,0.000060682,0.0001595368,0.00005264571],"category_scores_gemma":[0.000006388605,0.0001723029,0.00007728449,0.00008751504,0.00006937635,0.0001294434,4.273765e-7,0.0002234658,0.00003420452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008990295,"about_ca_system_score_gemma":0.00001851063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003596754,"about_ca_topic_score_gemma":0.00001172469,"domain_scores_codex":[0.9992363,0.00002772483,0.0002044467,0.000225423,0.0001202763,0.0001858639],"domain_scores_gemma":[0.9994079,0.0002167401,0.00003576478,0.0001928209,0.0000851184,0.00006161332],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000208792,0.00006661539,0.000007817167,0.00003808906,0.0001188146,0.00000112371,0.0003253831,0.8928559,0.09481416,0.0008695768,0.001604298,0.009089461],"study_design_scores_gemma":[0.001902974,0.00008808894,0.0004142742,0.00001323917,0.00005827618,0.000008342719,0.00007567736,0.969793,0.02651371,0.0003231685,0.0006105134,0.0001987681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04910187,0.00003568532,0.9477854,0.0002549332,0.001048204,0.0005189925,0.00004969736,0.0001484559,0.001056723],"genre_scores_gemma":[0.9857606,0.0001047141,0.01329596,0.00006039408,0.0001748786,0.0000291511,0.00004616884,0.00003866667,0.0004894137],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9366588,"threshold_uncertainty_score":0.702631,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4384393272","doi":"10.1016/j.isprsjprs.2023.07.001","title":"An attention-based multiscale transformer network for remote sensing image change detection","year":2023,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":160,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Natural Science Foundation of Jiangxi Province; National Natural Science Foundation of China","keywords":"Computer science; Transformer; Change detection; Artificial intelligence; Architecture; Pattern recognition (psychology); Computer vision; Data mining","retraction":null,"screen_n_in":null,"score":{"opus":0.02535829218572243,"gpt":0.2717231394693599,"spread":0.2463648472836374,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001022805,0.0002998409,0.0004197902,0.0005158749,0.0003241581,0.0001849893,0.00008463807,0.0002245159,0.000001250332],"category_scores_gemma":[0.0001113269,0.0002963218,0.0002659502,0.0009414091,0.0001073485,0.0003854192,0.000006383137,0.0004275473,0.000004484709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001082498,"about_ca_system_score_gemma":0.00002314093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001966417,"about_ca_topic_score_gemma":0.0002289745,"domain_scores_codex":[0.9980941,0.0001162224,0.0006630985,0.0002708875,0.0002906782,0.0005649485],"domain_scores_gemma":[0.998697,0.0002400141,0.0002478827,0.0002552111,0.0003303325,0.0002295551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000597256,0.000004129909,0.000004767216,0.00009958722,0.00003656128,0.00003911584,0.0001508146,0.002192014,0.3919825,8.484892e-8,0.00003818682,0.6053925],"study_design_scores_gemma":[0.0009509314,0.000148501,0.001091346,0.0004283927,0.0001231122,0.0003647873,0.0002782281,0.9050423,0.08991931,0.0001953051,0.001157005,0.0003007992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3112641,0.0001533093,0.6868013,0.0001323402,0.001087984,0.0003112788,0.000004559707,0.0002132586,0.00003190415],"genre_scores_gemma":[0.7926235,0.0001663741,0.2060668,0.00007522601,0.0009379091,2.864194e-8,0.00001903857,0.00009840351,0.0000126719],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9028503,"threshold_uncertainty_score":0.9999489,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W318364127","doi":"10.1016/j.isprsjprs.2015.04.010","title":"Scale parameter selection by spatial statistics for GeOBIA: Using mean-shift based multi-scale segmentation as an example","year":2015,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":160,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Segmentation; Panchromatic film; Computer science; Spatial analysis; Scale-space segmentation; Scale (ratio); Image segmentation; Pattern recognition (psychology); Artificial intelligence; Histogram; Segmentation-based object categorization; Data mining; Image resolution; Computer vision; Remote sensing; Image (mathematics); Cartography; Geology; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.04808426296852533,"gpt":0.2918447130145981,"spread":0.2437604500460728,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007013324,0.0002345429,0.0003272287,0.0002541646,0.0001521237,0.0001988011,0.00008048672,0.0001658561,0.000002749649],"category_scores_gemma":[0.0001777655,0.0002169599,0.00007979848,0.0002396527,0.00008198307,0.0003288212,0.00001232324,0.0002866732,0.000001114441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002218783,"about_ca_system_score_gemma":0.00007869051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002396252,"about_ca_topic_score_gemma":0.0009463197,"domain_scores_codex":[0.9984362,0.0001418489,0.0005629626,0.0002289848,0.0003136964,0.0003163748],"domain_scores_gemma":[0.9986217,0.0001877369,0.0003059737,0.0001859235,0.0004161031,0.000282589],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001667342,0.00005415845,0.0001220805,0.00008617765,0.00005666984,0.00001093492,0.001014315,0.01105173,0.4931232,1.910753e-7,0.0002583467,0.4940554],"study_design_scores_gemma":[0.001339132,0.0002270721,0.0001382167,0.00008457014,0.0001186762,0.0002052652,0.0004244659,0.781782,0.2148938,0.0002213552,0.0003550329,0.0002104304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.405516,0.00005347203,0.5937908,0.00001586259,0.0003936685,0.0001745681,0.00001079902,0.00003960467,0.00000523953],"genre_scores_gemma":[0.4944644,0.00001071786,0.5052758,0.00003748247,0.0001341181,3.973667e-8,0.00002840733,0.00004253786,0.0000064821],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7707302,"threshold_uncertainty_score":0.8847367,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3147179755","doi":"10.3390/rs13071349","title":"Performance Evaluation of Sentinel-2 and Landsat 8 OLI Data for Land Cover/Use Classification Using a Comparison between Machine Learning Algorithms","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":149,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Ministry of Forests","funders":"","keywords":"Land cover; Support vector machine; Computer science; Confusion matrix; Algorithm; Mahalanobis distance; Remote sensing; Artificial intelligence; Artificial neural network; Data mining; Machine learning; Land use; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.1792185572378691,"gpt":0.3415312931255921,"spread":0.162312735887723,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092994,0.0001745838,0.0003237774,0.0001130008,0.000139635,0.0001070774,0.00007657531,0.0001113832,0.000001517118],"category_scores_gemma":[0.0005743673,0.0001974281,0.00003384986,0.0002470354,0.00004363678,0.0003681118,0.00006858166,0.0001996771,0.000002020811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001243031,"about_ca_system_score_gemma":0.00006456414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004045438,"about_ca_topic_score_gemma":0.0000114692,"domain_scores_codex":[0.9984788,0.0001496874,0.0004381902,0.0003585391,0.000355283,0.0002194704],"domain_scores_gemma":[0.9985203,0.0002510158,0.0001888132,0.0005408897,0.0004427982,0.00005624746],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000183962,0.00001154081,0.02077785,0.0003234157,0.0001130669,0.000001844499,0.0002820268,0.1175393,0.2300069,8.633415e-7,0.0000508515,0.630874],"study_design_scores_gemma":[0.0006552746,0.000008791912,0.02328632,0.0002087946,0.0002584316,0.00003183555,0.00004887603,0.9620882,0.01266929,0.000008568284,0.0005476273,0.0001879434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8016872,0.0003144526,0.1974046,0.00004468357,0.000122387,0.0002110563,0.00002650618,0.00009307748,0.00009599283],"genre_scores_gemma":[0.9278098,0.0001215477,0.07085905,0.000005351916,0.0001551676,2.412743e-8,0.0009755485,0.0000502388,0.00002329465],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8445489,"threshold_uncertainty_score":0.8050883,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2605486153","doi":"10.1109/tgrs.2017.2689018","title":"Superpixel-Based Multiple Local CNN for Panchromatic and Multispectral Image Classification","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":135,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Panchromatic film; Multispectral image; Artificial intelligence; Computer science; Computer vision; Contextual image classification; Multispectral pattern recognition; Pattern recognition (psychology); Remote sensing; Image (mathematics); Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.02544551525432839,"gpt":0.2574967378916121,"spread":0.2320512226372837,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002016961,0.0001940317,0.0001810869,0.000137549,0.001051571,0.0003246754,0.0001043091,0.0001058895,0.000001035026],"category_scores_gemma":[0.00004808937,0.0001908439,0.00005754754,0.00008455724,0.0006124653,0.0003981579,0.000001284913,0.0001731175,0.000007769732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008134371,"about_ca_system_score_gemma":0.0000307398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001118696,"about_ca_topic_score_gemma":0.0001618734,"domain_scores_codex":[0.9989195,0.00002310952,0.0002213306,0.0003739202,0.0001544901,0.0003075834],"domain_scores_gemma":[0.999123,0.0001632326,0.0000655043,0.0004561679,0.00007475433,0.0001172952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000155834,0.00001278938,0.000003495877,0.00007199765,0.000007425358,0.000003097468,0.000162446,0.001506897,0.2565521,0.000002229914,0.00001108505,0.7416508],"study_design_scores_gemma":[0.0005343731,0.0000424115,0.005422247,0.0001164623,0.00002786816,0.0000288767,0.000132725,0.9142014,0.07915184,0.00005641438,0.00007823945,0.0002071508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2147473,0.00001623055,0.7839747,0.0003124098,0.0004237305,0.000275944,0.000009336248,0.0001509606,0.00008939033],"genre_scores_gemma":[0.8249899,0.00003788215,0.1748244,0.00003294271,0.00002982878,4.420657e-7,0.000002144563,0.00002701327,0.00005542211],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9126945,"threshold_uncertainty_score":0.8087938,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4210348940","doi":"10.3390/electronics11030431","title":"Change Detection in Remote Sensing Image Data Comparing Algebraic and Machine Learning Methods","year":2022,"lang":"en","type":"article","venue":"Electronics","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":131,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Change detection; Computer science; Pixel; Artificial intelligence; Remote sensing; Decision tree; Tree (set theory); Computation; Computer vision; Algorithm; Mathematics; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.05910530897374042,"gpt":0.3009998463596283,"spread":0.2418945373858879,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00100394,0.0001402458,0.0001815577,0.0001877656,0.0001875747,0.00005656649,0.0001511608,0.00003902628,0.000005545867],"category_scores_gemma":[0.0001186195,0.0001817466,0.00001648544,0.0004431088,0.00002029857,0.0002364985,0.0002224927,0.000885256,0.000002915792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000395257,"about_ca_system_score_gemma":0.00001595555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009548767,"about_ca_topic_score_gemma":0.0004706344,"domain_scores_codex":[0.9987416,0.0002696716,0.0002046585,0.0003062899,0.0001334443,0.0003442948],"domain_scores_gemma":[0.9994063,0.00009044106,0.00005214893,0.0004005738,0.00001461309,0.00003593954],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008651907,0.000003476079,0.00004601146,0.0000252363,0.0000120319,0.000007615242,0.0003089109,0.002607761,0.2169577,0.000006893476,0.00001024194,0.7800054],"study_design_scores_gemma":[0.0002153322,0.00003040271,0.0007800005,0.000009806194,0.00001358283,0.0001005628,0.00005564456,0.9667108,0.0137786,0.0001671745,0.0179689,0.0001691864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3395121,0.01389389,0.6445082,0.0002044074,0.0003392571,0.0003420865,0.000004122126,0.0006315146,0.0005643692],"genre_scores_gemma":[0.9338426,0.000628381,0.06524222,0.00002741757,0.00006654362,9.115835e-7,0.00009421882,0.0000678753,0.00002983506],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.964103,"threshold_uncertainty_score":0.741141,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1696305814","doi":"10.1155/2015/538063","title":"Urban Land Use and Land Cover Classification Using Remotely Sensed SAR Data through Deep Belief Networks","year":2015,"lang":"en","type":"article","venue":"Journal of Sensors","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":128,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Deep belief network; Artificial intelligence; Computer science; Deep learning; Land cover; Support vector machine; Artificial neural network; Synthetic aperture radar; Field (mathematics); Pattern recognition (psychology); Machine learning; Remote sensing; Land use; Geography; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.1331286178345331,"gpt":0.2854826539302463,"spread":0.1523540360957132,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004163505,0.0001744999,0.0002786454,0.0001041103,0.00005807731,0.0001906224,0.0001596383,0.0001475018,0.000002505352],"category_scores_gemma":[0.0003684124,0.0001588313,0.00003651587,0.0001849581,0.000068199,0.0010714,0.00004062646,0.0003419442,0.000007697321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001822633,"about_ca_system_score_gemma":0.00004755456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003641239,"about_ca_topic_score_gemma":0.000006885913,"domain_scores_codex":[0.9987107,0.00009667707,0.0004915789,0.0001834325,0.0003033501,0.0002142404],"domain_scores_gemma":[0.9986339,0.0001495115,0.0002863778,0.0004907419,0.00028174,0.0001577689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001822103,0.00004312601,0.02248882,0.0000597352,0.0002018872,0.000182082,0.001320975,0.9246437,0.02548462,0.00001350247,0.02066221,0.004717153],"study_design_scores_gemma":[0.0007139122,0.0000340634,0.01403852,0.00008493193,0.0000909609,0.0006065361,0.00009454974,0.9710907,0.0002994287,0.00003021547,0.01273746,0.000178766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9424214,0.0008339651,0.05558461,0.0001720096,0.0005824555,0.0001079674,0.000009495251,0.00005935449,0.0002287992],"genre_scores_gemma":[0.9679452,0.0005381824,0.03071475,0.00005297626,0.0006095467,2.18941e-8,0.00002612469,0.00005697861,0.00005622669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04644699,"threshold_uncertainty_score":0.6476951,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1992024126","doi":"10.1007/s10661-015-4489-3","title":"Land cover mapping based on random forest classification of multitemporal spectral and thermal images","year":2015,"lang":"en","type":"article","venue":"Environmental Monitoring and Assessment","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":120,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"U.S. Geological Survey","keywords":"Random forest; Land cover; Remote sensing; Thematic map; Environmental science; Change detection; Thematic Mapper; Contextual image classification; Land use; Spectral bands; Satellite imagery; Computer science; Geography; Cartography; Artificial intelligence; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.02433679457589763,"gpt":0.2469573695910722,"spread":0.2226205750151746,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001501857,0.0001281906,0.0001321645,0.00005488398,0.00004019404,0.000037321,0.0000357678,0.0000430142,0.000002501814],"category_scores_gemma":[0.000006445845,0.0001220715,0.00002121135,0.00002838345,0.00006737629,0.0001119588,0.00001523231,0.0001143831,0.000002918833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001304444,"about_ca_system_score_gemma":0.000007813642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008683599,"about_ca_topic_score_gemma":1.911171e-7,"domain_scores_codex":[0.9993358,0.00003268109,0.0001640795,0.0001592218,0.0001861596,0.0001220545],"domain_scores_gemma":[0.9996802,0.00005073409,0.00005014473,0.0001376055,0.000003987539,0.00007738331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000352098,0.00006506131,0.6975472,0.00003723828,0.00002028889,0.000003908173,0.0001490491,0.02787662,0.2647353,0.000006970169,0.00002617085,0.009496983],"study_design_scores_gemma":[0.001152983,0.00005057465,0.7703892,0.00003985828,0.000009838414,0.000001576316,0.0001472003,0.2141529,0.01375264,0.000007409367,0.0001906856,0.0001051301],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9940057,0.0001149712,0.004404785,0.00006297341,0.0002066089,0.0001474966,0.000008065931,0.00004706536,0.00100237],"genre_scores_gemma":[0.9932174,0.00004021778,0.00655825,0.000002477778,0.0001007838,0.000006157401,0.00001718583,0.000021407,0.00003608247],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2509826,"threshold_uncertainty_score":0.4977931,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2745252301","doi":"10.1109/igarss.2017.8127330","title":"Deep residual networks for hyperspectral image classification","year":2017,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":117,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Residual; Computer science; Artificial intelligence; Convolutional neural network; Normalization (sociology); Hyperspectral imaging; Deep learning; Pattern recognition (psychology); Contextual image classification; Embedding; Regularization (linguistics); Deep neural networks; Dimensionality reduction; Feature learning; Image (mathematics); Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.03160568644587343,"gpt":0.2710259716334991,"spread":0.2394202851876257,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001446548,0.000121302,0.0001123941,0.00004130632,0.0002628389,0.0002941628,0.0002372444,0.00009762079,0.00001813024],"category_scores_gemma":[0.0001511348,0.0001225551,0.00005250452,0.00002804049,0.00007906809,0.0003368302,0.00001533454,0.0001057754,0.00005282934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008510167,"about_ca_system_score_gemma":0.000009057519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006946773,"about_ca_topic_score_gemma":0.00002678111,"domain_scores_codex":[0.9992932,0.000009216824,0.0001691793,0.0001947277,0.00008723343,0.0002464542],"domain_scores_gemma":[0.9990382,0.00005054034,0.00005839415,0.0007096826,0.00008558954,0.00005756283],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007739792,0.00007297178,0.0007119489,0.0001297557,0.0001398341,0.000009340195,0.0003715443,0.03286884,0.690303,0.02184455,0.07088438,0.1825864],"study_design_scores_gemma":[0.0002597975,0.00001251711,0.03084768,0.000007022103,0.00001603516,0.000004074318,0.00006175156,0.9576672,0.008951559,0.0003509359,0.001660458,0.0001609433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.016283,0.00006343481,0.9333361,0.001071867,0.0005860325,0.000323153,0.000002102514,0.0005295179,0.04780475],"genre_scores_gemma":[0.9129457,0.0000351686,0.0855744,0.00002340663,0.000461394,0.00001322873,0.00002677853,0.00004934335,0.0008705927],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9247984,"threshold_uncertainty_score":0.4997651,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2608922915","doi":"10.3390/rs9060586","title":"Exploiting Deep Matching and SAR Data for the Geo-Localization Accuracy Improvement of Optical Satellite Images","year":2017,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":116,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Remote sensing; Artificial intelligence; Satellite; Synthetic aperture radar; Computer vision; Matching (statistics); Geology; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.04560480712519072,"gpt":0.293730511693214,"spread":0.2481257045680232,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005326582,0.0001576922,0.0001895238,0.0000444014,0.0004402471,0.0003153553,0.0002709377,0.00006698404,4.87686e-7],"category_scores_gemma":[0.001009602,0.0001354493,0.00003519217,0.00003961577,0.0001205186,0.0004633197,0.0001916518,0.00012788,0.000001788144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004849417,"about_ca_system_score_gemma":0.0000126913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006945063,"about_ca_topic_score_gemma":0.00001039638,"domain_scores_codex":[0.9989619,0.0000168263,0.0003364757,0.000278809,0.000161218,0.0002447877],"domain_scores_gemma":[0.9979194,0.0004692244,0.000188614,0.001270512,0.0001060944,0.00004609607],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005599775,0.000001703293,0.000003752909,0.0001103891,0.00002441328,0.000001636759,0.0001692581,0.003641254,0.2657468,0.00002011424,0.00001384055,0.7302613],"study_design_scores_gemma":[0.0002062125,0.00000939832,0.0005184858,0.0001685329,0.00005856163,0.00001779057,0.0003262433,0.9446098,0.05245411,0.000530687,0.0009592784,0.0001409137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09944309,0.0004440079,0.8986686,0.0003721159,0.000251231,0.0003466817,0.000008021671,0.00009903244,0.0003672205],"genre_scores_gemma":[0.8674587,0.0006112246,0.131624,0.00003001934,0.0001779283,1.318847e-8,0.00003483591,0.00005215227,0.00001116091],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9409685,"threshold_uncertainty_score":0.5523461,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2783096881","doi":"10.1080/10095020.2017.1399674","title":"Object-based classification of hyperspectral data using Random Forest algorithm","year":2018,"lang":"en","type":"article","venue":"Geo-spatial Information Science","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":108,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"University of Zanjan; University of Houston; National Science Foundation","keywords":"Hyperspectral imaging; Random forest; Computer science; Pixel; Support vector machine; Artificial intelligence; Pattern recognition (psychology); Segmentation; Classifier (UML); Land cover; Imaging spectrometer; Remote sensing; Spectrometer; Land use; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.04982034773340255,"gpt":0.2897445955874565,"spread":0.2399242478540539,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009162584,0.0001309771,0.0001505144,0.0003994511,0.0002149578,0.000184864,0.0006391506,0.00006676493,0.00002123208],"category_scores_gemma":[0.0004125408,0.0001308351,0.00002801035,0.0009864422,0.0006462446,0.00379855,0.00006964333,0.0001029779,0.0001120547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001751223,"about_ca_system_score_gemma":0.0002337528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002124138,"about_ca_topic_score_gemma":0.00004978262,"domain_scores_codex":[0.9983912,0.00002173847,0.000517792,0.0001904649,0.0005953449,0.0002834598],"domain_scores_gemma":[0.9981887,0.00005508482,0.000213566,0.0008514061,0.0006089511,0.00008233101],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007348542,0.00004340014,0.0007258176,0.0001071261,0.00001992117,5.664118e-7,0.002129326,0.08872742,0.4261339,0.0003339114,0.0007461487,0.480959],"study_design_scores_gemma":[0.0004857483,0.00002687673,0.01495,0.00002374343,0.00001029782,0.000004677201,0.0001287628,0.9428142,0.04063924,0.00002642978,0.0007547786,0.0001352261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1031016,0.000007541986,0.8933651,0.00005239777,0.0005856434,0.0002379944,0.00003993302,0.000142357,0.002467346],"genre_scores_gemma":[0.9268792,0.000003853758,0.07283072,0.00003676662,0.0001194643,0.000001986197,0.000115676,0.000008590145,0.000003710712],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8540868,"threshold_uncertainty_score":0.53353,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2160934142","doi":"10.3390/rs4082256","title":"Object-Based Classification of Urban Areas Using VHR Imagery and Height Points Ancillary Data","year":2012,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":108,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Impervious surface; Land cover; Computer science; Cohen's kappa; Thematic map; Remote sensing; Support vector machine; Pattern recognition (psychology); Object based; Classifier (UML); Contextual image classification; Artificial intelligence; Object (grammar); Data mining; Cartography; Land use; Image (mathematics); Geography; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.05804470664752823,"gpt":0.2672405055847475,"spread":0.2091957989372193,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000569667,0.0002419543,0.0002979717,0.0002089657,0.00009528993,0.00005650722,0.0001343684,0.0001469429,0.000002712507],"category_scores_gemma":[0.000272807,0.0002661588,0.00004502769,0.000317385,0.0001254373,0.0005476341,0.00006941647,0.000202101,0.00001133545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001720453,"about_ca_system_score_gemma":0.0000551713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003570185,"about_ca_topic_score_gemma":0.000004137466,"domain_scores_codex":[0.9984514,0.0001043525,0.0004346851,0.0003293782,0.0002734836,0.0004066621],"domain_scores_gemma":[0.9982935,0.0001542684,0.0001630681,0.001116839,0.000126894,0.0001454452],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001545033,0.00001774869,0.0007763322,0.0002239816,0.00004113366,0.000007875705,0.0002538666,0.0009648177,0.906777,0.000009359883,0.0006878528,0.09022456],"study_design_scores_gemma":[0.0002131257,0.000005537537,0.01113687,0.0002138018,0.00006508164,0.00008980676,0.00007277739,0.9443871,0.04272278,0.00002307346,0.0008112041,0.0002587784],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7825773,0.001316983,0.2137129,0.00008950484,0.0004792075,0.0002061181,0.00001679274,0.0002715059,0.001329719],"genre_scores_gemma":[0.8880371,0.00004058749,0.1114394,0.00003647908,0.0002565705,3.815892e-9,0.000103737,0.00007597698,0.00001015264],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9434223,"threshold_uncertainty_score":0.9999791,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1510047790","doi":"10.1109/tip.2015.2456505","title":"Incorporating Adaptive Local Information Into Fuzzy Clustering for Image Segmentation","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":107,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Artificial intelligence; Image segmentation; Pattern recognition (psychology); Pixel; Cluster analysis; Range segmentation; Computer science; Fuzzy clustering; Smoothing; Scale-space segmentation; Segmentation-based object categorization; Computer vision; Fuzzy logic; Region growing; Segmentation; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02449092549421801,"gpt":0.2607718656546555,"spread":0.2362809401604375,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000305447,0.0002537,0.0001906571,0.000295877,0.0002843191,0.0003571452,0.0001146279,0.0001116875,0.000002694309],"category_scores_gemma":[0.00002686221,0.0002857471,0.00006742634,0.0003752444,0.0001115237,0.003587105,0.000001798046,0.0002581044,0.00006841164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005778827,"about_ca_system_score_gemma":0.0001125917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002256687,"about_ca_topic_score_gemma":0.00002930457,"domain_scores_codex":[0.9987113,0.00003250577,0.0004881796,0.0002150317,0.000282331,0.0002706671],"domain_scores_gemma":[0.9990165,0.00005530171,0.0001491298,0.0001886413,0.0004587626,0.0001316754],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009695893,0.00003060501,6.045145e-7,0.0003315037,0.0000220342,0.000001901053,0.002946212,0.296516,0.09212404,0.000004333468,0.0001785482,0.6077473],"study_design_scores_gemma":[0.0007203419,0.00007477065,0.000006144825,0.0001238331,0.00003606594,0.00001685361,0.002459448,0.8230785,0.1727816,0.0003690381,0.00007274628,0.0002607213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002884047,0.00003184341,0.9938971,0.0001070411,0.0004977557,0.0005147914,0.00001499661,0.0006649886,0.001387435],"genre_scores_gemma":[0.674635,0.00000295705,0.3251062,0.00004430322,0.0000552698,0.00006485594,0.00002437547,0.0000465282,0.00002056145],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.671751,"threshold_uncertainty_score":0.9999595,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1996828724","doi":"10.1016/j.rse.2012.09.022","title":"Landcover classification of the Lower Nhecolândia subregion of the Brazilian Pantanal Wetlands using ALOS/PALSAR, RADARSAT-2 and ENVISAT/ASAR imagery","year":2012,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":106,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Victoria","funders":"","keywords":"Wetland; Habitat; Vegetation (pathology); Environmental science; Geography; Grassland; Biodiversity; Physical geography; Remote sensing; Ecology; Hydrology (agriculture); Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.01872223099757121,"gpt":0.2107248522293865,"spread":0.1920026212318153,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003431026,0.0002019177,0.0002852313,0.00006946193,0.00007937828,0.00001147802,0.0001296618,0.0001222991,0.000006343266],"category_scores_gemma":[0.00005200733,0.0001478055,0.0001385061,0.0001400026,0.0003599712,0.0001339711,0.00007684568,0.0001752688,0.000002725562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001735688,"about_ca_system_score_gemma":0.0000197742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000761278,"about_ca_topic_score_gemma":0.000007897446,"domain_scores_codex":[0.9985603,0.0001277642,0.0004822051,0.0001871615,0.0003856426,0.0002569202],"domain_scores_gemma":[0.998758,0.00009785522,0.000336028,0.0007286434,0.00002102671,0.00005850434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0000202312,0.00004922099,0.008300783,0.0001183736,0.00006184392,6.968174e-7,0.0004264916,0.004212423,0.9579207,0.00001153719,0.00009909696,0.02877857],"study_design_scores_gemma":[0.0003843232,0.0000234841,0.5818884,0.0003014781,0.0001641322,0.0000599265,0.0001163962,0.2195717,0.1956532,0.00006253918,0.001543655,0.000230753],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.985027,0.0002963064,0.01348364,0.0001527411,0.000386293,0.0002624862,0.000009907412,0.00001808741,0.0003635154],"genre_scores_gemma":[0.9912201,0.000179333,0.008407504,0.0000175232,0.00007804229,2.46145e-8,0.000004946594,0.00004335371,0.00004919707],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7622675,"threshold_uncertainty_score":0.6027336,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2956786564","doi":"10.1007/s00521-019-04349-9","title":"A deep learning framework for land-use/land-cover mapping and analysis using multispectral satellite imagery","year":2019,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":101,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Winnipeg","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multispectral image; Computer science; Land cover; Deep learning; Artificial intelligence; Satellite imagery; Context (archaeology); Satellite; Remote sensing; Pixel; Pattern recognition (psychology); Field (mathematics); Contextual image classification; Convolutional neural network; Scale (ratio); Land use; Image (mathematics); Cartography; Geography; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02146645467919889,"gpt":0.2638803138753766,"spread":0.2424138591961777,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001060284,0.0001445017,0.0002125547,0.0001450986,0.0002113868,0.0002088027,0.00005422121,0.00007736863,0.000001919822],"category_scores_gemma":[0.00003592437,0.0001528187,0.00006672793,0.0003944746,0.00003287592,0.0001175878,0.00002908109,0.0002290418,0.00000605186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000296491,"about_ca_system_score_gemma":0.000003640882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000156418,"about_ca_topic_score_gemma":0.000001959677,"domain_scores_codex":[0.9991665,0.00002429011,0.0002122305,0.0002975802,0.00007284215,0.0002264816],"domain_scores_gemma":[0.9992056,0.0004314268,0.0000712356,0.0001858658,0.00004558155,0.00006021414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007826923,0.00001409713,0.1973219,0.0001719503,0.0001832714,8.112834e-7,0.0005366124,0.6641498,0.02705503,0.0002288449,0.000005920115,0.1103239],"study_design_scores_gemma":[0.0001313474,0.000006651364,0.07793391,0.00002622636,0.00008786942,0.000008800399,0.00003282066,0.9205688,0.0001697548,0.00009273289,0.0007816321,0.0001594688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4948622,0.0002256226,0.5044544,0.00002096014,0.00002980604,0.0002097454,0.000002133141,0.0001569052,0.00003815895],"genre_scores_gemma":[0.9082605,0.00005480805,0.09144955,0.00002883337,0.0001105041,0.00000548765,0.00003456766,0.00002878489,0.00002693069],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4133983,"threshold_uncertainty_score":0.6231765,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2146449740","doi":"10.3390/s8021321","title":"The Successive Projection Algorithm (SPA), an Algorithm with a Spatial Constraint for the Automatic Search of Endmembers in Hyperspectral Data","year":2008,"lang":"en","type":"article","venue":"Sensors","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":99,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta; University of Lethbridge","funders":"Networks of Centres of Excellence of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Endmember; Hyperspectral imaging; Pixel; Algorithm; Adjacency list; Computer science; Spatial analysis; Projection (relational algebra); Pattern recognition (psychology); Mathematics; Artificial intelligence; Remote sensing; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.04154505271190751,"gpt":0.2787351103496917,"spread":0.2371900576377842,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003730304,0.000141998,0.0001528765,0.00008249089,0.0001786715,0.00004021306,0.0002861656,0.00005826448,0.000002827496],"category_scores_gemma":[0.00009961434,0.00008857087,0.00002916559,0.0002451739,0.0003800923,0.0001500127,0.00002327777,0.0002105251,0.000002152785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000109535,"about_ca_system_score_gemma":0.0001084748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007389762,"about_ca_topic_score_gemma":0.0005209293,"domain_scores_codex":[0.9988936,0.0000833891,0.0002485192,0.0002360932,0.000257915,0.0002804749],"domain_scores_gemma":[0.9987264,0.0004327573,0.00006392848,0.0006306987,0.0001063297,0.00003992618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005125641,0.00008499454,0.0001917905,0.00007755064,0.0001727671,0.00004611578,0.005774752,0.05140543,0.0053973,0.00006587406,0.0001863759,0.9365458],"study_design_scores_gemma":[0.0004298191,0.00008078659,0.003524491,0.00002633827,0.00002215089,0.0001197336,0.003388041,0.9868229,0.005365814,0.000009910248,0.00009497417,0.0001150495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6294028,0.0001240296,0.3667209,0.0005574138,0.0003518529,0.001986193,0.0001516071,0.0002879967,0.0004171834],"genre_scores_gemma":[0.9199922,0.000067111,0.07963277,0.00000588167,0.0001347637,0.00001571297,0.00005957896,0.00004442973,0.00004754813],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9364308,"threshold_uncertainty_score":0.3611816,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2156999391","doi":"10.1109/tip.2010.2044965","title":"A Label Field Fusion Bayesian Model and Its Penalized Maximum Rand Estimator for Image Segmentation","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":97,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"","keywords":"Artificial intelligence; Markov random field; Segmentation; Image segmentation; Scale-space segmentation; Computer science; Pattern recognition (psychology); Segmentation-based object categorization","retraction":null,"screen_n_in":null,"score":{"opus":0.01647114196360106,"gpt":0.2743005678911878,"spread":0.2578294259275867,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001653829,0.0002368792,0.0001984528,0.0001789479,0.0003723776,0.0002712183,0.00008956315,0.0001425388,0.00002560609],"category_scores_gemma":[0.00003326914,0.0002493667,0.0000559373,0.000168179,0.00004822516,0.000842885,0.000001177361,0.0003467967,0.00001050474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004460065,"about_ca_system_score_gemma":0.00004759354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003116415,"about_ca_topic_score_gemma":0.00001621227,"domain_scores_codex":[0.9989378,0.00001527517,0.0002959288,0.0003157871,0.0001690393,0.0002661998],"domain_scores_gemma":[0.9993716,0.0001000231,0.00006700662,0.0001831119,0.0001708126,0.0001073767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007905075,0.00004426345,3.707209e-7,0.0003244011,0.00001124112,0.000001958342,0.0002886258,0.00484309,0.8731849,0.00000273583,0.00008941028,0.1211299],"study_design_scores_gemma":[0.0009818114,0.00002174793,0.000003811501,0.00005834304,0.00004576891,0.00001949509,0.00003357499,0.6192526,0.3791624,0.0002389064,0.00001619755,0.0001652964],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05317009,0.00005848148,0.9448324,0.0003570268,0.0002856794,0.0005303092,0.00002387338,0.0003865825,0.0003555778],"genre_scores_gemma":[0.7001445,0.00002794559,0.2994656,0.00005511169,0.00003624415,0.00005945798,0.000007482039,0.00006562969,0.0001380395],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6469744,"threshold_uncertainty_score":0.9999959,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2024448972","doi":"10.3390/rs6087339","title":"Urban Built-Up Area Extraction from Landsat TM/ETM+ Images Using Spectral Information and Multivariate Texture","year":2014,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":96,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"National Science Foundation","keywords":"Remote sensing; Multispectral image; Multivariate statistics; Computer science; Mahalanobis distance; Hyperspectral imaging; Variogram; Pattern recognition (psychology); Artificial intelligence; Confusion matrix; Geography; Kriging","retraction":null,"screen_n_in":null,"score":{"opus":0.01584788245708334,"gpt":0.2357304390796938,"spread":0.2198825566226104,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001966839,0.0002544008,0.0002370917,0.0001752103,0.000149015,0.0002686198,0.00005732576,0.0001845615,0.000004421268],"category_scores_gemma":[0.0002048941,0.0002664145,0.00005229413,0.0001632071,0.00004524031,0.0008578503,0.00002611581,0.000317725,0.0000298582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001741714,"about_ca_system_score_gemma":0.00001474439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005245727,"about_ca_topic_score_gemma":0.00003064809,"domain_scores_codex":[0.9988706,0.00005769399,0.0003502374,0.0002264544,0.0001971984,0.0002978363],"domain_scores_gemma":[0.9992056,0.0001245075,0.0001332213,0.0003482746,0.00009133147,0.00009706252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001798624,0.000002662845,0.00007673567,0.00004378954,0.0000307869,0.000004969822,0.0007383245,0.007082037,0.7147956,0.000004845459,0.0004339118,0.2767684],"study_design_scores_gemma":[0.0004042487,0.000008642511,0.007832875,0.0001478149,0.00004617764,0.0001006631,0.00009164922,0.9482064,0.04024419,0.0002373913,0.002393983,0.0002859143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5158028,0.00006592499,0.4820324,0.00007508669,0.0005528925,0.0001102572,0.000005485741,0.0003256699,0.001029541],"genre_scores_gemma":[0.9275703,0.00003627129,0.07179573,0.00004621191,0.0004022294,8.134084e-9,0.0000688337,0.00004678197,0.00003359951],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9411244,"threshold_uncertainty_score":0.9999788,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4382137697","doi":"10.3390/rs15133265","title":"Small Object Detection Based on Deep Learning for Remote Sensing: A Comprehensive Review","year":2023,"lang":"en","type":"review","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":95,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Computer science; Object detection; Remote sensing; Artificial intelligence; Object (grammar); Remote sensing application; Computer vision; Orientation (vector space); Pattern recognition (psychology); Geography; Hyperspectral imaging","retraction":null,"screen_n_in":null,"score":{"opus":0.1007334781840737,"gpt":0.3144095739837815,"spread":0.2136760957997078,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0009114604,0.001403758,0.003211553,0.0009706641,0.0004237901,0.000219767,0.0002606666,0.0008690084,0.000002294375],"category_scores_gemma":[0.001826373,0.001470675,0.001406449,0.001642521,0.00009343617,0.00008435866,0.00006818844,0.001850726,0.000389883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001278441,"about_ca_system_score_gemma":0.0001805015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004881572,"about_ca_topic_score_gemma":0.00004812047,"domain_scores_codex":[0.9946321,0.0007486304,0.001673574,0.001316504,0.0005534959,0.001075725],"domain_scores_gemma":[0.9948475,0.002175024,0.0008075148,0.001403446,0.0005197265,0.0002467961],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009296998,0.000002606043,1.187516e-9,0.08092796,0.000161023,0.00007619508,0.0000212594,0.007907913,0.0002428558,2.242385e-7,0.0001392155,0.9105114],"study_design_scores_gemma":[0.0001633184,0.00004793506,1.504883e-7,0.08575349,0.0007940457,0.0001820111,0.000008268308,0.5101008,0.00006442297,0.00001835356,0.4021835,0.0006837313],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000003356452,0.6005826,0.3934504,0.00003925927,0.001186375,0.002312824,0.000006044367,0.001915164,0.0005039942],"genre_scores_gemma":[0.0000158105,0.9295321,0.0680318,0.0001759094,0.0007336407,1.689975e-7,0.0003598456,0.0009212302,0.0002295196],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9098277,"threshold_uncertainty_score":0.9998713,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2055149942","doi":"10.1109/jstars.2014.2344017","title":"Unsupervised Change Detection in Multitemporal SAR Images Over Large Urban Areas","year":2014,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":94,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"Swedish National Space Agency","keywords":"Thresholding; Histogram; Artificial intelligence; Synthetic aperture radar; Balanced histogram thresholding; Change detection; Pixel; Pattern recognition (psychology); Image histogram; Computer science; Histogram matching; Computer vision; Mathematics; Image segmentation; Image (mathematics); Image texture","retraction":null,"screen_n_in":null,"score":{"opus":0.0245551208495337,"gpt":0.2240620848226549,"spread":0.1995069639731211,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000424411,0.0001632174,0.0002672366,0.0004026977,0.00007237975,0.00006546219,0.00005816721,0.0001480304,0.000001327769],"category_scores_gemma":[0.0001128663,0.0001671533,0.00003474214,0.0007525301,0.00002959496,0.0002014726,0.000009106943,0.0004666622,8.776801e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001356216,"about_ca_system_score_gemma":0.00002684323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008577799,"about_ca_topic_score_gemma":0.0007684091,"domain_scores_codex":[0.9988124,0.00006135966,0.0005215458,0.0001492522,0.0002051696,0.0002503033],"domain_scores_gemma":[0.999355,0.00007501312,0.0001445356,0.0001514719,0.0002097145,0.0000643263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000404926,0.00004138481,0.004993424,0.0001062862,0.00003290753,0.00002195586,0.001075396,0.003749664,0.4613192,0.00005412628,0.00006411802,0.5285011],"study_design_scores_gemma":[0.001011579,0.00002543768,0.3505189,0.0001753893,0.00001338864,0.00003391492,0.00005784875,0.6295382,0.01607475,0.0001546089,0.002213386,0.0001826058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9484191,0.0001165565,0.05045815,0.0001399497,0.0003405384,0.0001811555,0.000001848903,0.00005321317,0.0002895195],"genre_scores_gemma":[0.9658111,0.0001544207,0.03346907,0.00006823574,0.0004432879,6.908272e-8,0.000005300672,0.0000307107,0.00001778825],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6257885,"threshold_uncertainty_score":0.6816314,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3037656662","doi":"10.3390/rs12122012","title":"Geographic Object-Based Image Analysis: A Primer and Future Directions","year":2020,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":94,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; University of Calgary","keywords":"Workflow; Data science; Pixel; Computer science; Cartography; Remote sensing; Geography; Artificial intelligence; Database","retraction":null,"screen_n_in":null,"score":{"opus":0.01004682603476901,"gpt":0.2157431422271481,"spread":0.2056963161923791,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008915037,0.0001958392,0.0002597074,0.0002845346,0.0001227233,0.0001129077,0.00004696095,0.0001012858,0.000005708364],"category_scores_gemma":[0.00005103709,0.0002109006,0.0001406468,0.001394461,0.00006410043,0.0001087442,0.00001421652,0.0002423777,0.00001975836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004062381,"about_ca_system_score_gemma":0.00001720857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004073841,"about_ca_topic_score_gemma":0.00002090865,"domain_scores_codex":[0.9990193,0.00005387936,0.0002202837,0.0003123287,0.0001581718,0.0002360237],"domain_scores_gemma":[0.999385,0.00004859475,0.00004722523,0.0002870984,0.00008198055,0.000150104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002004138,0.00000938416,0.000151037,0.0001538197,0.0007316591,0.00006589392,0.0009855708,0.007668432,0.5232417,0.000006711813,0.000325611,0.4666401],"study_design_scores_gemma":[0.0001705716,0.00001019045,0.004534572,0.0000199932,0.0003922877,0.00001322171,0.00008806273,0.9732643,0.009466876,0.00001186321,0.0118007,0.0002273825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5028659,0.002204749,0.483082,0.004757066,0.000465491,0.0003484622,0.000008296147,0.002193345,0.004074706],"genre_scores_gemma":[0.8878598,0.0002315562,0.1110985,0.0002613795,0.0004475437,1.454651e-8,0.00002491248,0.00006337574,0.00001286462],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9655958,"threshold_uncertainty_score":0.8600277,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2047152380","doi":"10.1080/01431160500406888","title":"Assessment of land‐cover changes related to shrimp aquaculture using remote sensing data: a case study in the Giao Thuy District, Vietnam","year":2006,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":91,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Mangrove; Deforestation (computer science); Reforestation; Aquaculture; Shrimp; Land cover; Feature (linguistics); Shrimp farming; Wetland; Environmental science; Remote sensing; Land use; Geography; Environmental resource management; Ecology; Agroforestry; Computer science; Fishery; Biology","retraction":null,"screen_n_in":null,"score":{"opus":0.03734489949515291,"gpt":0.3302010912201152,"spread":0.2928561917249623,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001237332,0.0002566389,0.0003932698,0.0004913831,0.00007739244,0.0001981632,0.0004342498,0.0001065148,0.000002172608],"category_scores_gemma":[0.000215097,0.0002045504,0.00009463893,0.000545894,0.00004210533,0.0003332714,0.0001145483,0.0005790187,0.00000203063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000487825,"about_ca_system_score_gemma":0.00007846392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00145758,"about_ca_topic_score_gemma":0.001124036,"domain_scores_codex":[0.9973642,0.0002338677,0.0009548745,0.0002676842,0.0009238513,0.0002554783],"domain_scores_gemma":[0.9981315,0.0002013788,0.0005102854,0.0005081638,0.000588075,0.00006065262],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001060126,0.0001873609,0.001488693,0.00007178355,0.0006705431,0.02673551,0.004884198,0.2875176,0.15463,0.0000102177,0.0008464094,0.5228516],"study_design_scores_gemma":[0.000903064,0.00007164162,0.007073566,0.0004933718,0.0001134492,0.01977397,0.001688089,0.9680937,0.0007720395,0.00008618425,0.000703457,0.0002274902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8548006,0.0001064019,0.1427495,0.0006456297,0.0008953673,0.0002856856,0.00001584173,0.00003268264,0.0004682174],"genre_scores_gemma":[0.9019068,0.00002240907,0.09757297,0.00006057477,0.0003539077,5.348692e-9,0.00002258737,0.00004189329,0.00001877369],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6805761,"threshold_uncertainty_score":0.8341324,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2028151808","doi":"10.1016/j.isprsjprs.2014.04.010","title":"SAR change detection based on intensity and texture changes","year":2014,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":91,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China; Defence Research and Development Canada","keywords":"Synthetic aperture radar; Artificial intelligence; Pattern recognition (psychology); Change detection; Computer science; Principal component analysis; Interferometric synthetic aperture radar; Texture (cosmology); Computer vision; Gaussian; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.0165192862651067,"gpt":0.2216685254151356,"spread":0.2051492391500289,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005051785,0.0001857665,0.0002836618,0.0003759931,0.0001092695,0.00008395776,0.00005525925,0.0001522818,9.681803e-7],"category_scores_gemma":[0.0002721637,0.0001606022,0.00005838511,0.0002641322,0.00008134966,0.00009731687,0.00001713777,0.0004334021,0.00000118583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005076868,"about_ca_system_score_gemma":0.000005031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007227885,"about_ca_topic_score_gemma":0.00008162979,"domain_scores_codex":[0.9991445,0.00007588814,0.0002318804,0.000150982,0.0001913374,0.0002054261],"domain_scores_gemma":[0.9992259,0.0001239158,0.0001491658,0.0002050896,0.0001589798,0.0001369119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004910923,0.000005057199,0.00002141863,0.00006056152,0.00001676505,0.0000180256,0.0001674926,0.0001136915,0.1113731,3.564346e-7,0.00002599593,0.8881484],"study_design_scores_gemma":[0.0004590338,0.0002303537,0.002727882,0.0003649847,0.00005249014,0.0006305461,0.0001182033,0.8796838,0.1115024,0.0001204395,0.003920035,0.0001898184],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5790357,0.0004155108,0.418498,0.0006094881,0.0009374768,0.0001411119,8.210242e-7,0.00009099623,0.0002709331],"genre_scores_gemma":[0.9853997,0.0001868411,0.01354064,0.0003594469,0.0004734655,8.74402e-9,9.753541e-7,0.00003298608,0.000005947431],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8879586,"threshold_uncertainty_score":0.6549167,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2036842392","doi":"10.1109/lgrs.2012.2236818","title":"Learn Multiple-Kernel SVMs for Domain Adaptation in Hyperspectral Data","year":2013,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":89,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Hyperspectral imaging; Support vector machine; Pattern recognition (psychology); Artificial intelligence; Computer science; Domain adaptation; Pairwise comparison; Robustness (evolution); Kernel (algebra); Boosting (machine learning); Classifier (UML); Binary classification; Machine learning; Data mining; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.03616628011789082,"gpt":0.2394416021260729,"spread":0.203275322008182,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003148293,0.0001566553,0.0001492559,0.0001582658,0.0001144967,0.000162128,0.0001811421,0.00006697873,6.08186e-7],"category_scores_gemma":[0.0001217278,0.0001538846,0.00002506498,0.0002442317,0.0001972179,0.0005279158,0.00002397954,0.0001574114,0.00001767146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008087224,"about_ca_system_score_gemma":0.00001639716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007972455,"about_ca_topic_score_gemma":0.0001984319,"domain_scores_codex":[0.998755,0.00003208916,0.0002252055,0.0004259765,0.000166579,0.0003951227],"domain_scores_gemma":[0.9992781,0.0001361401,0.00004503623,0.000432321,0.00003682805,0.00007158561],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004403908,0.000003713444,0.00003791305,0.00002513811,0.000004369099,0.000006416633,0.0007207358,0.006282023,0.8243346,0.00000294698,0.0006906335,0.1678872],"study_design_scores_gemma":[0.000334438,0.00001046532,0.006599773,0.00005685684,0.000005369597,0.00003308833,0.0004891293,0.9888607,0.002783141,0.0001465373,0.0004788131,0.0002017043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5969537,0.00002673212,0.4001009,0.002128731,0.0003659494,0.0002599118,0.000003449784,0.00009760565,0.00006301432],"genre_scores_gemma":[0.7117545,0.00002731091,0.2875543,0.0004359608,0.0001264104,1.750235e-7,0.00002042196,0.00002727797,0.0000536175],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9825786,"threshold_uncertainty_score":0.6275231,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4321783250","doi":"10.1111/mice.12981","title":"Large‐scale building damage assessment using a novel hierarchical transformer architecture on satellite images","year":2023,"lang":"en","type":"article","venue":"Computer-Aided Civil and Infrastructure Engineering","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":88,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Texas A and M University","keywords":"Computer science; Transformer; Architecture; Deep learning; Satellite imagery; Encoder; Artificial intelligence; Data mining; Real-time computing; Remote sensing; Engineering; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.008401223590461457,"gpt":0.2299937896692188,"spread":0.2215925660787574,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002054752,0.0004734845,0.0003996387,0.0004909429,0.0001523575,0.0002020657,0.0002096741,0.0001943021,0.0000120836],"category_scores_gemma":[0.00001677296,0.000464131,0.0001118352,0.0006161067,0.00005145959,0.0002278576,0.0000662922,0.0008164558,0.000004880495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001384806,"about_ca_system_score_gemma":0.00002619015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000143401,"about_ca_topic_score_gemma":0.000002017384,"domain_scores_codex":[0.9981216,0.00002438916,0.0003948233,0.0004816356,0.0003010489,0.0006765241],"domain_scores_gemma":[0.9991953,0.0001469596,0.00004303874,0.0003695122,0.00003924351,0.0002059935],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004367552,0.000008662484,0.00006844717,0.000187158,0.00004288542,0.00001231689,0.000486714,0.6170051,0.3687808,0.0002516166,0.0001016093,0.01305029],"study_design_scores_gemma":[0.0005649793,0.00004695286,0.0526069,0.0002744804,0.00002605574,0.00007352934,0.00003380952,0.9345961,0.006737398,0.0003161654,0.004242151,0.0004814339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3941445,0.00006394486,0.6040034,0.00006511798,0.0005222458,0.0001602433,0.00002567887,0.0007769031,0.0002379287],"genre_scores_gemma":[0.8354411,0.00009534133,0.1637128,0.00005974987,0.0005004578,0.000005645599,0.00004912035,0.0001233894,0.00001232393],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4412966,"threshold_uncertainty_score":0.999781,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2135350092","doi":"10.1109/tgrs.2004.832239","title":"Derivative spectral unmixing of hyperspectral data applied to mixtures of lichen and rock","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":84,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Endmember; Hyperspectral imaging; Remote sensing; Subpixel rendering; Spectral signature; Spectral resolution; Pixel; VNIR; Computer science; Spectral bands; Geology; Environmental science; Spectral line; Artificial intelligence; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.02268610328809217,"gpt":0.2440979039957483,"spread":0.2214118007076561,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001271109,0.0001348306,0.0001884162,0.0001893766,0.0001110704,0.00002909068,0.0001096891,0.00005806026,6.306208e-7],"category_scores_gemma":[0.00001312077,0.000131513,0.00002174167,0.0003850975,0.0001194684,0.0001550865,0.000003788023,0.0001471303,0.00000130259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004342953,"about_ca_system_score_gemma":0.00003132079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001664325,"about_ca_topic_score_gemma":0.00006170103,"domain_scores_codex":[0.9991106,0.00001143553,0.0002120212,0.0003002874,0.0001666169,0.0001990283],"domain_scores_gemma":[0.999454,0.00004907476,0.00004333551,0.000331793,0.00004112137,0.00008063154],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001449526,0.00001106572,2.925897e-7,0.0000355989,0.00001466956,0.000001972791,0.00182177,0.02989039,0.8537446,0.00001497358,0.00000184571,0.1144483],"study_design_scores_gemma":[0.000248746,0.00005306457,0.0005853753,0.0001363496,0.00002911864,0.00004563827,0.0006288189,0.2128939,0.784958,0.0002459846,0.00001319998,0.0001618251],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3000229,0.00002878557,0.6993467,0.0001206711,0.0001076304,0.0001075542,0.000007863229,0.00004544981,0.0002124416],"genre_scores_gemma":[0.7523754,0.00005624541,0.2475079,0.0000210245,0.00001686286,2.236158e-8,9.638143e-7,0.00001234965,0.000009193837],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4523525,"threshold_uncertainty_score":0.5362946,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4388756939","doi":"10.1109/jstars.2023.3333959","title":"Concatenated Deep-Learning Framework for Multitask Change Detection of Optical and SAR Images","year":2023,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":82,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Hubei Key Laboratory of Intelligent Geo-Information Processing; Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China; Ministry of Natural Resources","keywords":"Computer science; Artificial intelligence; Deep learning; Synthetic aperture radar; Change detection; Computer vision; Remote sensing; Pattern recognition (psychology); Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.03821191737512016,"gpt":0.253660164354144,"spread":0.2154482469790238,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003057923,0.000113038,0.0002345995,0.0002816336,0.0000869221,0.00003944864,0.00003437509,0.0001520288,2.631304e-7],"category_scores_gemma":[0.0003650606,0.0001136419,0.00002773149,0.0007132839,0.00005666908,0.00009514999,0.000008579187,0.0003823049,3.103299e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003834873,"about_ca_system_score_gemma":0.00001623283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009642762,"about_ca_topic_score_gemma":0.00001958343,"domain_scores_codex":[0.9991324,0.00002487255,0.0004147308,0.0001134432,0.0001360895,0.0001784731],"domain_scores_gemma":[0.9991213,0.0002751625,0.0001537316,0.0000759893,0.0003236851,0.0000501246],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000208883,0.000004761281,0.0001301282,0.0001055251,0.00003220542,0.00000469734,0.0004444184,0.004746296,0.5436924,0.00006020968,0.000003993178,0.4507545],"study_design_scores_gemma":[0.0005202518,0.00005584128,0.08742364,0.0002066834,0.00003695641,0.00004191852,0.0002430039,0.8280145,0.08186916,0.001115683,0.0003320386,0.0001403465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7570879,0.0001249131,0.2421305,0.0001714342,0.0002075133,0.0001864666,0.000001424216,0.00006263606,0.00002721072],"genre_scores_gemma":[0.7914066,0.0005240832,0.2078548,0.00001291538,0.0001672822,9.748351e-8,0.00000414659,0.00002228965,0.000007796991],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8232682,"threshold_uncertainty_score":0.4634182,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}