{"meta":{"query_hash":"784870ab8c8b","filters":{"venue":"Science of Remote Sensing"},"cohort_total":17,"direct_labels_cover":0,"predictions_cover":17,"exported":17,"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/784870ab8c8b","api":"https://metacan.xera.ac/api/v1/cohort?venue=Science+of+Remote+Sensing"},"results":[{"id":"W3213867029","doi":"10.1016/j.srs.2021.100036","title":"The Arctic Nearshore Turbidity Algorithm (ANTA) - A multi sensor turbidity algorithm for Arctic nearshore environments","year":2021,"lang":"en","type":"article","venue":"Science of Remote Sensing","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":19,"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":"Horizon 2020; Universität Potsdam; Deutsche Forschungsgemeinschaft; Deutscher Akademischer Austauschdienst; U.S. Geological Survey; European Commission; Norges Forskningsråd; European Space Agency; National Aeronautics and Space Administration; Harry Frank Guggenheim Foundation; National Science Foundation","keywords":"Colored dissolved organic matter; Arctic; Environmental science; Turbidity; Oceanography; Sediment; Permafrost; Ocean color; Thermokarst; Shore; Hydrology (agriculture); Geology; Phytoplankton; Ecology; Geomorphology; Satellite","score_opus":0.04354260994727398,"score_gpt":0.27006927495288835,"score_spread":0.22652666500561436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213867029","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":[0.959608,0.0018589284,0.029332276,0.00168283,0.003972584,0.0010634118,0.0019425366,0.00007210788,0.0004672791],"genre_scores_gemma":[0.59462184,0.0013764399,0.40045172,0.00063339045,0.0009321837,2.9612755e-7,0.00080468727,0.000035300025,0.0011441335],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99699235,0.00012811318,0.0003940817,0.00072755665,0.00084604404,0.00091184024],"domain_scores_gemma":[0.99819994,0.00047061444,0.00021318292,0.0006085173,0.00022287863,0.00028484766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012749686,0.0002725305,0.00033180893,0.00009258001,0.0012924941,0.00030065974,0.00041524853,0.000106813066,0.00017687412],"category_scores_gemma":[0.00039965275,0.00020821144,0.00018460762,0.00069384475,0.0013094727,0.00035882488,0.00009310682,0.0002919496,0.00006253147],"study_design_candidate":"design_other","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":[0.00001519529,0.000020118589,0.0021379902,0.000039696664,0.000018476685,0.00007137951,0.0006847218,0.00011972134,0.011290424,9.525981e-7,0.00006373917,0.9855376],"study_design_scores_gemma":[0.0005068894,0.00011589168,0.049094066,0.00014191029,0.000054410582,0.00033378048,0.002308589,0.924624,0.017346608,0.00046691735,0.0046186075,0.00038832772],"about_ca_topic_score_codex":0.0032239326,"about_ca_topic_score_gemma":0.0038732167,"teacher_disagreement_score":0.98514926,"about_ca_system_score_codex":0.000052070114,"about_ca_system_score_gemma":0.00024244832,"threshold_uncertainty_score":0.9940951},"labels":[],"label_agreement":null},{"id":"W4313419107","doi":"10.1016/j.srs.2022.100072","title":"Modelling internal tree attributes for breeding applications in Douglas-fir progeny trials using RPAS-ALS","year":2022,"lang":"en","type":"article","venue":"Science of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":12,"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 British Columbia","funders":"","keywords":"Heritability; Douglas fir; Tree breeding; Tree (set theory); Softwood; Branching (polymer chemistry); Genetic gain; Biology; Forestry; Mathematics; Woody plant; Botany; Geography; Genetic variation; Evolutionary biology","score_opus":0.07972688484481538,"score_gpt":0.3182160802705276,"score_spread":0.23848919542571223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313419107","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":[0.57276917,0.00003218045,0.42468727,0.0001637979,0.00015087759,0.0008400162,0.000013772338,0.000034035187,0.0013088688],"genre_scores_gemma":[0.73712313,0.0000036006552,0.2627082,0.000023853023,0.000067742825,0.0000010642714,0.000005613245,0.000017050574,0.0000497865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972797,0.000113935326,0.00073284586,0.00064238167,0.0007080014,0.0005231618],"domain_scores_gemma":[0.99862254,0.00031927213,0.00045711856,0.00042813589,0.000057772988,0.00011514366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045227753,0.00017773903,0.00040308974,0.00028543285,0.0010260525,0.00008476215,0.0005074714,0.000042182786,0.000019298688],"category_scores_gemma":[0.00021423592,0.0001780759,0.0001560362,0.0017935554,0.0005642093,0.0002604044,0.00039212697,0.00020897345,0.0000040855616],"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":[0.000025505802,0.00003800821,0.00022707149,0.000010965373,0.0000063805305,0.0000010910791,0.000748777,0.41711763,0.41497838,0.00013459957,0.000034958153,0.16667661],"study_design_scores_gemma":[0.00031078458,0.000047317266,0.00013128563,0.000037832204,0.000022953664,0.00003599909,0.00087602815,0.9611113,0.031159462,0.0039681857,0.002102138,0.00019667622],"about_ca_topic_score_codex":0.0015885298,"about_ca_topic_score_gemma":0.000052069743,"teacher_disagreement_score":0.5439937,"about_ca_system_score_codex":0.00059945806,"about_ca_system_score_gemma":0.00012012587,"threshold_uncertainty_score":0.78916705},"labels":[],"label_agreement":null},{"id":"W4327565197","doi":"10.1016/j.srs.2023.100082","title":"De-noised and contrast enhanced KH-9 HEXAGON mapping and panoramic camera images for urban research","year":2023,"lang":"en","type":"article","venue":"Science of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":10,"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 Guelph","funders":"","keywords":"Contrast (vision); Computer vision; Artificial intelligence; Art; Computer science; Computer graphics (images)","score_opus":0.028201662689907158,"score_gpt":0.3092457611526246,"score_spread":0.28104409846271744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327565197","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":[0.9783338,0.000031744614,0.016965132,0.0007501607,0.00005055829,0.00034489078,0.0000033872705,0.00005402552,0.0034663156],"genre_scores_gemma":[0.95003545,0.00004627691,0.049507342,0.00003322095,0.000029938785,1.16814434e-7,0.0000016273871,0.0000120088735,0.00033399856],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983187,0.00005558176,0.00018055092,0.00048638464,0.00040048853,0.00055834214],"domain_scores_gemma":[0.99919844,0.0002525472,0.00007030406,0.00026064858,0.000068567526,0.0001494966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020626485,0.00010205403,0.00015410878,0.00019795631,0.00071125396,0.00013855255,0.00014707982,0.000045760404,0.000003234758],"category_scores_gemma":[0.00038869397,0.000098716395,0.000025116802,0.0012762268,0.002146804,0.00016781308,0.00017573782,0.00013115576,0.0000133671865],"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":[0.0000047300487,0.00000225136,0.00005898034,0.000010101435,0.0000012562958,8.983607e-7,0.00093635014,0.000044855125,0.7023248,0.000014681217,0.00019182649,0.29640925],"study_design_scores_gemma":[0.00055314664,0.00011031532,0.038373284,0.00017909518,0.000012119511,0.0000457052,0.003932585,0.3446913,0.6034885,0.006236223,0.0020554645,0.00032227824],"about_ca_topic_score_codex":0.0007371845,"about_ca_topic_score_gemma":0.00002844088,"teacher_disagreement_score":0.34464645,"about_ca_system_score_codex":0.00010148358,"about_ca_system_score_gemma":0.00006179899,"threshold_uncertainty_score":0.7909989},"labels":[],"label_agreement":null},{"id":"W4388798688","doi":"10.1016/j.srs.2023.100110","title":"Modelling tree biomass using direct and additive methods with point cloud deep learning in a temperate mixed forest","year":2023,"lang":"en","type":"article","venue":"Science of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal; Natural Resources Canada; Canadian Forest Service; University of British Columbia","funders":"Natural Resources Canada; National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Random forest; Mean squared error; Tree (set theory); Point cloud; Forest inventory; Mean absolute percentage error; Artificial neural network; Computer science; Statistics; Convolutional neural network; Environmental science; Mathematics; Remote sensing; Artificial intelligence; Forest management; Agroforestry; Geography","score_opus":0.02333127896668182,"score_gpt":0.2836347798890132,"score_spread":0.2603035009223314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388798688","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":[0.84266347,0.00001370369,0.15428151,0.00008217186,0.000058121354,0.00015206145,7.0961755e-7,0.00007019785,0.0026780518],"genre_scores_gemma":[0.65351236,0.000013874341,0.3464028,0.00000707283,0.000013605938,3.2259436e-8,0.0000011639208,0.00001390771,0.000035196685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99814445,0.00015250932,0.00025766523,0.00058411446,0.0003883476,0.00047292534],"domain_scores_gemma":[0.99926805,0.00017080993,0.00014907401,0.0002512382,0.00003913854,0.00012171085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018425408,0.00016587929,0.0002421099,0.00028775504,0.00045100256,0.000077710654,0.00013783423,0.000051406907,0.0000028123122],"category_scores_gemma":[0.00015629595,0.00014352308,0.00003371584,0.0027821637,0.0010919461,0.00026985363,0.00017618325,0.00016989271,0.00000942171],"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":[0.000014002772,0.000005221247,0.00029635645,0.000006615289,0.0000038621915,0.000011208921,0.0014698587,0.39021546,0.26834145,0.000007031804,0.0000023786447,0.33962655],"study_design_scores_gemma":[0.00016894093,0.000039475894,0.0029705025,0.00010979646,0.000011008622,0.00003822357,0.0011751207,0.9503111,0.044438146,0.0004862209,0.00007357037,0.00017786674],"about_ca_topic_score_codex":0.003367452,"about_ca_topic_score_gemma":0.000699531,"teacher_disagreement_score":0.56009567,"about_ca_system_score_codex":0.0001570653,"about_ca_system_score_gemma":0.000049436578,"threshold_uncertainty_score":0.58527017},"labels":[],"label_agreement":null},{"id":"W4395452106","doi":"10.1016/j.srs.2024.100131","title":"Retrieving forest soil moisture from SMAP observations considering a microwave polarization difference index (MPDI) to τ-ω model","year":2024,"lang":"en","type":"article","venue":"Science of Remote Sensing","topic":"Soil Moisture and Remote Sensing","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":false,"ca_institutions":"University of Guelph","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; Korea Meteorological Administration; National Aeronautics and Space Administration; U.S. Department of Agriculture; California Institute of Technology; Ministry of Science, ICT and Future Planning; Jet Propulsion Laboratory; National Research Foundation","keywords":"Microwave; Environmental science; Polarization (electrochemistry); Water content; Index (typography); Remote sensing; Soil science; Physics; Geology; Computer science; Chemistry","score_opus":0.021386274530546572,"score_gpt":0.23647095251763414,"score_spread":0.21508467798708758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395452106","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":[0.7269116,0.00007914818,0.26817572,0.0008231032,0.0004149952,0.00016975742,0.0000031822826,0.000101181955,0.0033213021],"genre_scores_gemma":[0.8572504,0.0000075527514,0.14203708,0.00036516847,0.00007682449,2.6640475e-8,0.0000055624314,0.000026219928,0.00023115595],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974105,0.00003565908,0.00040236482,0.0008189025,0.0008128488,0.0005197401],"domain_scores_gemma":[0.99895734,0.00013492434,0.00011446686,0.00049139874,0.00008031157,0.00022153485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048568746,0.00025057248,0.0002612588,0.00023088949,0.00044901852,0.00028741462,0.00031999563,0.00012967619,0.000004339918],"category_scores_gemma":[0.0005154128,0.00022950025,0.00008867121,0.0020440547,0.0006652313,0.00048494735,0.00040773148,0.0003175046,0.0000289715],"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":[0.000006758876,0.000005298421,0.0034610794,0.000014030948,0.0000064665924,0.000016417847,0.0018105665,0.03003475,0.7970954,0.0000490735,0.000054372802,0.16744578],"study_design_scores_gemma":[0.000082656654,0.00001908776,0.089169696,0.00047879154,0.000024413037,0.00002649021,0.00027179375,0.8464397,0.056349397,0.0067608245,0.00009655389,0.00028057361],"about_ca_topic_score_codex":0.007631594,"about_ca_topic_score_gemma":0.010080873,"teacher_disagreement_score":0.816405,"about_ca_system_score_codex":0.00035475404,"about_ca_system_score_gemma":0.00018861481,"threshold_uncertainty_score":0.99897665},"labels":[],"label_agreement":null},{"id":"W4400699641","doi":"10.1016/j.srs.2024.100150","title":"Towards global spaceborne lidar biomass: Developing and applying boreal forest biomass models for ICESat-2 laser altimetry data","year":2024,"lang":"en","type":"article","venue":"Science of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":13,"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","funders":"National Aeronautics and Space Administration","keywords":"Lidar; Altimeter; Remote sensing; Environmental science; Boreal; Taiga; Vegetation (pathology); Satellite; Deciduous; Geography; Forestry; Ecology","score_opus":0.037617716593094146,"score_gpt":0.30269443897367143,"score_spread":0.2650767223805773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400699641","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":[0.37119773,0.00022424189,0.61503965,0.0012071832,0.00030202503,0.00058937544,0.00007324915,0.00015094347,0.011215639],"genre_scores_gemma":[0.5912475,0.000017758184,0.40859753,0.00003565256,0.000048076028,1.1518734e-7,0.000020222136,0.00001430258,0.000018858038],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99744785,0.000026440015,0.00032830454,0.0010353063,0.00060640264,0.0005556904],"domain_scores_gemma":[0.9988003,0.000084458676,0.00009589584,0.00078192935,0.000050688,0.00018676881],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001363656,0.0002224357,0.0002358848,0.00014569816,0.00046025674,0.00030797496,0.00056166097,0.00008927973,0.0000022022246],"category_scores_gemma":[0.00014692129,0.00020135674,0.000052738604,0.0018041654,0.0011772705,0.0008229902,0.00076034287,0.00009842432,0.000015190135],"study_design_candidate":"design_other","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":[0.0000119540255,0.000008060027,0.00010049276,0.00010605929,0.00001708185,0.000009145456,0.00026171483,0.0004000395,0.03711491,0.0006362329,0.00026171154,0.9610726],"study_design_scores_gemma":[0.00015617402,0.00002783904,0.0018965018,0.00020592929,0.000039539245,0.00009669601,0.00026010527,0.9600178,0.01868889,0.014656363,0.0036580565,0.00029609047],"about_ca_topic_score_codex":0.004931317,"about_ca_topic_score_gemma":0.0008939505,"teacher_disagreement_score":0.9607765,"about_ca_system_score_codex":0.00027592207,"about_ca_system_score_gemma":0.00023713228,"threshold_uncertainty_score":0.821109},"labels":[],"label_agreement":null},{"id":"W4402093955","doi":"10.1016/j.srs.2024.100160","title":"Characterizing forest structural changes in response to non-stand replacing disturbances using bitemporal airborne laser scanning data","year":2024,"lang":"en","type":"article","venue":"Science of Remote Sensing","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":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université Laval; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Laser scanning; Remote sensing; Environmental science; Laser; Geology; Optics; Physics","score_opus":0.028283356881192157,"score_gpt":0.2974444869352533,"score_spread":0.26916113005406117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402093955","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":[0.9929567,0.00006610345,0.004617563,0.0010827405,0.00036569513,0.00024392885,0.000013949651,0.000058037414,0.0005952654],"genre_scores_gemma":[0.92456,0.0000045331094,0.0751766,0.00006711841,0.00007730902,3.2900545e-8,0.0000060881744,0.000021562424,0.00008672118],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99754393,0.000064691754,0.00033646505,0.0009362862,0.0005861917,0.00053246407],"domain_scores_gemma":[0.9987332,0.0001097695,0.00011486812,0.0008566065,0.00002608026,0.00015943979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022106997,0.0001943142,0.00023701534,0.00036438485,0.00035754702,0.0002626004,0.0005123698,0.000050326096,0.000007981776],"category_scores_gemma":[0.0002888654,0.0001775432,0.000031030468,0.002404882,0.000633162,0.0007596287,0.0006184453,0.00019612505,0.00001566967],"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":[0.00007026142,0.000002951614,0.0007178056,0.000026487858,0.0000029959765,0.000040112354,0.002419282,0.005004349,0.8362658,0.0000017572657,0.00004482075,0.15540339],"study_design_scores_gemma":[0.000114695315,0.000044674667,0.035570923,0.0009809585,0.000011784203,0.00009358422,0.00080373813,0.9092515,0.051311344,0.000101319616,0.001416434,0.00029903997],"about_ca_topic_score_codex":0.0020188193,"about_ca_topic_score_gemma":0.0010024003,"teacher_disagreement_score":0.90424716,"about_ca_system_score_codex":0.0003420202,"about_ca_system_score_gemma":0.00014014942,"threshold_uncertainty_score":0.72400016},"labels":[],"label_agreement":null},{"id":"W4402140099","doi":"10.1016/j.srs.2024.100159","title":"Characterizing annual leaf area index changes and volume growth using ALS and satellite data in forest plantations","year":2024,"lang":"en","type":"article","venue":"Science of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","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":false,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; University of British Columbia","funders":"","keywords":"Leaf area index; Remote sensing; Environmental science; Satellite; Canopy; Mean squared error; Scale (ratio); Meteorology; Geography; Mathematics; Statistics; Ecology; Cartography","score_opus":0.0294017399159381,"score_gpt":0.27525680891593746,"score_spread":0.24585506899999937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402140099","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":[0.9937474,0.00015758397,0.0040718475,0.00080168684,0.00008141802,0.00013726014,0.000026273856,0.00003247657,0.0009440238],"genre_scores_gemma":[0.97248375,0.00018974165,0.027179638,0.00005189355,0.000029546274,1.5646041e-8,0.000010169279,0.0000109630355,0.00004426565],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986349,0.000026138174,0.00018685692,0.0005698794,0.00029501604,0.00028724878],"domain_scores_gemma":[0.99942213,0.00006415982,0.00006534831,0.00033520622,0.000018371245,0.00009476726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073730474,0.00011783471,0.00014417515,0.00021159096,0.00022339671,0.00016422503,0.00018388411,0.00004408945,0.0000038049668],"category_scores_gemma":[0.00008590285,0.00011280644,0.000010265583,0.00085894024,0.000950714,0.00066082535,0.00036788607,0.00012406778,0.0000052948276],"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":[0.000006872762,0.000007888956,0.012105289,0.00005239578,0.000004851472,0.000024944495,0.004870896,0.00012697955,0.3311504,0.000042422642,0.00002590696,0.65158117],"study_design_scores_gemma":[0.0000797983,0.000017959555,0.112343945,0.00029190068,0.000014487548,0.00023611596,0.0012623365,0.8812229,0.0028834091,0.0007144554,0.00074454455,0.00018812326],"about_ca_topic_score_codex":0.0038384935,"about_ca_topic_score_gemma":0.0021380275,"teacher_disagreement_score":0.88109595,"about_ca_system_score_codex":0.000064289736,"about_ca_system_score_gemma":0.000045142937,"threshold_uncertainty_score":0.58026797},"labels":[],"label_agreement":null},{"id":"W4403848157","doi":"10.1016/j.srs.2024.100173","title":"A comprehensive evaluation of satellite-based and reanalysis soil moisture products over the upper Blue Nile Basin, Ethiopia","year":2024,"lang":"en","type":"article","venue":"Science of Remote Sensing","topic":"Soil Moisture and Remote Sensing","field":"Environmental 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":"Alberta Energy","funders":"","keywords":"Structural basin; Satellite; Environmental science; Moisture; Hydrology (agriculture); Geography; Geology; Meteorology; Engineering; Geomorphology; Geotechnical engineering","score_opus":0.018384994918113674,"score_gpt":0.2715888059147112,"score_spread":0.2532038109965975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403848157","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":[0.9916716,0.0011549371,0.00041873552,0.0014564603,0.00032240202,0.00027403902,0.0000011880022,0.000028959888,0.0046716807],"genre_scores_gemma":[0.98927516,0.000057252633,0.010335101,0.00019775426,0.00005832918,2.4758416e-8,0.0000022907875,0.000013987011,0.00006008638],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99699616,0.00019521659,0.0003141385,0.0006087722,0.0015903861,0.00029533706],"domain_scores_gemma":[0.998804,0.00018310515,0.00015135438,0.000542886,0.00024781088,0.000070843605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022606028,0.00017934,0.00024774444,0.00017036533,0.00027181456,0.000096797536,0.00018845027,0.00007904952,0.000014299522],"category_scores_gemma":[0.0003890574,0.000117698335,0.0000916777,0.0019224774,0.0020434996,0.00022300944,0.00013950904,0.0002173337,0.000009482286],"study_design_candidate":"design_other","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":[0.000011427155,0.000009014702,0.00044913148,0.00006422203,0.000020864458,0.0000055012556,0.0023436842,0.0045544845,0.31091717,0.000012006514,0.000097293276,0.6815152],"study_design_scores_gemma":[0.0002489298,0.000052733107,0.16506463,0.00039034832,0.00032535542,0.000051295854,0.0010732572,0.57972234,0.24930531,0.0009377035,0.0025707243,0.00025734803],"about_ca_topic_score_codex":0.0031654274,"about_ca_topic_score_gemma":0.0007104838,"teacher_disagreement_score":0.68125784,"about_ca_system_score_codex":0.00016200234,"about_ca_system_score_gemma":0.00020826374,"threshold_uncertainty_score":0.75293595},"labels":[],"label_agreement":null},{"id":"W4405941957","doi":"10.1016/j.srs.2024.100193","title":"Responses of spectral indices to heat and drought differ by tree size in Douglas-fir","year":2024,"lang":"en","type":"article","venue":"Science of Remote Sensing","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Government of British Columbia; Natural Resources Canada; Canadian Forest Service; University of British Columbia","funders":"Canadian Forest Service; Natural Sciences and Engineering Research Council of Canada; Genome British Columbia; Natural Resources Canada; Forest Genetics Council of British Columbia","keywords":"Douglas fir; Tree (set theory); Forestry; Environmental science; Mathematics; Statistics; Geography; Combinatorics","score_opus":0.005645166741680016,"score_gpt":0.22431357179642478,"score_spread":0.21866840505474477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405941957","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":[0.99670255,0.000075752374,0.00029156354,0.00041145895,0.00010786915,0.000077091885,0.000008982309,0.0000118107,0.0023129026],"genre_scores_gemma":[0.99028444,0.000025382598,0.009349478,0.000024978504,0.0000070813926,2.0051136e-8,5.600806e-7,0.00000438039,0.00030370452],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990443,0.000023696459,0.00016558474,0.00026144422,0.00030459295,0.00020041206],"domain_scores_gemma":[0.99969167,0.00009346433,0.000021716829,0.00012430963,0.000004349834,0.00006446851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042708952,0.000075645105,0.000115660616,0.00012065854,0.00005013689,0.00004387241,0.00011628134,0.000029403564,0.000010287675],"category_scores_gemma":[0.000063622545,0.00006258357,0.00001959585,0.0008058435,0.00047849191,0.00021226882,0.00011295317,0.000074658,0.000005254512],"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":[0.00002183403,0.0000076169335,0.0029891434,0.000011555861,0.0000017934371,0.000016465796,0.0012471657,0.00054555794,0.95224994,0.000024988893,0.000037640322,0.042846315],"study_design_scores_gemma":[0.00029679557,0.00020386174,0.1840082,0.00046886358,0.000017541764,0.00012693151,0.0003114768,0.6029429,0.2073657,0.0027392318,0.0011517551,0.0003667342],"about_ca_topic_score_codex":0.0008552855,"about_ca_topic_score_gemma":0.00041602793,"teacher_disagreement_score":0.7448842,"about_ca_system_score_codex":0.000078197765,"about_ca_system_score_gemma":0.00002126191,"threshold_uncertainty_score":0.25520837},"labels":[],"label_agreement":null},{"id":"W4407058921","doi":"10.1016/j.srs.2025.100199","title":"Evaluating war-induced damage to agricultural land in the Gaza Strip since October 2023 using PlanetScope and SkySat imagery","year":2025,"lang":"en","type":"article","venue":"Science of Remote Sensing","topic":"Animal Diversity and Health Studies","field":"Agricultural and Biological Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"United Nations University Institute for Water, Environment, and Health","funders":"Swedish National Space Agency; Kent State University; National Aeronautics and Space Administration","keywords":"Gaza strip; Environmental science; Ancient history; Palestine; History","score_opus":0.08797554670096472,"score_gpt":0.339183147469434,"score_spread":0.25120760076846926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407058921","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":[0.9960357,0.000099623096,0.000009488679,0.0021361308,0.000096915915,0.0002183679,0.000005018488,0.00001022958,0.0013885134],"genre_scores_gemma":[0.99562323,0.000023432018,0.0038386614,0.00042573837,0.000043578042,3.3373098e-8,0.0000014974008,2.8169978e-7,0.000043541077],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99878955,0.000083581814,0.00018149694,0.00029287732,0.00032553924,0.00032696873],"domain_scores_gemma":[0.99945927,0.00024345204,0.0000747747,0.000050625156,0.00011828696,0.0000536117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011287917,0.00010180274,0.00016957436,0.000043159496,0.00074934494,0.00007538953,0.00021980249,0.00003884911,0.0000025982106],"category_scores_gemma":[0.00027597885,0.000037555645,0.000024738954,0.0011886331,0.00022270491,0.00018273828,0.00022766368,0.0001294165,0.0000026225366],"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":[0.000027535685,0.000009926891,0.0020652947,0.000023364202,0.0000024847725,0.000007344045,0.0009892827,0.000051382733,0.9007188,0.000025695332,0.0000901204,0.095988736],"study_design_scores_gemma":[0.00014835798,0.00018671749,0.9731888,0.00035766655,0.000015825011,0.0000116791325,0.01061935,0.011320384,0.003657593,0.00018027108,0.00014513856,0.00016818606],"about_ca_topic_score_codex":0.0035656104,"about_ca_topic_score_gemma":0.0017844243,"teacher_disagreement_score":0.9711235,"about_ca_system_score_codex":0.00004247611,"about_ca_system_score_gemma":0.00004398041,"threshold_uncertainty_score":0.5763431},"labels":[],"label_agreement":null},{"id":"W4412935307","doi":"10.1016/j.srs.2025.100265","title":"Terrain complexity index: a novel metric for estimating multiscale three-dimensional terrain structure of montane areas based on digital elevation model","year":2025,"lang":"en","type":"article","venue":"Science of Remote Sensing","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","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":"Campbell Scientific (Canada)","funders":"Key Research and Development Program of Liaoning Province; National Natural Science Foundation of China","keywords":"Digital elevation model; Terrain; Montane ecology; Elevation (ballistics); Index (typography); Metric (unit); Remote sensing; Geography; Geology; Computer science; Physical geography; Cartography; Mathematics; Ecology; Geometry; Engineering; World Wide Web; Biology","score_opus":0.023694655407091064,"score_gpt":0.2586780160469602,"score_spread":0.23498336063986916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412935307","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":[0.6335699,0.00000797141,0.36495593,0.00014687519,0.00019216053,0.00020534743,0.00012624516,0.000021095335,0.0007745026],"genre_scores_gemma":[0.63704854,9.044758e-8,0.36281013,0.00006886046,0.000018504217,3.0723992e-9,0.000035817535,0.0000028754405,0.00001517263],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820656,0.000018998338,0.00038830642,0.000426086,0.00061536516,0.00034467812],"domain_scores_gemma":[0.9987458,0.0003615447,0.00025520672,0.0002888712,0.00025858742,0.00009000556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005053449,0.00018009753,0.0003038247,0.0005755678,0.00030935858,0.000083854946,0.00020300101,0.0000789967,0.000005652645],"category_scores_gemma":[0.0009810273,0.0001430985,0.00009086136,0.0011462207,0.0005117456,0.00024652228,0.00002124855,0.00014142596,4.6259294e-7],"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":[0.00010234738,0.000013601145,0.001037889,0.000039158273,0.0000059432505,9.106386e-7,0.00008626311,0.5760339,0.008307569,0.000009765817,0.000011863179,0.41435078],"study_design_scores_gemma":[0.0005888482,0.00009710937,0.024211166,0.00028957918,0.000012724305,0.00000863161,0.000048683567,0.96711916,0.0030331824,0.0044469144,0.000002574059,0.00014141796],"about_ca_topic_score_codex":0.0020603756,"about_ca_topic_score_gemma":0.0017182594,"teacher_disagreement_score":0.41420937,"about_ca_system_score_codex":0.000023157134,"about_ca_system_score_gemma":0.0003023694,"threshold_uncertainty_score":0.5835388},"labels":[],"label_agreement":null},{"id":"W4414282169","doi":"10.1016/j.srs.2025.100285","title":"Investigating surface gravity and height variations due to glacial isostatic adjustment: A comparative study using GRACE, GRACE-FO and absolute gravity measurements data in Canada and Fennoscandia","year":2025,"lang":"en","type":"article","venue":"Science of Remote Sensing","topic":"Geophysics and Gravity Measurements","field":"Earth and Planetary Sciences","cited_by":0,"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 New Brunswick; Natural Resources Canada","funders":"","keywords":"Post-glacial rebound; Gravimetry; Surface gravity; Gravitational field; Gravimeter; Free-air gravity anomaly; Gravity of Earth","score_opus":0.06753150029431672,"score_gpt":0.2888198754606081,"score_spread":0.2212883751662914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414282169","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":[0.9983784,0.00024889826,0.0002802432,0.00010669612,0.00024752563,0.0005848379,0.00007986439,0.0000053570584,0.00006819604],"genre_scores_gemma":[0.98442405,0.0000075737166,0.0154768415,0.000064355416,0.000008537129,3.2297546e-8,0.0000121750745,0.0000023677462,0.0000040744267],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99771845,0.0001899939,0.00034979277,0.00063974684,0.00070939434,0.00039260334],"domain_scores_gemma":[0.9989911,0.00011404416,0.00016838285,0.00033495127,0.00016610813,0.00022546256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001645201,0.00019141042,0.00037127826,0.00018729009,0.00058710173,0.00014681226,0.00022968066,0.000023807434,0.0000013069013],"category_scores_gemma":[0.0002165576,0.0001841656,0.000009476041,0.0012460313,0.00031344348,0.00043850456,0.00017413539,0.00014828892,3.4391635e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022616594,0.00003193369,0.9373636,0.00006098557,0.000037758986,0.000010484817,0.003506603,0.0015180709,0.0054141623,0.000013687108,0.000010794146,0.052009277],"study_design_scores_gemma":[0.000365245,0.000042045038,0.839563,0.00015094526,0.00003685297,0.000005322663,0.0014018279,0.15667328,0.00017975473,0.0014290205,0.0000026520431,0.00015003969],"about_ca_topic_score_codex":0.89010346,"about_ca_topic_score_gemma":0.9661704,"teacher_disagreement_score":0.15515521,"about_ca_system_score_codex":0.00006825112,"about_ca_system_score_gemma":0.0010179137,"threshold_uncertainty_score":0.7510056},"labels":[],"label_agreement":null},{"id":"W4414640070","doi":"10.1016/j.srs.2026.100433","title":"Synergetic inversion of leaf chlorophyll content and leaf area index from Sentinel-2 data using artificial neural networks trained with a radiative transfer model","year":2025,"lang":"en","type":"article","venue":"Science of Remote Sensing","topic":"Leaf Properties and Growth Measurement","field":"Agricultural and Biological Sciences","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":"University of Toronto","funders":"National Key Research and Development Program of China","keywords":"Leaf area index; Radiative transfer; Atmospheric radiative transfer codes; Inversion (geology); Shortwave; Vegetation (pathology); Vegetation Index; Artificial neural network","score_opus":0.09763693833928982,"score_gpt":0.23847410154264181,"score_spread":0.140837163203352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414640070","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":[0.98523545,0.00012487125,0.013595972,0.00071560184,0.000079777274,0.00017360313,0.000013246939,0.00001151547,0.000049953713],"genre_scores_gemma":[0.998478,0.000011903341,0.0013385304,0.000117219126,0.000035186193,2.9633947e-8,0.000009705739,0.0000010916718,0.000008358057],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856275,0.000047722442,0.00027600865,0.0004562955,0.00038536705,0.00027187454],"domain_scores_gemma":[0.9994404,0.000050100258,0.00008972232,0.0001466,0.00019817894,0.000075047654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004202417,0.00013974836,0.0002597032,0.00003850655,0.0002966988,0.00006850728,0.000274626,0.00005301883,0.0000029472435],"category_scores_gemma":[0.000061548846,0.000057512836,0.00003796862,0.0005868512,0.0007783717,0.00025883375,0.0001403751,0.000109601104,6.221532e-8],"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":[0.0002288795,0.000021256574,0.00049234263,0.000012017256,0.000015286663,0.0000023264065,0.00028761238,0.017461557,0.8770909,0.000020112577,0.0000033425665,0.1043644],"study_design_scores_gemma":[0.00021636805,0.00007855621,0.0030716364,0.00016731866,0.0000370674,0.0000016412282,0.0008276998,0.96379304,0.031484857,0.00020592535,0.0000024471258,0.00011346228],"about_ca_topic_score_codex":0.0017211176,"about_ca_topic_score_gemma":0.00045354245,"teacher_disagreement_score":0.94633144,"about_ca_system_score_codex":0.000032106956,"about_ca_system_score_gemma":0.00006464095,"threshold_uncertainty_score":0.2867943},"labels":[],"label_agreement":null},{"id":"W4415219457","doi":"10.1016/j.srs.2025.100314","title":"A review of PlanetScope CubeSats for forest monitoring","year":2025,"lang":"en","type":"review","venue":"Science of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Earth observation; Satellite; Aeronomy; Scope (computer science)","score_opus":0.02836993990174569,"score_gpt":0.3255778550926634,"score_spread":0.2972079151909177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415219457","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000022014068,0.9896024,0.0007060528,0.000031640066,0.0008658427,0.0015814059,0.000018102022,0.000031924414,0.007140629],"genre_scores_gemma":[0.0000075053877,0.8841995,0.115408234,0.000029138759,0.000085856846,1.3917258e-7,0.00001361898,0.000022314158,0.0002337042],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9965589,0.000092286835,0.0010616054,0.0008454801,0.00086413533,0.0005775896],"domain_scores_gemma":[0.997413,0.0003724536,0.0010735361,0.0008724625,0.00013410486,0.00013445268],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015109199,0.0004560804,0.0018632067,0.00021888237,0.00019724766,0.000035084602,0.00091515144,0.00021491076,0.000009898344],"category_scores_gemma":[0.001341101,0.00033452702,0.0005084323,0.0023572245,0.0010019162,0.00018517923,0.00047046613,0.0002891856,0.000019122393],"study_design_candidate":"design_other","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":[0.0000013350301,0.0000055943788,8.4323716e-7,0.07559437,0.000010868456,0.0000024253754,0.00001990277,0.000021853743,0.0002023973,0.0000026970874,0.0009043148,0.9232334],"study_design_scores_gemma":[0.000070523834,0.000048372516,0.000009898668,0.48781362,0.0003763453,0.00008165923,0.000013397374,0.00047996626,0.0004191548,0.00010191845,0.51022357,0.00036155342],"about_ca_topic_score_codex":0.00021171606,"about_ca_topic_score_gemma":0.000019674248,"teacher_disagreement_score":0.9228718,"about_ca_system_score_codex":0.00036754762,"about_ca_system_score_gemma":0.00039032352,"threshold_uncertainty_score":0.99991065},"labels":[],"label_agreement":null},{"id":"W7102401962","doi":"10.1016/j.srs.2025.100319","title":"Debris covered glacier mapping using newly annotated multisource remote sensing data and geo-foundational model","year":2025,"lang":"en","type":"article","venue":"Science of Remote Sensing","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","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":"Glacier; Debris; Rock glacier; Feature (linguistics); Identification (biology)","score_opus":0.058727519916721795,"score_gpt":0.27899404447143433,"score_spread":0.22026652455471254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7102401962","genre_codex":"empirical","genre_gemma":"methods","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":[0.63161665,0.00024112257,0.36671147,0.0003300183,0.0002538844,0.00011711275,0.00003773113,0.000026825977,0.00066521036],"genre_scores_gemma":[0.45263568,0.000050791266,0.5468243,0.00027054772,0.000036509882,6.113876e-10,0.000049650534,0.0000035075475,0.00012901405],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99808335,0.000037502236,0.00037443882,0.0006128361,0.00046192284,0.0004299385],"domain_scores_gemma":[0.99874234,0.00018912076,0.00018640692,0.00049636455,0.0002854188,0.00010034584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082505296,0.00016895347,0.0002506017,0.00016154691,0.0010986855,0.00019397575,0.0003113135,0.00005291713,0.000010283828],"category_scores_gemma":[0.00058424217,0.0001630264,0.00003260314,0.0015655516,0.00075079995,0.0005928491,0.00021108339,0.00013596007,0.0000032414985],"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":[0.000019134759,0.0000023223547,0.0018349208,0.000028392173,0.000022578726,0.000005902103,0.00062948826,0.08946545,0.009965625,0.000010039975,0.00007189737,0.8979443],"study_design_scores_gemma":[0.000236935,0.000008125773,0.024446724,0.00016771063,0.00002430924,0.00002606284,0.0010365873,0.97179675,0.00015655353,0.0010260817,0.0009048801,0.00016925331],"about_ca_topic_score_codex":0.008795877,"about_ca_topic_score_gemma":0.001563522,"teacher_disagreement_score":0.897775,"about_ca_system_score_codex":0.000029350243,"about_ca_system_score_gemma":0.00042214064,"threshold_uncertainty_score":0.99780464},"labels":[],"label_agreement":null},{"id":"W7111351982","doi":"10.1016/j.srs.2025.100352","title":"Weed classification in sugarcane fields in Northeast Thailand from multi-temporal Sentinel-1 and Sentinel-2 data together with random forest algorithm","year":2025,"lang":"en","type":"article","venue":"Science of Remote Sensing","topic":"Remote Sensing in Agriculture","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":"North York General Hospital","funders":"Mahasarakham University","keywords":"Random forest; Weed; Vegetation (pathology); Field (mathematics)","score_opus":0.017681399646276838,"score_gpt":0.25338910034707096,"score_spread":0.2357077007007941,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7111351982","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":[0.9751352,0.000032776803,0.022140875,0.00072085264,0.00012533495,0.0003427992,0.0000054314073,0.000018577228,0.001478157],"genre_scores_gemma":[0.888987,0.00001004629,0.11069763,0.000071183495,0.000023507033,5.077231e-8,0.000027607784,0.0000074440054,0.00017554767],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980166,0.00008446283,0.00034949675,0.0007557052,0.00044335576,0.00035037217],"domain_scores_gemma":[0.99900943,0.000090927104,0.00015975253,0.00061998534,0.000047286318,0.000072618575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008220725,0.00018285138,0.00027858434,0.00016453912,0.00011548217,0.00008185848,0.0003788114,0.000095728145,0.000007693253],"category_scores_gemma":[0.00020751893,0.0001367846,0.00002000696,0.0012756565,0.00083986856,0.00039653477,0.00037911368,0.00023605533,0.000004816786],"study_design_candidate":"observational","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":[0.00014861238,0.00008769149,0.62433153,0.000024635327,0.000013179336,0.00006157442,0.0011460844,0.0013574011,0.10063852,0.00000311059,0.00017934364,0.2720083],"study_design_scores_gemma":[0.0010955281,0.00000644875,0.48680744,0.00016847045,0.000007688208,0.000008003004,0.0003585544,0.5104393,0.00080833567,0.00009147878,0.000092737326,0.0001160362],"about_ca_topic_score_codex":0.01492599,"about_ca_topic_score_gemma":0.03253161,"teacher_disagreement_score":0.5090819,"about_ca_system_score_codex":0.00009775757,"about_ca_system_score_gemma":0.00006145795,"threshold_uncertainty_score":0.9916337},"labels":[],"label_agreement":null}]}