{"meta":{"query_hash":"7a06e3c85785","filters":{"venue":"Remote Sensing Letters"},"cohort_total":33,"direct_labels_cover":0,"predictions_cover":33,"exported":33,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/7a06e3c85785","api":"https://metacan.xera.ac/api/v1/cohort?venue=Remote+Sensing+Letters"},"results":[{"id":"W1969669786","doi":"10.1080/01431161.2010.516281","title":"Comparison of a regional-level habitat index derived from MERIS and MODIS estimates of canopy-absorbed photosynthetically active radiation","year":2010,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; University of British Columbia","funders":"Canadian Space Agency; Government of Canada","keywords":"Photosynthetically active radiation; Moderate-resolution imaging spectroradiometer; Environmental science; Remote sensing; Imaging spectrometer; Leaf area index; Vegetation (pathology); Spectroradiometer; Canopy; Seasonality; Primary production; Enhanced vegetation index; Calibration; Climatology; Physical geography; Vegetation Index; Geography; Normalized Difference Vegetation Index; Satellite; Spectrometer; Ecology; Ecosystem; Reflectivity; Geology; Photosynthesis","score_opus":0.015156506458927622,"score_gpt":0.24866838109365033,"score_spread":0.2335118746347227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969669786","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9901595,0.000011222848,0.0073801596,0.0017533505,0.00012298283,0.00019859964,0.000016111304,0.000031379346,0.00032666902],"genre_scores_gemma":[0.8923846,0.0000063174275,0.10723438,0.00029810713,0.00003036938,2.733554e-8,0.000019759336,0.00002031214,0.0000061129376],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99851763,0.00007842087,0.00035880256,0.00039607557,0.00041040624,0.00023864237],"domain_scores_gemma":[0.9988625,0.00033486227,0.00034912172,0.0003181283,0.000030598683,0.000104765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011260036,0.00021837623,0.00040101135,0.000054540873,0.000088766166,0.000021208627,0.00012553556,0.00015490959,0.00003259194],"category_scores_gemma":[0.00020736111,0.00019091854,0.00007608611,0.00018108814,0.00066072965,0.00011440326,0.00008390579,0.0003025078,0.000012031026],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005433556,0.000020892132,0.0054937066,0.000008030027,0.000036584912,0.0000039814936,0.0014896872,0.0008543403,0.9281659,9.2053654e-7,0.0004899703,0.06338164],"study_design_scores_gemma":[0.0003101277,0.000020490437,0.30671984,0.000065611,0.000052510306,0.000020353782,0.00014186306,0.13024364,0.561881,0.00020088788,0.00013815424,0.0002055123],"about_ca_topic_score_codex":0.0053723934,"about_ca_topic_score_gemma":0.0010221765,"teacher_disagreement_score":0.3662849,"about_ca_system_score_codex":0.00008035678,"about_ca_system_score_gemma":0.000012957863,"threshold_uncertainty_score":0.81214875},"labels":[],"label_agreement":null},{"id":"W2007133996","doi":"10.1080/01431161003743165","title":"Nonlinear anisotropic diffusive filtering applied to the ocean's mean dynamic topography","year":2010,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Geophysics and Gravity Measurements","field":"Earth and Planetary Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geoid; Geology; Gulf Stream; Geodesy; Current (fluid); Smoothing; Anisotropy; Isotropy; Ocean current; Residual; Remote sensing; Geophysics; Oceanography; Computer science; Physics; Algorithm","score_opus":0.007654085266722045,"score_gpt":0.1931393983881176,"score_spread":0.18548531312139557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007133996","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9920226,0.000005067491,0.0020465574,0.004052684,0.00084077,0.00020856824,0.000016574153,0.000043761505,0.0007634224],"genre_scores_gemma":[0.9814155,0.0000019974173,0.013520146,0.004725878,0.00025906265,1.0496511e-8,0.00003939419,0.000007763984,0.00003020943],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9988448,0.000032561562,0.00014032159,0.00031633413,0.0003092989,0.00035669477],"domain_scores_gemma":[0.9993375,0.000044344186,0.000056060853,0.00041520025,0.000027743572,0.00011915009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001872448,0.00016199844,0.00014190949,0.000078277684,0.00029250377,0.00011007342,0.0002188069,0.000040782674,0.00004412907],"category_scores_gemma":[0.000016128466,0.00012034208,0.000082142586,0.00025017484,0.00006344096,0.000042755266,0.00002069038,0.00028854198,0.0002744642],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048482798,0.000016199196,0.004502814,0.00002575238,0.0000886802,0.000056693323,0.0015870662,0.003194413,0.5112963,0.000012165975,0.0016114004,0.47756004],"study_design_scores_gemma":[0.001430279,0.00018163766,0.6857855,0.00011892755,0.00016248602,0.00010790282,0.0006168809,0.21799682,0.009873982,0.0015137778,0.08039767,0.0018141485],"about_ca_topic_score_codex":0.0016782854,"about_ca_topic_score_gemma":0.004135745,"teacher_disagreement_score":0.6812827,"about_ca_system_score_codex":0.0000038588955,"about_ca_system_score_gemma":0.000011258525,"threshold_uncertainty_score":0.49074078},"labels":[],"label_agreement":null},{"id":"W2017706717","doi":"10.1080/01431161.2011.559289","title":"Assessing the utility of LiDAR to differentiate among vegetation structural classes","year":2011,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Parks Canada; University of British Columbia","funders":"Parks Canada","keywords":"Canopy; Lidar; Vegetation (pathology); Structural complexity; Ranging; Remote sensing; Metric (unit); Percentile; Field (mathematics); Forest structure; Contrast (vision); Environmental science; Geography; Physical geography; Computer science; Statistics; Mathematics; Artificial intelligence","score_opus":0.02640850270242466,"score_gpt":0.25940041543797127,"score_spread":0.2329919127355466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017706717","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9626676,0.0000033728836,0.028177498,0.0006546167,0.00014743076,0.00018417899,5.0095383e-7,0.000049084618,0.008115728],"genre_scores_gemma":[0.97487366,7.7972487e-7,0.02449032,0.00054759736,0.00003647314,3.001591e-8,0.0000025944305,0.000016440837,0.000032074888],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9987976,0.00013769513,0.00025492135,0.00030043378,0.00026562085,0.00024375973],"domain_scores_gemma":[0.99921775,0.000071736846,0.00014898813,0.00047012497,0.000015709433,0.00007567576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022441235,0.00014137638,0.00014729475,0.00003848091,0.00023867446,0.000059290618,0.00016394183,0.00004553312,0.00006760489],"category_scores_gemma":[0.000051850653,0.0001057671,0.0000770863,0.00027296977,0.0003159716,0.00016794713,0.00008799907,0.00015044726,0.000076057186],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017989061,0.00001903645,0.028813444,0.000027121718,0.000045863562,0.000010288017,0.007390028,0.0005454612,0.50614357,0.000011333684,0.001096802,0.45587906],"study_design_scores_gemma":[0.000067265995,0.0000069934817,0.92708755,0.000038462655,0.00002924262,0.000010653197,0.00013052682,0.05079682,0.021216035,0.00029994824,0.00018609998,0.00013038857],"about_ca_topic_score_codex":0.0024897922,"about_ca_topic_score_gemma":0.00021240994,"teacher_disagreement_score":0.8982741,"about_ca_system_score_codex":0.00005025567,"about_ca_system_score_gemma":0.0000062934128,"threshold_uncertainty_score":0.43130574},"labels":[],"label_agreement":null},{"id":"W2022858746","doi":"10.1080/01431161.2010.510810","title":"Relationship between canopy height and Landsat ETM+ response in lowland Amazonian rainforest","year":2011,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nova Scotia Community College","funders":"U.S. Geological Survey; Ohio State University","keywords":"Canopy; Rainforest; Thematic Mapper; Swamp; Environmental science; Tree canopy; Floodplain; Remote sensing; Normalized Difference Vegetation Index; Geography; Leaf area index; Physical geography; Forestry; Ecology; Cartography; Biology","score_opus":0.02797777678425367,"score_gpt":0.23044855974179623,"score_spread":0.20247078295754256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022858746","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98733133,0.0000070548053,0.0022654193,0.0038419634,0.000044311164,0.00015591198,0.0000024399994,0.000055281012,0.0062963134],"genre_scores_gemma":[0.98489857,0.000002391977,0.013943573,0.00078888977,0.000046713616,3.8292736e-8,0.000007879156,0.000021918286,0.0002900321],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99876434,0.00017633596,0.00023899651,0.00033818957,0.00017294448,0.00030917258],"domain_scores_gemma":[0.99913347,0.0003081495,0.000074838674,0.00034955784,0.0000046411105,0.00012937142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054320716,0.00015485557,0.00016016254,0.00011128168,0.00017907347,0.000034901357,0.00008953407,0.000093299204,0.00002665401],"category_scores_gemma":[0.00013330484,0.00015099227,0.00003478101,0.00030933268,0.00023395689,0.00009725457,0.000062402105,0.00024239032,0.00022814881],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015894158,0.000009078329,0.95973057,0.000007654608,0.00000809977,0.00007429387,0.0046198904,0.00010355379,0.010619314,0.000013049873,0.0016474082,0.023008142],"study_design_scores_gemma":[0.00032799703,0.000018235241,0.9913121,0.000030303167,0.000013006681,0.000045057008,0.000053382933,0.0021113958,0.00055787264,0.00031804875,0.005012979,0.00019961744],"about_ca_topic_score_codex":0.003510199,"about_ca_topic_score_gemma":0.00064219127,"teacher_disagreement_score":0.03158153,"about_ca_system_score_codex":0.0001409298,"about_ca_system_score_gemma":0.000012234649,"threshold_uncertainty_score":0.6157286},"labels":[],"label_agreement":null},{"id":"W2030180421","doi":"10.1080/2150704x.2014.912765","title":"PolSAR image classification using a semi-supervised classifier based on hypergraph learning","year":2014,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Classifier (UML); Hyperspectral imaging; Support vector machine; Hypergraph; Contextual image classification; Synthetic aperture radar; Polarimetry; Image (mathematics); Mathematics; Scattering; Physics","score_opus":0.01990635304895458,"score_gpt":0.22621729155909354,"score_spread":0.20631093851013896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030180421","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32097432,0.000010280682,0.66980714,0.0018140966,0.000562687,0.00022575617,0.0000015824779,0.0009954005,0.005608707],"genre_scores_gemma":[0.87329745,0.000007855328,0.12454355,0.001494623,0.00033055554,1.2950903e-7,0.000047305737,0.00020984524,0.0000686851],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972904,0.0003637733,0.00051501853,0.0006533597,0.00051332486,0.0006641296],"domain_scores_gemma":[0.9983367,0.0003012321,0.0001532652,0.0009033125,0.00012364825,0.0001818427],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005576053,0.00047849293,0.0004018017,0.00053757854,0.00030753485,0.00022806837,0.00018982905,0.00023244927,0.0000125461065],"category_scores_gemma":[0.00036452257,0.00053712644,0.00020618235,0.00063154305,0.0001701069,0.00023605856,0.000021269034,0.00074240833,0.00022414456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000171458,0.000007925221,0.00003568343,0.000056522636,0.00002148787,0.000012862288,0.000087055014,0.11603257,0.82479036,0.0000073750225,0.0007526349,0.058178365],"study_design_scores_gemma":[0.0005404274,0.000023268374,0.0013305119,0.00025097645,0.000055241286,0.000033723754,0.0000517067,0.9692638,0.022641977,0.000024819126,0.005234064,0.00054946815],"about_ca_topic_score_codex":0.00006016608,"about_ca_topic_score_gemma":0.000004551588,"teacher_disagreement_score":0.85323125,"about_ca_system_score_codex":0.00044496384,"about_ca_system_score_gemma":0.000032019485,"threshold_uncertainty_score":0.99970806},"labels":[],"label_agreement":null},{"id":"W2041782372","doi":"10.1080/2150704x.2014.960611","title":"Multilook polarimetric SAR data probability density function estimation using a generalized form of multivariate K-distribution","year":2014,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Univariate; Wishart distribution; Probability density function; Multivariate statistics; Mathematics; K-distribution; Synthetic aperture radar; Covariance matrix; Covariance; Univariate distribution; Multivariate normal distribution; Probability distribution; Cumulative distribution function; Statistics; Computer science; Artificial intelligence","score_opus":0.025922708979063604,"score_gpt":0.2491051438850052,"score_spread":0.2231824349059416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041782372","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2585032,0.000019797219,0.7406662,0.00010205013,0.00010434444,0.00026208346,0.000018669432,0.00028173052,0.00004189118],"genre_scores_gemma":[0.44372913,0.0000029839584,0.55596745,0.000049280257,0.000044063832,2.985531e-8,0.00018685572,0.000019147543,0.0000010881672],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988561,0.0000783595,0.00034710488,0.00031563488,0.00019314447,0.00020964247],"domain_scores_gemma":[0.99878496,0.000117123345,0.00012474,0.00084669207,0.00007863046,0.00004788069],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054892973,0.00018060053,0.00025379704,0.0001095309,0.00011071755,0.000025267147,0.00013559173,0.00012070901,0.0000025873312],"category_scores_gemma":[0.00024184259,0.0001840566,0.00005879439,0.0003371918,0.000058599875,0.00016398904,0.000061760904,0.0001468853,0.000004216324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017959093,0.000016175103,0.000030179068,0.00008635111,0.00004501588,4.896243e-7,0.000041945565,0.0016649432,0.18976739,0.00015680067,0.00015447944,0.80801827],"study_design_scores_gemma":[0.00023775075,0.000008267356,0.00095745514,0.00005287203,0.00008799684,0.000013029879,0.0000026168425,0.9582696,0.02705837,0.00072285964,0.012414439,0.00017472972],"about_ca_topic_score_codex":0.0012904735,"about_ca_topic_score_gemma":0.00001321376,"teacher_disagreement_score":0.95660466,"about_ca_system_score_codex":0.00020683934,"about_ca_system_score_gemma":0.000013409724,"threshold_uncertainty_score":0.75056106},"labels":[],"label_agreement":null},{"id":"W2042160084","doi":"10.1080/2150704x.2012.742210","title":"A tool for semi-automated extraction of waterbody feature in SAR imagery","year":2012,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Natural Resources Canada","funders":"Canadian Space Agency","keywords":"Computer science; Feature extraction; Feature (linguistics); Remote sensing; Artificial intelligence; Satellite imagery; Synthetic aperture radar; Process (computing); Computer vision; Geology","score_opus":0.00795819410474896,"score_gpt":0.23918041699189233,"score_spread":0.23122222288714336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042160084","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9637287,0.00008325031,0.033863228,0.00034987327,0.00082974054,0.00018513954,0.0000036306683,0.0008609517,0.00009546934],"genre_scores_gemma":[0.98124856,0.000011778391,0.018293468,0.000121056284,0.00022681175,1.15570906e-7,0.00002085941,0.000046255478,0.00003108057],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992078,0.00002513198,0.00021179834,0.000120016004,0.000099215446,0.00033604715],"domain_scores_gemma":[0.999667,0.00007300903,0.000059401107,0.0001460155,0.00002139052,0.00003319784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021406899,0.00014571229,0.0001857632,0.00017663349,0.000033378743,0.000016137063,0.000036154262,0.0001339011,0.0000024026367],"category_scores_gemma":[0.000032710275,0.000145503,0.00007101125,0.00016729103,0.000017103388,0.00020758586,0.000006740587,0.00018157874,0.000011869925],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015317337,0.0000088533,0.00012109037,0.00013586337,0.000022487013,0.000006626783,0.00018163952,0.018353088,0.9601191,7.7340366e-7,0.009125792,0.011909324],"study_design_scores_gemma":[0.00032572396,0.000006408254,0.005599683,0.00020197246,0.000028371938,0.000107508415,0.000027374455,0.7945318,0.19449614,0.000006305243,0.0044438145,0.00022490184],"about_ca_topic_score_codex":0.00004339859,"about_ca_topic_score_gemma":0.0000022711554,"teacher_disagreement_score":0.7761787,"about_ca_system_score_codex":0.00012453792,"about_ca_system_score_gemma":0.0000046775513,"threshold_uncertainty_score":0.593344},"labels":[],"label_agreement":null},{"id":"W2051878103","doi":"10.1080/01431161.2011.572093","title":"A change detection measure based on a likelihood ratio and statistical properties of SAR intensity images","year":2011,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"China Scholarship Council","keywords":"Change detection; Measure (data warehouse); Histogram; Synthetic aperture radar; Computer science; Likelihood-ratio test; Detector; Artificial intelligence; Intensity (physics); Pattern recognition (psychology); Statistics; Image (mathematics); Data mining; Mathematics; Optics; Physics; Telecommunications","score_opus":0.04266488147491038,"score_gpt":0.20192387894872982,"score_spread":0.15925899747381944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051878103","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50106174,0.000042747222,0.49759027,0.00036454806,0.00023517855,0.00024959675,0.000004011783,0.00022174357,0.00023013109],"genre_scores_gemma":[0.9658592,0.0000075606154,0.033649907,0.00036619426,0.00006905168,7.017133e-8,0.0000030155613,0.00004285322,0.00000216273],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990786,0.000075766045,0.00021314976,0.00022830267,0.00019303235,0.00021116599],"domain_scores_gemma":[0.9994651,0.000036692243,0.000056860135,0.00027203732,0.00010699882,0.000062333216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019572624,0.00017743488,0.00021426464,0.00015902263,0.00005711569,0.000027006688,0.000039047492,0.00007400901,0.0000016787692],"category_scores_gemma":[0.00017228983,0.000170582,0.00003708754,0.00013333667,0.00013733437,0.00010432702,0.000011387637,0.000192915,0.0000114705745],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007754598,0.00000683485,0.00001357307,0.0001019123,0.000017018116,0.000015833606,0.0005113682,0.00007496756,0.8341554,5.983176e-7,0.00008281023,0.16494218],"study_design_scores_gemma":[0.00017893233,0.00003903268,0.006189402,0.00022946953,0.000030074856,0.000026269872,0.000040546343,0.49869114,0.49438918,0.000011711615,0.000025589503,0.00014864182],"about_ca_topic_score_codex":0.00022346419,"about_ca_topic_score_gemma":0.000022148208,"teacher_disagreement_score":0.4986162,"about_ca_system_score_codex":0.00008530085,"about_ca_system_score_gemma":0.000009683545,"threshold_uncertainty_score":0.6956132},"labels":[],"label_agreement":null},{"id":"W2064831011","doi":"10.1080/01431160903518057","title":"Comparison of surface reflectance derived by relative radiometric normalization versus atmospheric correction for generating large-scale Landsat mosaics","year":2010,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Thematic Mapper; Remote sensing; Radiance; Atmospheric correction; Normalization (sociology); Radiometry; Environmental science; Radiometric dating; Reflectivity; Scale (ratio); Bidirectional reflectance distribution function; Optics; Geology; Satellite imagery; Physics","score_opus":0.011491516870650042,"score_gpt":0.26363746541541766,"score_spread":0.25214594854476763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064831011","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6958588,0.000023806164,0.30165654,0.00025149697,0.0015095521,0.00024265157,0.0000037728641,0.000062859195,0.00039054317],"genre_scores_gemma":[0.6664124,0.000008675001,0.33292606,0.00020874963,0.00011168616,4.6636345e-8,0.000078585144,0.000034463945,0.00021932997],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831206,0.000107405285,0.0003814391,0.0004417389,0.0003709477,0.00038641752],"domain_scores_gemma":[0.99888974,0.00028817652,0.0004064343,0.00028145462,0.000052354237,0.00008181293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030872843,0.0002286901,0.0003108942,0.000021152142,0.00031278637,0.000042411055,0.000121168574,0.00018724549,0.00001736107],"category_scores_gemma":[0.0003010756,0.00021678256,0.00009456519,0.0010756532,0.0001261289,0.00021849474,0.000049498434,0.00035676215,0.00002010182],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006466972,0.000019980283,0.0016614514,0.000008681983,0.000018268787,9.536949e-7,0.0009395296,0.06441117,0.90534866,3.9368143e-7,0.015604833,0.011921402],"study_design_scores_gemma":[0.0007227029,0.000060960072,0.001364258,0.000024316323,0.000041376425,0.000012926781,0.00013162669,0.8674064,0.12618698,0.0000031620227,0.0037940976,0.00025116027],"about_ca_topic_score_codex":0.00028517627,"about_ca_topic_score_gemma":0.00060476136,"teacher_disagreement_score":0.80299526,"about_ca_system_score_codex":0.0002642381,"about_ca_system_score_gemma":0.000008934789,"threshold_uncertainty_score":0.88401365},"labels":[],"label_agreement":null},{"id":"W2101145407","doi":"10.1080/2150704x.2014.917774","title":"Improved SIFT match for optical satellite images registration by size classification of blob-like structures","year":2014,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Scale-invariant feature transform; Normalization (sociology); Computer science; Artificial intelligence; Computation; Satellite; Matching (statistics); Computer vision; Pattern recognition (psychology); Scale (ratio); Feature (linguistics); Image (mathematics); Algorithm; Mathematics; Statistics","score_opus":0.012953703082347086,"score_gpt":0.26579626452394145,"score_spread":0.25284256144159434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101145407","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012745414,0.000043964614,0.98165333,0.004775946,0.00013550709,0.000267296,0.0000030223623,0.00019497058,0.00018057568],"genre_scores_gemma":[0.33463636,0.000025554524,0.66393656,0.0012299303,0.000072226394,1.7395415e-7,0.000009419121,0.000015236317,0.000074556825],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99875224,0.00005624961,0.0003318332,0.00041191446,0.00020019287,0.0002475863],"domain_scores_gemma":[0.99860764,0.00038775615,0.00024633278,0.00056209223,0.000139867,0.00005634284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003328492,0.00015949423,0.00020989266,0.00005077723,0.00008414563,0.000107376414,0.00026931605,0.00008458049,5.281988e-7],"category_scores_gemma":[0.00037957833,0.00015172552,0.00008555147,0.0001684461,0.00011446775,0.00032197539,0.000044253415,0.00011772635,0.0000014035141],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012285028,0.0000033627007,0.0000014819934,0.000024664707,0.0000051289508,5.4844e-7,0.000038013517,0.0000025709148,0.7124107,0.00043633935,0.0015898335,0.2854751],"study_design_scores_gemma":[0.00027334865,0.000089126224,0.00067407644,0.000040162286,0.000015603346,0.000016249229,0.000009069183,0.06916533,0.9147824,0.006628564,0.008079043,0.00022705467],"about_ca_topic_score_codex":0.0000318414,"about_ca_topic_score_gemma":0.0000020803393,"teacher_disagreement_score":0.32189095,"about_ca_system_score_codex":0.000042906446,"about_ca_system_score_gemma":0.000017939938,"threshold_uncertainty_score":0.6187187},"labels":[],"label_agreement":null},{"id":"W2104863115","doi":"10.1080/2150704x.2013.846487","title":"A marked point process for automated building detection from lidar point-clouds","year":2013,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"York University","keywords":"Reversible-jump Markov chain Monte Carlo; Lidar; Point cloud; Ranging; Computer science; Maximum a posteriori estimation; Markov chain Monte Carlo; Posterior probability; Algorithm; Process (computing); Point (geometry); Bayesian probability; Point process; Artificial intelligence; Remote sensing; Mathematics; Maximum likelihood; Geography; Statistics; Geometry","score_opus":0.008926932244175245,"score_gpt":0.23605765317871152,"score_spread":0.2271307209345363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104863115","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.819316,0.0000041597414,0.17354853,0.0047036167,0.00019412975,0.0006303747,0.0000037371135,0.00062129775,0.0009781155],"genre_scores_gemma":[0.9047389,0.000001478178,0.09280684,0.0021270781,0.00016201223,6.499326e-7,0.000016597469,0.000054811317,0.00009163247],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827915,0.00007867681,0.0003160864,0.0005958561,0.0002702405,0.00045996392],"domain_scores_gemma":[0.9990576,0.00015291132,0.00016215221,0.00045503187,0.000030446654,0.0001418507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023361761,0.00023968455,0.00021581013,0.00006340876,0.00035301739,0.00013446315,0.00015408048,0.00011353,0.00011687991],"category_scores_gemma":[0.00011748358,0.00023931451,0.0001181631,0.0003195757,0.00013695128,0.00022748816,0.00005713486,0.00017906848,0.0006319354],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000146719885,0.000008925108,0.000022273482,0.000008657625,0.000019041707,0.0000027405874,0.0003381865,0.0014993284,0.8516026,4.0486273e-7,0.0039490424,0.1425341],"study_design_scores_gemma":[0.00045126607,0.000023085207,0.005016642,0.000072118215,0.00004440238,0.00004745102,0.00013996917,0.891517,0.09731062,0.0010960283,0.0038830573,0.000398406],"about_ca_topic_score_codex":0.00610323,"about_ca_topic_score_gemma":0.00010182695,"teacher_disagreement_score":0.8900176,"about_ca_system_score_codex":0.00026550726,"about_ca_system_score_gemma":0.000010460916,"threshold_uncertainty_score":0.97589624},"labels":[],"label_agreement":null},{"id":"W2217362142","doi":"10.1080/2150704x.2015.1126683","title":"Large deformation monitoring over a coal mining region using pixel-tracking method with high-resolution Radarsat-2 imagery","year":2015,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Canadian Space Agency; Priority Academic Program Development of Jiangsu Higher Education Institutions","keywords":"Deformation monitoring; Remote sensing; Tracking (education); Pixel; Deformation (meteorology); Coal; Coal mining; High resolution; Geology; Resolution (logic); Mining engineering; Environmental science; Computer science; Computer vision; Artificial intelligence; Geography; Archaeology","score_opus":0.02444688269877866,"score_gpt":0.2580892089819126,"score_spread":0.23364232628313397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2217362142","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30916104,0.000061212435,0.689492,0.0002197432,0.00023152928,0.0001444807,0.0000012342906,0.0005033574,0.00018541877],"genre_scores_gemma":[0.4045495,0.000007243748,0.59503835,0.000093111805,0.00024662612,1.0944783e-7,0.000007442407,0.000054061944,0.00000355128],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986203,0.00007703654,0.00029857748,0.0002675036,0.0003256528,0.00041096815],"domain_scores_gemma":[0.9992506,0.00009622132,0.00011806257,0.00035722813,0.000076207245,0.00010169363],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044440615,0.00025678772,0.00026379127,0.00019801743,0.00017344169,0.000093641494,0.00008852379,0.00012303719,0.0000010591677],"category_scores_gemma":[0.000036617086,0.00024890213,0.000059089765,0.00027007418,0.00003823229,0.0003544756,0.00003144284,0.00023758179,0.000004534445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008255121,0.000025815802,0.00051654776,0.00014809941,0.0001929933,0.00013076885,0.0042426195,0.0129440995,0.2114277,0.00012656976,0.001970162,0.76819205],"study_design_scores_gemma":[0.0007301976,0.00002244383,0.00058624835,0.00066043145,0.00012774985,0.0006272014,0.0007351485,0.88983905,0.063703574,0.00014028566,0.04216798,0.0006596553],"about_ca_topic_score_codex":0.00025212663,"about_ca_topic_score_gemma":0.000006818323,"teacher_disagreement_score":0.876895,"about_ca_system_score_codex":0.0004594109,"about_ca_system_score_gemma":0.000026497215,"threshold_uncertainty_score":0.9999963},"labels":[],"label_agreement":null},{"id":"W2545243407","doi":"10.1080/2150704x.2016.1244362","title":"An open-source method of constructing cloud-free composites of forest understory temperature using MODIS","year":2016,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates Bio Solutions; Alberta Innovates - Technology Futures; National Aeronautics and Space Administration","keywords":"Environmental science; Remote sensing; Moderate-resolution imaging spectroradiometer; Cloud computing; Cloud cover; Image resolution; Understory; Meteorology; Temporal resolution; Satellite; Computer science; Geography","score_opus":0.01897331462535465,"score_gpt":0.25128572903378277,"score_spread":0.23231241440842812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2545243407","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7928746,0.0000054281118,0.20601857,0.00046368517,0.0000951443,0.00013535029,0.000005982999,0.00001982753,0.00038143925],"genre_scores_gemma":[0.6811049,8.246892e-7,0.31853682,0.00027768902,0.00003911609,1.3150273e-8,0.0000021278559,0.000018148661,0.000020363503],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998837,0.00020479635,0.0002573446,0.00028062184,0.00022245773,0.00019779225],"domain_scores_gemma":[0.9990682,0.00018440728,0.00019958883,0.00047047823,0.000014809109,0.000062495215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033499644,0.000128676,0.00022408173,0.000047936865,0.0000906168,0.00002167739,0.00028391514,0.000072147726,0.000027819551],"category_scores_gemma":[0.0000621985,0.000102773105,0.000043856508,0.00014118422,0.00034100653,0.00026581693,0.00018173852,0.000084126514,0.000004233607],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018555813,0.0000072616576,0.0058751362,0.000011570249,0.0000136083145,0.000003367513,0.00038287978,0.0027755715,0.971956,0.000013421929,0.00040829403,0.01853431],"study_design_scores_gemma":[0.0017288243,0.00010534055,0.006372932,0.0006702108,0.00010641494,0.00027090835,0.0007190002,0.044876814,0.94337445,0.00081554695,0.00043845526,0.0005210859],"about_ca_topic_score_codex":0.0016659936,"about_ca_topic_score_gemma":0.00016285312,"teacher_disagreement_score":0.112518266,"about_ca_system_score_codex":0.00018156857,"about_ca_system_score_gemma":0.000013241414,"threshold_uncertainty_score":0.41909656},"labels":[],"label_agreement":null},{"id":"W2902401595","doi":"10.1080/2150704x.2018.1536300","title":"Multi-sensor, multi-scale, Bayesian data synthesis for mapping within-year wildfire progression","year":2018,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; McGill University","funders":"","keywords":"Remote sensing; Environmental science; Merge (version control); Land cover; Scale (ratio); Bayesian probability; Meteorology; Computer science; Cartography; Geography; Land use; Artificial intelligence","score_opus":0.030678716903550212,"score_gpt":0.270133768815257,"score_spread":0.23945505191170682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902401595","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60683006,0.000013894679,0.38684362,0.0030595753,0.0011737157,0.0014338988,0.00003553557,0.00034597126,0.00026375733],"genre_scores_gemma":[0.3676155,0.0000017851693,0.63081986,0.001004904,0.00031113636,7.10075e-7,0.000021836813,0.00008166625,0.00014260005],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972133,0.00022840568,0.0004174479,0.001061095,0.00042422593,0.00065550266],"domain_scores_gemma":[0.997841,0.0002627273,0.00026990368,0.0014287977,0.000013698436,0.00018387378],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009999943,0.00033236164,0.0003420621,0.00008245035,0.0004564206,0.00010425998,0.0005665901,0.00015133574,0.000034595734],"category_scores_gemma":[0.00050926657,0.00030691226,0.000089627254,0.00027869054,0.0003932754,0.000343855,0.00038999412,0.00019447527,0.0006763726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005053822,0.000049582788,0.0035987545,0.00009466562,0.000045921686,0.00004435905,0.0010266862,0.000049133083,0.7786939,1.129486e-7,0.00835562,0.20799069],"study_design_scores_gemma":[0.0006351159,0.00003262692,0.007602527,0.00060373783,0.000041700834,0.00006635901,0.0001283543,0.9651178,0.014538486,0.0000017500951,0.010757001,0.00047450984],"about_ca_topic_score_codex":0.0011436656,"about_ca_topic_score_gemma":0.0006803317,"teacher_disagreement_score":0.9650687,"about_ca_system_score_codex":0.00024333269,"about_ca_system_score_gemma":0.000011863347,"threshold_uncertainty_score":0.9999383},"labels":[],"label_agreement":null},{"id":"W3093930159","doi":"10.1080/2150704x.2020.1825869","title":"Classifying open water features using optical satellite imagery and an object-oriented convolutional neural network","year":2020,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ducks Unlimited Canada","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Satellite imagery; Thresholding; Pattern recognition (psychology); Deep learning; Satellite; Segmentation; Ranging; Random forest; Remote sensing; Support vector machine; Image (mathematics); Geology","score_opus":0.02727260073829568,"score_gpt":0.26342216964673415,"score_spread":0.23614956890843847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093930159","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.958769,0.000028582133,0.022020934,0.016106138,0.000169502,0.00030073128,0.0000019334202,0.00010984079,0.0024933373],"genre_scores_gemma":[0.84357095,0.0000072550456,0.14221111,0.013618875,0.00046464676,3.5447634e-8,0.000035841804,0.00004019152,0.000051082745],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982085,0.00014807367,0.00024159464,0.0006232257,0.00026618334,0.0005123719],"domain_scores_gemma":[0.99930024,0.000059602164,0.00006168272,0.00027637093,0.000012291843,0.0002897869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023565492,0.0002174842,0.00022706793,0.00002229993,0.0004971293,0.00025354582,0.00017343402,0.00008259497,0.000033812128],"category_scores_gemma":[0.00002786024,0.00018639241,0.000055379154,0.00020726134,0.000380249,0.0003063197,0.00037311603,0.00031341452,0.000085259366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007460137,0.000010528387,0.00050372165,0.000008904504,0.00002353999,0.00007826426,0.0009859006,0.019462483,0.8986985,0.000023263909,0.003317365,0.076812916],"study_design_scores_gemma":[0.0006663339,0.00005073924,0.021055045,0.00005970229,0.00008582265,0.00049474224,0.00019688817,0.93859786,0.011351118,0.00018641312,0.026529059,0.00072627596],"about_ca_topic_score_codex":0.00065374427,"about_ca_topic_score_gemma":0.000033217908,"teacher_disagreement_score":0.9191354,"about_ca_system_score_codex":0.000106201835,"about_ca_system_score_gemma":0.000010089968,"threshold_uncertainty_score":0.7600862},"labels":[],"label_agreement":null},{"id":"W3194300052","doi":"10.1080/2150704x.2021.1961174","title":"Estimating wind slab thickness in a Tundra snowpack using Ku-band scatterometer observations","year":2021,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Scatterometer; Snowpack; Tundra; Slab; Environmental science; Remote sensing; Geology; Snow; Ku band; Wind speed; Meteorology; Atmospheric sciences; Arctic; Geography; Geomorphology; Optics; Geophysics","score_opus":0.05089550553307153,"score_gpt":0.24415036585903807,"score_spread":0.19325486032596653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194300052","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9749677,0.00018589254,0.015912442,0.007763645,0.0007464754,0.0000980299,0.000011411102,0.00003801099,0.00027639454],"genre_scores_gemma":[0.67714757,0.000015040035,0.3138603,0.0084128305,0.00032822404,1.8154779e-8,0.00008907243,0.000014425694,0.00013249968],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858665,0.00010147134,0.00032023047,0.00036166533,0.00023419365,0.00039578334],"domain_scores_gemma":[0.999215,0.00027791745,0.00009718121,0.00027021105,0.00007384549,0.00006585412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002187701,0.00017086676,0.00024201088,0.000058708058,0.0003763349,0.00014840029,0.00008962748,0.000058978738,0.00017533037],"category_scores_gemma":[0.00019144264,0.00017303349,0.00007116281,0.0007999308,0.000088505505,0.00022741623,0.000021040367,0.00021111216,0.000041698033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026244248,0.000027227568,0.3550243,0.00017639325,0.0001227214,0.0008009366,0.004738564,0.443062,0.06963331,0.000006956804,0.0024517607,0.123929575],"study_design_scores_gemma":[0.00030301764,0.0000074313148,0.4884897,0.00020717122,0.000033127708,0.000119569195,0.00049920595,0.50829566,0.0001963161,0.00012740558,0.0014366366,0.0002847543],"about_ca_topic_score_codex":0.0076436894,"about_ca_topic_score_gemma":0.0062813,"teacher_disagreement_score":0.29794785,"about_ca_system_score_codex":0.000028528742,"about_ca_system_score_gemma":0.00006473185,"threshold_uncertainty_score":0.9989645},"labels":[],"label_agreement":null},{"id":"W3194895167","doi":"10.1080/2150704x.2021.1962575","title":"Single photon lidar signal attenuation under boreal forest conditions","year":2021,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; University of British Columbia","funders":"","keywords":"Lidar; Remote sensing; Attenuation; Environmental science; Canopy; Leaf area index; Tree canopy; Taiga; Geology; Optics; Geography; Ecology; Forestry; Physics","score_opus":0.015463424677143558,"score_gpt":0.23264204757358692,"score_spread":0.21717862289644335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194895167","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8809247,0.000007902192,0.079281926,0.010016094,0.00015140644,0.00014214028,0.0000062533377,0.00013064103,0.02933895],"genre_scores_gemma":[0.97233135,0.0000040295877,0.021537704,0.005159276,0.00014468297,6.273613e-8,0.0001693671,0.000034069624,0.0006194435],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99852484,0.000105262465,0.00023358408,0.00045829854,0.00033773537,0.0003402558],"domain_scores_gemma":[0.99923337,0.000098391756,0.00009612163,0.00042507987,0.000026972633,0.00012005024],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013066795,0.0001714581,0.0001521891,0.000044305958,0.00033457947,0.000093282644,0.00008321212,0.00008270489,0.00022627828],"category_scores_gemma":[0.00003224392,0.0001891033,0.000103745864,0.00038469548,0.00022127022,0.00011853532,0.00006676259,0.00018869324,0.00087453326],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000046131713,0.000032900116,0.00022594257,0.0000036716608,0.000017904349,0.00005504303,0.00020514123,0.003602845,0.9416382,0.00003197147,0.010583188,0.043598585],"study_design_scores_gemma":[0.0016616356,0.00008957787,0.1769562,0.00025292908,0.00028334954,0.0014786866,0.00088199304,0.20235431,0.47803006,0.008567033,0.12759879,0.0018454172],"about_ca_topic_score_codex":0.0012659943,"about_ca_topic_score_gemma":0.0007670049,"teacher_disagreement_score":0.46360815,"about_ca_system_score_codex":0.00028542834,"about_ca_system_score_gemma":0.000023459466,"threshold_uncertainty_score":0.9999034},"labels":[],"label_agreement":null},{"id":"W4309235608","doi":"10.1080/2150704x.2022.2136019","title":"Super-resolution of Sentinel-2 images using Wasserstein GAN","year":2022,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Mean squared error; Image resolution; Resolution (logic); Computer science; Superresolution; Remote sensing; Satellite; Artificial intelligence; High resolution; Spectral bands; Pattern recognition (psychology); Generative adversarial network; Image (mathematics); Mathematics; Geology; Statistics; Physics","score_opus":0.01876400338812535,"score_gpt":0.2602951166165042,"score_spread":0.24153111322837884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309235608","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.092932515,0.00006912661,0.9031227,0.003097339,0.00016818637,0.0000844306,0.0000012069311,0.0003602143,0.00016425192],"genre_scores_gemma":[0.3341782,0.0000020169625,0.66473943,0.0009974124,0.000033032306,5.743426e-8,0.0000019782335,0.000018285418,0.000029572378],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983911,0.00017352306,0.00027948437,0.00041374314,0.00041218792,0.000329954],"domain_scores_gemma":[0.99901813,0.00006281508,0.00020770462,0.00059122907,0.00007709051,0.00004300924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038681968,0.00015414934,0.00019934811,0.00023790932,0.0003405842,0.00006864172,0.0004841722,0.000024597335,0.00000361498],"category_scores_gemma":[0.00007237907,0.0001808884,0.000084492305,0.00060863927,0.00009957493,0.0004338805,0.00043156988,0.00024593805,0.0000022457432],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000045926804,0.000008677619,0.000015053591,0.000020973883,0.000007411502,0.000053715325,0.00023525319,0.0017371648,0.97944534,0.000034566987,0.00075339014,0.017683888],"study_design_scores_gemma":[0.00014636383,0.00001324296,0.000043328608,0.00005068081,0.000010649729,0.00022671152,0.000043994612,0.8367314,0.16080967,0.0009610217,0.0007468504,0.00021610183],"about_ca_topic_score_codex":0.00014163187,"about_ca_topic_score_gemma":5.9889163e-7,"teacher_disagreement_score":0.8349942,"about_ca_system_score_codex":0.00019919548,"about_ca_system_score_gemma":0.000052617306,"threshold_uncertainty_score":0.73764145},"labels":[],"label_agreement":null},{"id":"W4313528395","doi":"10.1080/2150704x.2022.2163203","title":"R-ProjNet: an optimal rotated-projection neural network for wood segmentation from point clouds","year":2022,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"China Postdoctoral Science Foundation; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Point cloud; Projection (relational algebra); Computer science; Artificial intelligence; Segmentation; Preprocessor; Artificial neural network; Computer vision; Convolution (computer science); Laser scanning; Process (computing); Algorithm; Laser; Optics","score_opus":0.014473764001258922,"score_gpt":0.24153732726337632,"score_spread":0.2270635632621174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313528395","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89419013,0.0000084277,0.101021804,0.0030847096,0.0005187888,0.0006058885,0.000016658862,0.00017546206,0.00037813428],"genre_scores_gemma":[0.75771713,0.0000021348394,0.23672147,0.00439148,0.0005505091,0.0000013628024,0.0004270034,0.0000636223,0.00012527213],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819696,0.00021585966,0.00026620744,0.000575179,0.00034036388,0.00040542832],"domain_scores_gemma":[0.9992736,0.000070825015,0.00015671493,0.00039423458,0.000009909491,0.00009474215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000356421,0.00018876846,0.0001585268,0.00004019601,0.0009143909,0.00007869262,0.00014628902,0.000044487904,0.00015342064],"category_scores_gemma":[0.000012365588,0.0002128135,0.000099014855,0.00033218515,0.00008605071,0.00015826982,0.00011079582,0.00024876487,0.000051439452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010844906,0.000021005302,0.00005881068,0.0000019254144,0.0000201637,0.000007875805,0.0010548665,0.47010002,0.25909713,0.0000016609143,0.01161721,0.25791088],"study_design_scores_gemma":[0.000536527,0.000119589575,0.0016488311,0.0000071454333,0.000052248477,0.000068160516,0.00049588096,0.97974336,0.0043702107,0.000270142,0.012349937,0.0003379623],"about_ca_topic_score_codex":0.0037673367,"about_ca_topic_score_gemma":0.00015174565,"teacher_disagreement_score":0.5096433,"about_ca_system_score_codex":0.0004101753,"about_ca_system_score_gemma":0.000011805084,"threshold_uncertainty_score":0.86782825},"labels":[],"label_agreement":null},{"id":"W4364376900","doi":"10.1080/2150704x.2023.2201381","title":"Reducing patch-like Errors in SAR offset tracking displacements using logarithmic transformation and a weighted NCC algorithm","year":2023,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Central South University; National Natural Science Foundation of China; Ministry of Natural Resources","keywords":"Synthetic aperture radar; Logarithm; Algorithm; Offset (computer science); Amplitude; Residual; Computer science; Standard deviation; Transformation (genetics); Displacement (psychology); Matching (statistics); Mathematics; Computer vision; Optics; Physics; Mathematical analysis; Statistics","score_opus":0.013457548497043545,"score_gpt":0.23973970670976066,"score_spread":0.22628215821271713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4364376900","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46559566,0.000030940773,0.5328654,0.0006330194,0.00013219616,0.00023153047,0.0000061315686,0.00040329,0.00010181147],"genre_scores_gemma":[0.5053501,0.00012603441,0.49410525,0.00021463074,0.00007153369,3.4804032e-7,0.000055139004,0.000069028996,0.000007915067],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988831,0.00003854474,0.00032227335,0.00025013636,0.00017659579,0.00032934418],"domain_scores_gemma":[0.9996222,0.000059535345,0.00004537755,0.00020453757,0.000016714128,0.00005162299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024804563,0.00020095741,0.00021472579,0.00033491367,0.000117329815,0.000046018515,0.0000634092,0.00010668779,0.0000030087879],"category_scores_gemma":[0.000007910967,0.00021660222,0.000045450226,0.00050703343,0.00004400857,0.0001575428,0.000016701453,0.0002196186,0.000008087146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031393677,0.0000039525667,0.000014834853,0.000054419885,0.000022004511,0.000021735015,0.001163448,0.0004033418,0.043669257,0.0000050416556,0.0001944639,0.95444435],"study_design_scores_gemma":[0.00026032623,0.000004789624,0.00025729233,0.00031139032,0.000023446248,0.00004669006,0.00020880552,0.9794453,0.008854106,0.00010516464,0.010240074,0.00024261426],"about_ca_topic_score_codex":0.00041862688,"about_ca_topic_score_gemma":0.000023806248,"teacher_disagreement_score":0.97904193,"about_ca_system_score_codex":0.00018348255,"about_ca_system_score_gemma":0.000009365282,"threshold_uncertainty_score":0.8832782},"labels":[],"label_agreement":null},{"id":"W4386528487","doi":"10.1080/2150704x.2023.2254912","title":"PRO-YOLOv4-tiny: towards more balance between accuracy and speed in the detection of small targets in remotely sensed images","year":2023,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Infrared Target Detection Methodologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Nanjing University of Aeronautics and Astronautics; Ministry of Education; Ministry of Natural Resources","keywords":"Computer science; Key (lock); Pyramid (geometry); Remote sensing; Pooling; Fuse (electrical); Artificial intelligence; Drone; Computer vision; Computer security","score_opus":0.046514707864767704,"score_gpt":0.27795904916856445,"score_spread":0.23144434130379676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386528487","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9750341,0.000077780474,0.022270318,0.0015787903,0.0002593843,0.00032967987,0.000005390741,0.0003292113,0.00011532401],"genre_scores_gemma":[0.9537783,0.000079751146,0.045737147,0.00022009131,0.00012460178,1.5802406e-7,0.000007019077,0.000042410826,0.000010473031],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99843156,0.00034604498,0.00038181452,0.00026519957,0.00019116294,0.00038422336],"domain_scores_gemma":[0.9987878,0.0007679549,0.00008871989,0.0002888378,0.00003922618,0.000027453785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010969619,0.00020587526,0.00033141908,0.00045209515,0.00004900656,0.000034622684,0.00012166562,0.00013312654,7.3501167e-7],"category_scores_gemma":[0.0013306177,0.00018704837,0.000051345014,0.0010805496,0.00009186989,0.00010770282,0.000042150456,0.00047075108,0.000006881205],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020979269,0.0000013847255,0.0003973369,0.00012245018,0.000015745713,0.00011366756,0.0016499106,0.010712187,0.85180384,1.7849945e-7,0.00008660401,0.13507569],"study_design_scores_gemma":[0.00061247963,0.000024903453,0.22876984,0.00016493058,0.000019776522,0.000069883376,0.00076334487,0.1633799,0.60533434,0.00023197934,0.00029571567,0.00033292946],"about_ca_topic_score_codex":0.0003206606,"about_ca_topic_score_gemma":0.0000301996,"teacher_disagreement_score":0.24646954,"about_ca_system_score_codex":0.00009539361,"about_ca_system_score_gemma":0.000012880314,"threshold_uncertainty_score":0.7627611},"labels":[],"label_agreement":null},{"id":"W4388023413","doi":"10.1080/2150704x.2023.2273244","title":"Change Detection in Synthetic Aperture Radar Images based on a Spatial Pyramid Pooling Attention Network (SPPANet)","year":2023,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Pooling; Computer science; Pyramid (geometry); Synthetic aperture radar; Artificial intelligence; Change detection; Cluster analysis; Radar imaging; Pattern recognition (psychology); Remote sensing; Computer vision; Data mining; Radar; Geography; Mathematics; Telecommunications","score_opus":0.01600741610846116,"score_gpt":0.21389810505464193,"score_spread":0.19789068894618078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388023413","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63825417,0.0000605287,0.34940532,0.006712817,0.0021517086,0.0007310872,0.000004873988,0.0022661837,0.00041331584],"genre_scores_gemma":[0.99256617,0.000033541433,0.0052998196,0.0011369364,0.0007481097,3.9291174e-7,0.000063370746,0.00012992889,0.000021749145],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981935,0.00014963506,0.0003318598,0.0004323414,0.00032299687,0.00056964334],"domain_scores_gemma":[0.9991065,0.0002658977,0.00007981945,0.0004473952,0.00003182143,0.00006852553],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004073344,0.0002957329,0.00026468982,0.00046864842,0.00012814664,0.00009534823,0.000085934844,0.00016846723,0.000002971688],"category_scores_gemma":[0.00017581428,0.0003366857,0.00010856452,0.0009154982,0.000057286372,0.00012802033,0.000017164402,0.00042662062,0.00022177407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027593454,0.000004165952,0.00001905473,0.0000727446,0.000012273438,0.0001273469,0.000095637275,0.11930929,0.59081274,2.1023965e-7,0.0005198091,0.28899914],"study_design_scores_gemma":[0.00033541315,0.000016205844,0.012153261,0.0005193349,0.00002132778,0.000026913849,0.000020764193,0.97624415,0.009323788,0.0000143063935,0.0010043024,0.0003202357],"about_ca_topic_score_codex":0.0003257587,"about_ca_topic_score_gemma":0.000118272095,"teacher_disagreement_score":0.85693485,"about_ca_system_score_codex":0.00036839035,"about_ca_system_score_gemma":0.000009344128,"threshold_uncertainty_score":0.9999085},"labels":[],"label_agreement":null},{"id":"W4388927197","doi":"10.1080/2150704x.2023.2280464","title":"Peace-Athabasca Delta water surface elevations and slopes mapped from AirSWOT Ka-band InSAR","year":2023,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Victoria; Environment and Climate Change Canada","funders":"National Aeronautics and Space Administration","keywords":"Delta; Interferometric synthetic aperture radar; Geology; Surface water; Hydrology (agriculture); River delta; Wetland; Ocean surface topography; Elevation (ballistics); Environmental science; Streamflow; Water level; Glacier; Synthetic aperture radar; Remote sensing; Physical geography; Geomorphology; Oceanography; Drainage basin; Geography; Ecology","score_opus":0.011270078564690414,"score_gpt":0.21626528821225227,"score_spread":0.20499520964756185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388927197","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97911036,0.000015043189,0.005424687,0.013442479,0.00018532712,0.00020275962,0.0000067164333,0.00016988869,0.001442723],"genre_scores_gemma":[0.9754792,0.00012971897,0.019765178,0.0024711958,0.000084169784,1.4500273e-7,0.00012930219,0.000038673297,0.0019024189],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99868685,0.00006462222,0.00017292982,0.00040876833,0.00028844384,0.00037840768],"domain_scores_gemma":[0.9995457,0.000053163247,0.000040187137,0.00028051605,0.0000044901326,0.000075926066],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00023324256,0.00016939339,0.00014255334,0.000053704156,0.00024512605,0.00009281038,0.00011001005,0.000044021526,0.00016006253],"category_scores_gemma":[0.000010963901,0.00014003998,0.00004552732,0.00021038787,0.00011157633,0.00018130826,0.00022112334,0.0001160106,0.001087678],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018110133,0.000015787917,0.0044580777,0.000017175738,0.00007608694,0.00011974046,0.0021278507,0.006466324,0.8310829,0.0000044604244,0.12116773,0.034445785],"study_design_scores_gemma":[0.0026030506,0.00007888417,0.18100126,0.00018759594,0.00026717296,0.00001862219,0.0013495,0.19836709,0.12567283,0.00088436744,0.48784453,0.00172509],"about_ca_topic_score_codex":0.006271009,"about_ca_topic_score_gemma":0.0008825168,"teacher_disagreement_score":0.70541006,"about_ca_system_score_codex":0.00008056515,"about_ca_system_score_gemma":0.0000029388268,"threshold_uncertainty_score":0.9996901},"labels":[],"label_agreement":null},{"id":"W4400995019","doi":"10.1080/2150704x.2024.2370498","title":"Road extraction from remote sensing images based on a multi-scale asymmetric dual attention mechanism","year":2024,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Dual (grammatical number); Computer science; Extraction (chemistry); Scale (ratio); Mechanism (biology); Remote sensing; Computer vision; Artificial intelligence; Geology; Cartography; Geography; Physics; Chemistry; Chromatography","score_opus":0.011775537930350615,"score_gpt":0.2394074398672718,"score_spread":0.2276319019369212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400995019","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26280642,0.00012376613,0.73033,0.000898153,0.0030505252,0.00015615631,0.000011775019,0.0022385148,0.00038472432],"genre_scores_gemma":[0.8481612,0.000035981408,0.15050448,0.00043079007,0.000534589,2.91314e-8,0.00008207186,0.0001434889,0.00010737421],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981504,0.00010254436,0.0003568643,0.00055921474,0.00039841112,0.00043253184],"domain_scores_gemma":[0.9992947,0.00016997167,0.00007089663,0.00034646635,0.00003728283,0.0000806626],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028882647,0.00036190148,0.000264431,0.0006997088,0.0001790201,0.00027776475,0.000056765733,0.00023370875,0.000011282803],"category_scores_gemma":[0.000050539045,0.00038426422,0.00019988591,0.0006451981,0.000028939521,0.00029164593,0.000015794916,0.0006335889,0.00031439084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008806733,0.0000045472966,2.8811473e-7,0.000043570017,0.000037229653,0.00023034973,0.000039702034,0.015226145,0.5612391,4.4918949e-7,0.0008994405,0.42227033],"study_design_scores_gemma":[0.00030832604,0.000015091236,0.0006432314,0.00071233715,0.00009975662,0.00012041098,0.000027700169,0.908209,0.088148676,0.000048948357,0.0012880758,0.00037841438],"about_ca_topic_score_codex":0.0008035431,"about_ca_topic_score_gemma":0.00002008209,"teacher_disagreement_score":0.8929829,"about_ca_system_score_codex":0.00042862786,"about_ca_system_score_gemma":0.000016497317,"threshold_uncertainty_score":0.99986094},"labels":[],"label_agreement":null},{"id":"W4401749842","doi":"10.1080/2150704x.2024.2388848","title":"Crop classification based on G-CNN using multi-scale remote sensing images","year":2024,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Ministry of Natural Resources","keywords":"Computer science; Remote sensing; Scale (ratio); Crop; Artificial intelligence; Environmental science; Geology; Cartography; Geography; Forestry","score_opus":0.03773929255656809,"score_gpt":0.2585599672577289,"score_spread":0.2208206747011608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401749842","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60348177,0.00026711635,0.3824537,0.006362049,0.0023826894,0.00027240592,0.000027274931,0.0006437829,0.004109202],"genre_scores_gemma":[0.7602639,0.000028965856,0.2349349,0.0037634987,0.0005255793,5.9284216e-10,0.00010563152,0.000042653257,0.00033486774],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99725485,0.0003167467,0.00041382134,0.0008455171,0.000506849,0.00066222355],"domain_scores_gemma":[0.99870175,0.0003502738,0.00011448416,0.00059588,0.00006335339,0.00017423044],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005773496,0.00039523473,0.00033462484,0.0004093471,0.00044379412,0.00052334345,0.00013024385,0.0001738584,0.0000588756],"category_scores_gemma":[0.00009578782,0.00034186838,0.000214014,0.0005691802,0.00017883565,0.00018976365,0.000010568375,0.00053316134,0.0005548481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050315088,0.0000040755526,0.00009372639,0.000068453395,0.000026225636,0.00046798808,0.00017933313,0.029874993,0.069885544,1.9367637e-7,0.0010242654,0.8983249],"study_design_scores_gemma":[0.0002827498,0.000029183484,0.0055870567,0.0006721229,0.000066290806,0.00019951713,0.00005752489,0.9825311,0.0027322327,0.00002783439,0.0073684263,0.00044599798],"about_ca_topic_score_codex":0.0068938364,"about_ca_topic_score_gemma":0.0004098409,"teacher_disagreement_score":0.9526561,"about_ca_system_score_codex":0.000061659295,"about_ca_system_score_gemma":0.00009288717,"threshold_uncertainty_score":0.9999033},"labels":[],"label_agreement":null},{"id":"W4402679099","doi":"10.1080/2150704x.2024.2399864","title":"Classification of hyperspectral and LiDAR data by transformer-based enhancement","year":2024,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Ministry of Natural Resources","keywords":"Hyperspectral imaging; Lidar; Remote sensing; Transformer; Computer science; Artificial intelligence; Environmental science; Geology; Engineering; Voltage","score_opus":0.02604050940488013,"score_gpt":0.25089042839100606,"score_spread":0.22484991898612594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402679099","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42446035,0.00062696374,0.56941694,0.0040950277,0.00032757947,0.00015623853,0.000023121622,0.00029067285,0.00060313643],"genre_scores_gemma":[0.9519999,0.000113209164,0.047422737,0.00015062132,0.00007221496,3.5837235e-8,0.00015894057,0.000053490698,0.000028850585],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894243,0.000031912037,0.00026909617,0.00035136624,0.00019661152,0.00020856028],"domain_scores_gemma":[0.99929476,0.000068446585,0.000029204188,0.00053368986,0.000022344548,0.000051583316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020543393,0.00016268136,0.00016152501,0.00012853902,0.00003705877,0.00007864026,0.00011054707,0.00006461269,0.000004559815],"category_scores_gemma":[0.000023186303,0.0001733651,0.000035560734,0.00020697626,0.00010305754,0.00017450773,0.00000800002,0.00016953923,0.000019998677],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000426758,0.000003291864,0.0000017623119,0.00016213245,0.000028989643,0.000005703926,0.000112834285,0.00013760773,0.85017985,0.0000064168566,0.004117801,0.14523934],"study_design_scores_gemma":[0.000112214795,0.000009099694,0.00014834055,0.00017704874,0.000042943164,0.000009672447,0.000033655622,0.75048137,0.24037138,0.000009394066,0.00845781,0.00014709737],"about_ca_topic_score_codex":0.00003398662,"about_ca_topic_score_gemma":0.000005511178,"teacher_disagreement_score":0.75034374,"about_ca_system_score_codex":0.000118413205,"about_ca_system_score_gemma":0.000023183256,"threshold_uncertainty_score":0.70696235},"labels":[],"label_agreement":null},{"id":"W4404828909","doi":"10.1080/2150704x.2024.2433746","title":"Potential of model-based polarimetric decomposition extended with multi-frequency and multi-incidence PolSAR observations","year":2024,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Polarimetry; Decomposition; Remote sensing; Computer science; Geology; Physics; Optics; Scattering","score_opus":0.015617849241129548,"score_gpt":0.24974687654730762,"score_spread":0.23412902730617807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404828909","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10139628,0.00036506006,0.89677745,0.00075069396,0.000055584762,0.0001830954,0.000011447735,0.00042593948,0.000034446137],"genre_scores_gemma":[0.47044367,0.000015131058,0.52939034,0.00009862658,0.00001448514,1.6120454e-7,0.0000088992,0.000024958781,0.000003707177],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992418,0.000019111876,0.00019990111,0.00023064161,0.00014392147,0.00016457953],"domain_scores_gemma":[0.99955106,0.000067456516,0.000031777407,0.00025339474,0.00004743767,0.00004887647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009131951,0.00015876077,0.00015576459,0.00024861936,0.00007060886,0.00004295251,0.000068784815,0.0000730976,0.000001482724],"category_scores_gemma":[0.000014750104,0.00014836647,0.00004907508,0.00040097354,0.00007912601,0.00009997935,0.000011376842,0.0001539104,0.0000021204592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000048989696,0.000017249964,0.00003087625,0.00013590745,0.000050740593,0.000028902006,0.0000868069,0.0045761555,0.77022684,0.00014726148,0.00006118148,0.2246332],"study_design_scores_gemma":[0.0001526769,0.000010860399,0.0016312782,0.00021280364,0.00006241749,0.00004765769,0.000008782869,0.96863884,0.028783254,0.00010268779,0.0001801368,0.00016863547],"about_ca_topic_score_codex":0.00027890454,"about_ca_topic_score_gemma":0.000017790162,"teacher_disagreement_score":0.96406263,"about_ca_system_score_codex":0.00008034784,"about_ca_system_score_gemma":0.00002769743,"threshold_uncertainty_score":0.6050209},"labels":[],"label_agreement":null},{"id":"W4405432806","doi":"10.1080/2150704x.2024.2433747","title":"Assessing intertidal sediment photopigment content from spectral reflectance with an UAV-mounted 10-band multispectral sensor","year":2024,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Environment and Climate Change Canada; University of British Columbia","funders":"Environment and Climate Change Canada","keywords":"Multispectral image; Intertidal zone; Sediment; Remote sensing; Reflectivity; Environmental science; Tidal flat; Geology; Oceanography; Optics; Geomorphology; Physics","score_opus":0.027129187452125156,"score_gpt":0.24824512759316358,"score_spread":0.22111594014103841,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405432806","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9879456,0.00016875513,0.0046942034,0.0016354286,0.00081442867,0.00023862063,0.000032074004,0.00022097482,0.0042499243],"genre_scores_gemma":[0.98913664,0.00000832903,0.008511671,0.0011543938,0.00055899366,5.6451444e-8,0.00016330696,0.000018127288,0.0004484606],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979953,0.00013863399,0.00032036923,0.0006407643,0.00039281946,0.00051208475],"domain_scores_gemma":[0.9993151,0.00010072149,0.00007824701,0.00029063807,0.000031303447,0.00018402893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019158058,0.00029356958,0.0002897866,0.000108365806,0.00015179961,0.00067593134,0.00012106606,0.00006021864,0.00060068566],"category_scores_gemma":[0.000009374915,0.00022698245,0.0000869774,0.00019723793,0.00007267788,0.00045367025,0.00001083019,0.00028879716,0.00017892133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010300238,0.00005119643,0.005861836,0.00023974986,0.00059766223,0.006466728,0.002396212,0.0035480398,0.5990908,0.00000905259,0.0021151437,0.37859353],"study_design_scores_gemma":[0.0008639783,0.00041052542,0.014767572,0.0009868195,0.000107551554,0.00047277706,0.0012286771,0.94156754,0.033455946,0.000045905792,0.0053069694,0.0007857469],"about_ca_topic_score_codex":0.021698065,"about_ca_topic_score_gemma":0.015772525,"teacher_disagreement_score":0.9380195,"about_ca_system_score_codex":0.00006419188,"about_ca_system_score_gemma":0.00005553993,"threshold_uncertainty_score":0.98481655},"labels":[],"label_agreement":null},{"id":"W4408154205","doi":"10.1080/2150704x.2025.2471592","title":"Unsupervised change detection in SAR images using a non-local mean filter and hyperbolic tangent sigmoid function","year":2025,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sigmoid function; Hyperbolic function; Tangent; Function (biology); Filter (signal processing); Artificial intelligence; Mathematics; Computer vision; Change detection; Computer science; Pattern recognition (psychology); Mathematical analysis; Geometry; Artificial neural network","score_opus":0.029667042632637884,"score_gpt":0.25902854345359444,"score_spread":0.22936150082095655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408154205","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30591887,0.00013200175,0.691994,0.0012591828,0.00042996206,0.00013716413,2.2008209e-7,0.000050637966,0.00007794032],"genre_scores_gemma":[0.8452895,0.000018480647,0.14718361,0.007296112,0.00015588384,1.3613386e-7,0.0000010046512,0.0000163022,0.000038950155],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855685,0.00026241154,0.00021734198,0.0004663489,0.00017465888,0.0003223826],"domain_scores_gemma":[0.9994286,0.00009019265,0.00005239624,0.00033434434,0.00004369292,0.000050832565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050570746,0.00018145675,0.00021582797,0.00041667657,0.00016091918,0.0001668782,0.00013228861,0.00007925394,0.0000010062637],"category_scores_gemma":[0.000029467303,0.00018660398,0.00005890216,0.0006790938,0.00006934199,0.00037865172,0.00012188883,0.00021117288,0.000005454982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018944209,0.0000040365467,0.000012160596,0.00001991052,0.000008415965,0.000045044897,0.00044475403,0.00007783159,0.5090721,0.0000025859108,0.00002678337,0.49026743],"study_design_scores_gemma":[0.0008460547,0.00003341776,0.004207029,0.00025728907,0.00003223441,0.000082448445,0.000045760557,0.851594,0.14167511,0.0003032947,0.0006581641,0.00026519352],"about_ca_topic_score_codex":0.001173242,"about_ca_topic_score_gemma":0.000053390126,"teacher_disagreement_score":0.8515162,"about_ca_system_score_codex":0.00014973358,"about_ca_system_score_gemma":0.000022099486,"threshold_uncertainty_score":0.76094896},"labels":[],"label_agreement":null},{"id":"W4410733381","doi":"10.1080/2150704x.2025.2511196","title":"Estimating 3D individual tree crown growth using multi-temporal LiDAR","year":2025,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Science Foundation","keywords":"Lidar; Crown (dentistry); Tree (set theory); Remote sensing; Environmental science; Computer science; Geology; Mathematics; Materials science","score_opus":0.020605769149093466,"score_gpt":0.26200826198047933,"score_spread":0.24140249283138587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410733381","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.544233,0.000009305459,0.45049128,0.0017336843,0.0002798117,0.00017264606,0.0000021897597,0.00014702539,0.0029310158],"genre_scores_gemma":[0.43750575,6.0593214e-7,0.5599104,0.00223428,0.00008613028,2.6677641e-8,0.000010684609,0.000024770547,0.00022734399],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980819,0.0001236428,0.00037124896,0.0005890848,0.000347225,0.00048689212],"domain_scores_gemma":[0.99914277,0.00009601312,0.0001520947,0.00048482182,0.000017267443,0.00010704011],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037568962,0.00026762256,0.00025067382,0.00013462993,0.0005448038,0.00015020049,0.00023402415,0.00011203433,0.000029717376],"category_scores_gemma":[0.00012651346,0.0002804561,0.00011154338,0.00063314073,0.00031378446,0.00014703782,0.00021231151,0.0003153672,0.00020472083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002233845,0.00005514871,0.005685472,0.000042368407,0.00009333914,0.00008461532,0.0012582436,0.008394421,0.49033412,0.0000126225,0.0039984724,0.4900188],"study_design_scores_gemma":[0.0006891831,0.000011417271,0.027256468,0.00022763197,0.00012964073,0.00010199618,0.000090081485,0.95748407,0.008796624,0.0002351536,0.0044163433,0.00056140346],"about_ca_topic_score_codex":0.004864385,"about_ca_topic_score_gemma":0.00017744656,"teacher_disagreement_score":0.94908965,"about_ca_system_score_codex":0.00027953766,"about_ca_system_score_gemma":0.000030277472,"threshold_uncertainty_score":0.9999648},"labels":[],"label_agreement":null},{"id":"W4411096948","doi":"10.1080/2150704x.2025.2513558","title":"Burned area detection and estimation of fire carbon emissions in Canada in 2023 using Landsat 8/9 and Sentinel 2 data","year":2025,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Key Research and Development Program of China","keywords":"Environmental science; Remote sensing; Estimation; Greenhouse gas; Fire detection; Geology","score_opus":0.01130603991954658,"score_gpt":0.2174135460278598,"score_spread":0.20610750610831324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411096948","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99857974,0.000023037892,0.000497736,0.00060215645,0.00008526187,0.000111598,0.0000020841771,0.0000050915264,0.00009328986],"genre_scores_gemma":[0.99908626,0.000004629019,0.00075484667,0.00013309093,0.000004570735,2.2180437e-8,0.0000044353055,0.000004700339,0.000007451896],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999331,0.0000664335,0.00016153371,0.00022003116,0.00010038856,0.00012058748],"domain_scores_gemma":[0.99963844,0.000085935615,0.00005575384,0.00019304614,0.000001710499,0.000025124216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001997628,0.000071913215,0.0001229192,0.000055975102,0.00003095788,0.0000099429335,0.000044649998,0.000029422701,0.0000013316346],"category_scores_gemma":[0.0001006744,0.00007458606,0.0000045493794,0.0002450239,0.000027774966,0.00006998101,0.00010729026,0.000081126185,1.7512454e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026434722,0.0000057532093,0.09812152,0.00013628817,0.000008832657,0.000069709,0.00019307226,0.02488973,0.7129387,3.463562e-8,0.00021379489,0.16339608],"study_design_scores_gemma":[0.00018511328,0.0000012210651,0.10917504,0.00025167217,0.000006304866,0.000012440149,0.00003116422,0.88860023,0.0016600544,0.0000035577807,0.000016858472,0.00005635867],"about_ca_topic_score_codex":0.9250771,"about_ca_topic_score_gemma":0.78167623,"teacher_disagreement_score":0.86371046,"about_ca_system_score_codex":0.00034534524,"about_ca_system_score_gemma":0.000026962738,"threshold_uncertainty_score":0.3041531},"labels":[],"label_agreement":null},{"id":"W4413125305","doi":"10.1080/2150704x.2025.2544355","title":"Monitoring of thermal deformation of cylindrical storage facilities with DInSAR","year":2025,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Society for Digital Humanities","funders":"Norges Forskningsråd","keywords":"Deformation (meteorology); Thermal; Deformation monitoring; Geology; Geodesy; Seismology; Remote sensing; Meteorology; Physics","score_opus":0.0067076878833108805,"score_gpt":0.20364653698305993,"score_spread":0.19693884909974904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413125305","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51706517,0.000041549534,0.48064178,0.0001522108,0.000048870657,0.00006604741,0.0000015471601,0.00009896409,0.0018838218],"genre_scores_gemma":[0.7827923,0.000010734743,0.21713041,0.000019072455,0.000018998579,8.135478e-8,0.0000011644421,0.0000091039055,0.00001810088],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995312,0.000008906675,0.00017794709,0.00007523573,0.0001068214,0.00009989473],"domain_scores_gemma":[0.9996735,0.00004603477,0.000036938574,0.00019623949,0.00003068478,0.000016636941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000062974155,0.00008906603,0.0001505391,0.000092936636,0.000027025177,0.000004516468,0.000056884906,0.000045444907,0.00000189136],"category_scores_gemma":[0.000008719209,0.000076059296,0.00003594292,0.00014023323,0.00005768966,0.0000430997,0.00001060148,0.00008563277,8.734599e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014677398,0.000007689261,0.0003041386,0.00025339643,0.00007991008,0.0000023508017,0.00083611323,0.0013420496,0.21084562,0.00014997533,0.000085246844,0.7860788],"study_design_scores_gemma":[0.00024405634,0.000020831712,0.003941378,0.000605995,0.000050713992,0.000010534305,0.0005641749,0.092733465,0.8848208,0.00009620709,0.016686335,0.00022551333],"about_ca_topic_score_codex":0.000058628775,"about_ca_topic_score_gemma":9.650789e-7,"teacher_disagreement_score":0.7858533,"about_ca_system_score_codex":0.00004957822,"about_ca_system_score_gemma":0.000009358959,"threshold_uncertainty_score":0.31016082},"labels":[],"label_agreement":null},{"id":"W7117898718","doi":"10.1080/2150704x.2025.2610448","title":"Oil spill detection from dual-polarimetric Sentinel-1 SAR imagery with supervised contrastive learning","year":2025,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Oil Spill Detection and Mitigation","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Oil spill; Synthetic aperture radar; Contrastive analysis; Pattern recognition (psychology)","score_opus":0.004631248084345905,"score_gpt":0.19012518505259124,"score_spread":0.18549393696824534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117898718","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9267615,0.000018965724,0.0655933,0.0011220617,0.00028310608,0.00005370337,0.000001628203,0.00016025046,0.0060054488],"genre_scores_gemma":[0.9916253,0.000012442182,0.004773189,0.0022988783,0.00007977778,7.011765e-8,0.000016218419,0.000022337028,0.0011717958],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986974,0.00014943098,0.00019511947,0.0004329395,0.00024425486,0.00028083465],"domain_scores_gemma":[0.99951696,0.00012019721,0.00009663961,0.00017824388,0.000019246947,0.00006872766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018370485,0.00018193416,0.00016991435,0.00016737722,0.00030369064,0.000079083424,0.000049429073,0.000076273034,0.00012534014],"category_scores_gemma":[0.00013763036,0.0001755024,0.00006811721,0.00079527346,0.00014738452,0.00015361451,0.00004998607,0.0003218098,0.00027276127],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045033925,0.000005139402,0.0012135973,0.0000050932113,0.000026541195,0.000025529822,0.000087367654,0.0011854793,0.6832347,2.4242357e-7,0.00018463861,0.31398663],"study_design_scores_gemma":[0.0032435278,0.0000918515,0.18248062,0.0003250151,0.0002736926,0.00008857009,0.00058117,0.2476769,0.5350221,0.00007094862,0.029127274,0.0010183415],"about_ca_topic_score_codex":0.0070649805,"about_ca_topic_score_gemma":0.00028734645,"teacher_disagreement_score":0.31296828,"about_ca_system_score_codex":0.00028762943,"about_ca_system_score_gemma":0.00000986365,"threshold_uncertainty_score":0.99954706},"labels":[],"label_agreement":null}]}