{"meta":{"query_hash":"9b1bad24d45f","filters":{"venue":"Remote Sensing of Environment"},"cohort_total":399,"direct_labels_cover":0,"predictions_cover":399,"exported":399,"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/9b1bad24d45f","api":"https://metacan.xera.ac/api/v1/cohort?venue=Remote+Sensing+of+Environment"},"results":[{"id":"W1822483748","doi":"10.1016/j.rse.2015.08.016","title":"The fourth phase of the radiative transfer model intercomparison (RAMI) exercise: Actual canopy scenarios and conformity testing","year":2015,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":257,"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","funders":"Joint Research Centre; Sight Research UK; Haridus- ja Teadusministeerium; Eesti Teadusfondi; Natural Environment Research Council; European Commission","keywords":"Environmental science; Canopy; Computer science; Radiative transfer; Remote sensing; Tree canopy; Scale (ratio); Variance (accounting); Conformity; Meteorology; Simulation; Geography; Accounting; Physics; Optics","score_opus":0.03553795743916207,"score_gpt":0.24625073561878197,"score_spread":0.2107127781796199,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1822483748","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63556,0.00014965693,0.33629966,0.0008481278,0.00016998475,0.004488948,0.0024291114,0.002972566,0.017081952],"genre_scores_gemma":[0.73968595,0.000058846377,0.25223094,0.00019902954,0.000039520928,0.0024080889,0.0033218048,0.000539938,0.0015159698],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9859847,0.007871807,0.0007022299,0.0014089345,0.0029934063,0.0010389683],"domain_scores_gemma":[0.9692955,0.013923811,0.002250364,0.00823197,0.005438273,0.0008601115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03353248,0.001349852,0.0013386989,0.0014471876,0.001549326,0.002573535,0.0038085098,0.0023723058,0.0024360686],"category_scores_gemma":[0.03797561,0.0006588425,0.0020890646,0.0009475075,0.0011070506,0.002535222,0.0030029977,0.0029322752,0.0006358478],"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":[0.0037768653,0.004540705,0.043588504,0.00051445246,0.0005044456,0.00065109966,0.0034682006,0.6671726,0.06331262,0.016995257,0.010957506,0.18451776],"study_design_scores_gemma":[0.0012015067,0.0104758665,0.029039355,0.0003220126,0.00018356163,0.0003482175,0.0025369737,0.7657967,0.15061441,0.016551847,0.022515716,0.00041389294],"about_ca_topic_score_codex":0.0028453101,"about_ca_topic_score_gemma":0.0021871752,"teacher_disagreement_score":0.03353248,"about_ca_system_score_codex":0.002329424,"about_ca_system_score_gemma":0.0031897936,"threshold_uncertainty_score":0.17733884},"labels":[],"label_agreement":null},{"id":"W1856111382","doi":"10.1016/j.rse.2015.09.013","title":"Disaggregation of SMOS soil moisture over the Canadian Prairies","year":2015,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Environment and Climate Change Canada; Université de Sherbrooke","funders":"Agriculture and Agri-Food Canada; National Aeronautics and Space Administration; U.S. Department of Agriculture; Environment Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Water content; Environmental science; Soil science; Brightness temperature; Remote sensing; Biosphere; Satellite; Soil water; Moisture; Brightness; Atmospheric sciences; Hydrology (agriculture); Meteorology; Geology; Geography","score_opus":0.014706142617083232,"score_gpt":0.21396114617557108,"score_spread":0.19925500355848785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1856111382","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97864324,0.0002541573,0.0008972692,0.00021980445,0.000019644782,0.000034232202,0.015424302,0.00022852472,0.0042788666],"genre_scores_gemma":[0.9884785,0.00012740238,0.0016159889,0.00006105469,0.000009483407,0.000014777575,0.007873444,0.000028730441,0.0017906396],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99970716,0.000011551872,0.000010068902,0.00008491105,0.00008552497,0.00010074132],"domain_scores_gemma":[0.9996356,0.000023095206,0.000030316558,0.000043984688,0.00021931795,0.000047816513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025822542,0.00034207111,0.00039299804,0.0009672186,0.0007687682,0.00071760255,0.0006432986,0.00037712112,0.0018780751],"category_scores_gemma":[0.0005676243,0.00027818396,0.0004355498,0.0020890755,0.0002937798,0.00035002106,0.00042372942,0.0003963907,0.0002694175],"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":[0.0005438535,0.00022494137,0.76213664,0.00024710398,0.00095836003,0.00026244693,0.001464363,0.07328628,0.03770829,0.001753449,0.020672213,0.10074196],"study_design_scores_gemma":[0.000016993225,0.0000067328797,0.966618,0.000011385886,0.000036676433,0.0000144663045,0.00026588928,0.026682837,0.0007152543,0.000110163004,0.005489293,0.000032353313],"about_ca_topic_score_codex":0.9807475,"about_ca_topic_score_gemma":0.9918468,"teacher_disagreement_score":0.01925248,"about_ca_system_score_codex":0.006401094,"about_ca_system_score_gemma":0.0055462606,"threshold_uncertainty_score":0.046443403},"labels":[],"label_agreement":null},{"id":"W1894641570","doi":"10.1016/j.rse.2015.10.029","title":"Assessing fruit-tree crop classification from Landsat-8 time series for the Maipo Valley, Chile","year":2015,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":127,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Compute Canada","keywords":"Normalized Difference Vegetation Index; Remote sensing; Linear discriminant analysis; Random forest; Feature (linguistics); Vegetation (pathology); Support vector machine; Contextual image classification; Feature vector; Spectral bands; Time series; Pattern recognition (psychology); Mathematics; Computer science; Artificial intelligence; Statistics; Geography; Climate change; Image (mathematics); Geology","score_opus":0.03064505576207239,"score_gpt":0.24122566027919548,"score_spread":0.2105806045171231,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1894641570","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9979095,0.00005983748,0.00020513413,0.00006421364,0.0000024470949,0.000014411414,0.00081447995,0.00002120546,0.0009087731],"genre_scores_gemma":[0.99706286,0.000060614926,0.00055215106,0.000013814879,0.0000037040882,0.000023338735,0.0016416772,0.000007066751,0.0006347836],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99984026,0.000036959984,0.000012019629,0.000042584074,0.000030251163,0.000037857037],"domain_scores_gemma":[0.99940526,0.00019194277,0.0000855037,0.000043387157,0.00018950948,0.00008435826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006294493,0.00037558516,0.00024672376,0.001042673,0.00037659172,0.00088184304,0.00052397564,0.0004292463,0.00091024814],"category_scores_gemma":[0.0017151885,0.00015894312,0.0003350082,0.0008563057,0.00027086417,0.0006169472,0.0004579744,0.00024483012,0.00023965008],"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":[0.00036821672,0.00030095462,0.9360336,0.00017241116,0.00022221857,0.00042795268,0.0009327955,0.014647955,0.008803055,0.00042825603,0.002100504,0.03556206],"study_design_scores_gemma":[0.00004091405,0.000050149025,0.9652845,0.0000387556,0.00005682466,0.000041878306,0.0025346118,0.02960266,0.00095541513,0.00012249347,0.0012522469,0.000019521076],"about_ca_topic_score_codex":0.15889633,"about_ca_topic_score_gemma":0.17633915,"teacher_disagreement_score":0.15889633,"about_ca_system_score_codex":0.0016203006,"about_ca_system_score_gemma":0.0013037794,"threshold_uncertainty_score":0.31594288},"labels":[],"label_agreement":null},{"id":"W1900960255","doi":"10.1016/j.rse.2015.08.017","title":"Assimilation of SMOS soil moisture over the Great Lakes basin","year":2015,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Environmental science; Water content; Data assimilation; Ensemble Kalman filter; Assimilation (phonology); Soil science; Remote sensing; Atmospheric sciences; Meteorology; Geology; Mathematics; Kalman filter; Geography","score_opus":0.016679744589018126,"score_gpt":0.2192503276569634,"score_spread":0.20257058306794526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1900960255","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9982332,0.00003508016,0.00036283064,0.00010601113,0.000025230956,0.0000058209507,0.00041912583,0.00015319667,0.0006595045],"genre_scores_gemma":[0.9983157,0.00002388162,0.00069007906,0.000021415819,0.000014016925,0.000005174648,0.000537458,0.000012304089,0.00038004408],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99990034,0.000016602151,0.000006513586,0.00003311205,0.000023766273,0.000019677993],"domain_scores_gemma":[0.9998419,0.000020682051,0.000022545913,0.00003497692,0.000053095624,0.000026804182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030152252,0.00039064555,0.00040046292,0.00027353177,0.00042807477,0.00034945828,0.0004883701,0.0006060113,0.00074079854],"category_scores_gemma":[0.00054297416,0.00032831213,0.00043764972,0.0003794175,0.00031026837,0.0004523193,0.00036820138,0.00033115168,0.00018438871],"study_design_candidate":"observational","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":[0.0014650937,0.00085692055,0.20675904,0.00021347128,0.0008609261,0.00054906396,0.00093206496,0.60991,0.09473301,0.0017061528,0.009744878,0.07226947],"study_design_scores_gemma":[0.00020138112,0.000079744495,0.2749938,0.000012543314,0.00007393604,0.000028636376,0.000120511235,0.71739507,0.0052902987,0.00026314743,0.0014907698,0.00005019125],"about_ca_topic_score_codex":0.124558225,"about_ca_topic_score_gemma":0.16111545,"teacher_disagreement_score":0.8754418,"about_ca_system_score_codex":0.00072258565,"about_ca_system_score_gemma":0.0010296705,"threshold_uncertainty_score":0.24766642},"labels":[],"label_agreement":null},{"id":"W1956607329","doi":"10.1016/j.rse.2015.09.015","title":"Boreal Shield forest disturbance and recovery trends using Landsat time series","year":2015,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":87,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Geological Survey","keywords":"Disturbance (geology); Taiga; Boreal; Environmental science; Vegetation (pathology); Remote sensing; Physical geography; Geology; Forestry; Geography; Geomorphology","score_opus":0.011008110613963161,"score_gpt":0.1997226507815567,"score_spread":0.18871454016759354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1956607329","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99714226,0.00004847763,0.00011819309,0.0000227144,0.000005724748,0.0000047847843,0.0021961469,0.000015712483,0.0004459491],"genre_scores_gemma":[0.9960382,0.00003713059,0.00034195735,0.000008319839,0.000007880785,0.000005389791,0.0032824492,0.0000051194174,0.00027354932],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99977463,0.000038025875,0.00003135297,0.000074519456,0.000046496534,0.000035051664],"domain_scores_gemma":[0.99904054,0.00016429313,0.00039999894,0.000096138174,0.00016449633,0.00013452818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059341174,0.00026214632,0.00013772369,0.0013782352,0.00029427878,0.000548329,0.00028840703,0.00020334883,0.000882526],"category_scores_gemma":[0.0012068138,0.00013895759,0.00029037506,0.0012463375,0.00014832518,0.00064505875,0.00023282175,0.000232499,0.00015686864],"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":[0.000097240496,0.00005380542,0.9947107,0.000006690853,0.0000580814,0.000034853674,0.00007769646,0.00053484726,0.0004391347,0.000029580946,0.00039404497,0.0035633945],"study_design_scores_gemma":[0.0000024993067,0.000013688434,0.9982211,0.000001926495,0.000015445752,0.000029230772,0.000091549846,0.0012862748,0.00007546567,0.000010449215,0.00024972463,0.0000026002538],"about_ca_topic_score_codex":0.07715091,"about_ca_topic_score_gemma":0.18780151,"teacher_disagreement_score":0.07715091,"about_ca_system_score_codex":0.00044866424,"about_ca_system_score_gemma":0.0002994113,"threshold_uncertainty_score":0.1534037},"labels":[],"label_agreement":null},{"id":"W1965679259","doi":"10.1016/j.rse.2011.02.029","title":"Use of MODIS-derived surface reflectance data in the ORAC-AATSR aerosol retrieval algorithm: Impact of differences between sensor spectral response functions","year":2011,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Environment Research Council; Sight Research UK; Université de Sherbrooke; U.S. Geological Survey; National Aeronautics and Space Administration","keywords":"Aerosol; AERONET; Remote sensing; Radiometer; Albedo (alchemy); Environmental science; Moderate-resolution imaging spectroradiometer; Singular value decomposition; Brightness; Spectroradiometer; Computer science; Algorithm; Meteorology; Physics; Optics; Reflectivity; Satellite; Geology","score_opus":0.09207176403982582,"score_gpt":0.2790748552418719,"score_spread":0.1870030912020461,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965679259","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9609549,0.00076275464,0.03330693,0.00024734813,0.00014413592,0.000067752124,0.0004950353,0.00058026373,0.003440873],"genre_scores_gemma":[0.9263197,0.0002843033,0.071361974,0.00011923718,0.000020212996,0.000028766734,0.0006996389,0.00025895715,0.0009071095],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991985,0.00025267655,0.00009528299,0.00016154743,0.00022793833,0.00006399762],"domain_scores_gemma":[0.9984151,0.00071911106,0.00008220738,0.00020364614,0.00054597325,0.000033965465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002704958,0.00040493114,0.0005197547,0.0003889385,0.00044106808,0.0011974678,0.000715719,0.00076007715,0.0007303022],"category_scores_gemma":[0.0065132394,0.0002811331,0.00052124466,0.0005774832,0.00015756917,0.0012635648,0.00043481676,0.00047978063,0.00038819876],"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":[0.0046083764,0.0012893309,0.112254195,0.0005301728,0.0010604487,0.00028843954,0.00035291284,0.25087675,0.18451716,0.0021009746,0.0033252137,0.43879604],"study_design_scores_gemma":[0.00028733123,0.00036874152,0.03772775,0.00004041231,0.0003766797,0.00014631171,0.00009698136,0.8853816,0.07243396,0.00045343523,0.0026102357,0.00007665926],"about_ca_topic_score_codex":0.009789736,"about_ca_topic_score_gemma":0.016819064,"teacher_disagreement_score":0.009789736,"about_ca_system_score_codex":0.00038432927,"about_ca_system_score_gemma":0.0008302863,"threshold_uncertainty_score":0.019465506},"labels":[],"label_agreement":null},{"id":"W1965920920","doi":"10.1016/j.rse.2013.08.051","title":"Large-scale habitat mapping of the Brazilian Pantanal wetland: A synthetic aperture radar approach","year":2014,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Japan Aerospace Exploration Agency; Natural Sciences and Engineering Research Council of Canada; Agência Nacional de Águas; National Geographic Society","keywords":"Remote sensing; Wetland; Habitat; Land cover; Environmental science; Riparian forest; Synthetic aperture radar; Riparian zone; Ecology; Hydrology (agriculture); Geography; Land use; Geology","score_opus":0.00503486667935534,"score_gpt":0.17221857230456622,"score_spread":0.16718370562521087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965920920","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9696389,0.00063152995,0.023734407,0.00020493098,0.000012740817,0.000037182937,0.00040017744,0.00018318147,0.0051569],"genre_scores_gemma":[0.98808426,0.00017982382,0.01086102,0.000013373666,0.0000054969855,0.000009608543,0.00020121285,0.000014629887,0.0006304923],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99993956,0.0000113801525,0.0000020060206,0.00001700705,0.000012948292,0.000017045762],"domain_scores_gemma":[0.9999393,0.000015239509,0.000009635933,0.000008610873,0.000020178599,0.00000707014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011204504,0.00027362723,0.00017502332,0.0010255362,0.0002087911,0.0003920569,0.0002781975,0.00018345052,0.00069084135],"category_scores_gemma":[0.00026268,0.00019400197,0.00021742271,0.000683775,0.00015489329,0.0002349942,0.00035076766,0.000085735766,0.00014438812],"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":[0.00024899488,0.00027986275,0.1952263,0.0004627395,0.00025067484,0.0019141708,0.001332879,0.10438985,0.16845758,0.0038431094,0.0029505158,0.52064335],"study_design_scores_gemma":[0.000037898903,0.00010382373,0.58138907,0.00005403679,0.0001550301,0.0007816582,0.0016135622,0.40183234,0.0050903535,0.001851854,0.0070410157,0.000049446695],"about_ca_topic_score_codex":0.024957066,"about_ca_topic_score_gemma":0.0662477,"teacher_disagreement_score":0.024957066,"about_ca_system_score_codex":0.00020697195,"about_ca_system_score_gemma":0.00045824453,"threshold_uncertainty_score":0.04962361},"labels":[],"label_agreement":null},{"id":"W1966854945","doi":"10.1016/j.rse.2015.04.007","title":"A linear physically-based model for remote sensing of soil moisture using short wave infrared bands","year":2015,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":302,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Utah Agricultural Experiment Station; Utah State University; Restaurants Canada","keywords":"Remote sensing; Water content; Environmental science; Radiative transfer; Radiometry; Soil water; Satellite; Optics; Soil science; Geology; Physics","score_opus":0.04320525612107224,"score_gpt":0.254771075888143,"score_spread":0.21156581976707078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966854945","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06960917,0.00039104468,0.91838485,0.0004200301,0.00016875738,0.000106227075,0.0008776515,0.0020290518,0.008013182],"genre_scores_gemma":[0.87584865,0.0005513513,0.10166093,0.0002311337,0.00012530418,0.00036750457,0.0011274923,0.0002470332,0.019840632],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998202,0.0000336247,0.000009920341,0.000063412444,0.00005245528,0.000020517546],"domain_scores_gemma":[0.9998072,0.000077263736,0.000022577618,0.000014453203,0.000066630346,0.000012029103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029251876,0.0004733864,0.00060714094,0.00029006074,0.00049634004,0.0008117727,0.0018735495,0.0010844821,0.0032153057],"category_scores_gemma":[0.0006689079,0.0004962835,0.0006325868,0.00052599557,0.00038941263,0.0012268036,0.0005604161,0.00096942304,0.0010426309],"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":[0.000031371164,0.00006378915,0.0007842358,0.000054078733,0.00003296706,0.000041296913,0.000032934502,0.97971785,0.0031418996,0.0015301076,0.00063417596,0.013935303],"study_design_scores_gemma":[0.0000057787947,0.000008634192,0.00021483126,0.0000017459579,0.000005350723,0.000005667385,0.0000029181822,0.99896884,0.00022099579,0.00027295845,0.0002872425,0.000005056324],"about_ca_topic_score_codex":0.025160514,"about_ca_topic_score_gemma":0.020629637,"teacher_disagreement_score":0.025160514,"about_ca_system_score_codex":0.0007284435,"about_ca_system_score_gemma":0.0009873849,"threshold_uncertainty_score":0.050028145},"labels":[],"label_agreement":null},{"id":"W1969320838","doi":"10.1016/j.rse.2012.03.020","title":"A decadal investigation of supraglacial lakes in West Greenland using a fully automatic detection and tracking algorithm","year":2012,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":152,"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":"Natural Sciences and Engineering Research Council of Canada; Cooperative Institute for Research in Environmental Sciences; National Science Foundation","keywords":"Greenland ice sheet; Geology; Meltwater; Ice sheet; Glacier; Drainage; Population; Context (archaeology); Drainage basin; Physical geography; Geomorphology; Geography; Cartography; Paleontology; Ecology","score_opus":0.024533391443427897,"score_gpt":0.21109354987257478,"score_spread":0.1865601584291469,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969320838","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99513185,0.000053610216,0.003980736,0.000022130564,0.0000028058098,0.000012870035,0.0003768721,0.0001226806,0.00029641422],"genre_scores_gemma":[0.9892179,0.000026622147,0.009447706,0.000008316795,0.000002528425,0.000009217616,0.0010622112,0.000009621675,0.00021589424],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99991286,0.000010346017,0.0000063991324,0.000036862588,0.000019061821,0.000014378234],"domain_scores_gemma":[0.99981993,0.000027584472,0.00003968453,0.000024673183,0.000070216345,0.000017901983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002905509,0.000146251,0.00013330912,0.00065934315,0.00017643684,0.00028388796,0.00021439372,0.0001480673,0.00022753181],"category_scores_gemma":[0.000523936,0.00008720488,0.000120550976,0.0004832674,0.00008060333,0.0002206844,0.00019220532,0.00008763335,0.000054835284],"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":[0.00019014596,0.0001754907,0.714056,0.000049411043,0.00016294258,0.00020642983,0.00027949805,0.04356332,0.04171722,0.0005098413,0.0012726152,0.19781715],"study_design_scores_gemma":[0.00001113597,0.000042436794,0.7882138,0.000004940869,0.000027391128,0.000052626416,0.00006048477,0.20766833,0.002924568,0.00011264288,0.00087207364,0.000009483089],"about_ca_topic_score_codex":0.028361136,"about_ca_topic_score_gemma":0.05852898,"teacher_disagreement_score":0.028361136,"about_ca_system_score_codex":0.00030964485,"about_ca_system_score_gemma":0.00042588337,"threshold_uncertainty_score":0.056392133},"labels":[],"label_agreement":null},{"id":"W1970468125","doi":"10.1016/j.rse.2009.12.010","title":"Comparison and evaluation of Medium Resolution Imaging Spectrometer leaf area index products across a range of land use","year":2010,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Resources Canada; Canadian Space Agency; European Space Agency","keywords":"Remote sensing; Imaging spectrometer; Environmental science; Index (typography); Spectrometer; Leaf area index; Resolution (logic); Range (aeronautics); Optics; Computer science; Geography; Materials science; Agronomy; Physics; Artificial intelligence","score_opus":0.026975628910146942,"score_gpt":0.27114418339115465,"score_spread":0.24416855448100772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970468125","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98938876,0.00025587762,0.0071220966,0.000040448555,0.000010896084,0.00004818392,0.0014745236,0.00018330714,0.0014759706],"genre_scores_gemma":[0.9683717,0.0002672304,0.025904402,0.00003555916,0.00001254587,0.000047055066,0.0045942315,0.00008712306,0.0006801328],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99956256,0.0001043949,0.000028744269,0.00008882165,0.0001882484,0.000027273583],"domain_scores_gemma":[0.9979274,0.0008473952,0.00016866163,0.000167071,0.00082829717,0.00006112308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020546596,0.00039039762,0.00028349794,0.0013422979,0.00027463923,0.00071879005,0.00045241482,0.00039022294,0.00079731777],"category_scores_gemma":[0.0031102826,0.00016335362,0.00044221047,0.0012706486,0.00019777457,0.00110678,0.00033508008,0.00021951551,0.00025728618],"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":[0.007016079,0.001897197,0.3349367,0.0006835331,0.0006634128,0.00028231635,0.00070925406,0.043895543,0.19189142,0.0012304211,0.0024420249,0.41435203],"study_design_scores_gemma":[0.00023348132,0.0014938338,0.7987629,0.00003815533,0.0004679401,0.00042812465,0.0004768459,0.12371235,0.06968736,0.00045553973,0.0041548642,0.00008861673],"about_ca_topic_score_codex":0.0066115144,"about_ca_topic_score_gemma":0.010646689,"teacher_disagreement_score":0.0066115144,"about_ca_system_score_codex":0.0003708314,"about_ca_system_score_gemma":0.00023780412,"threshold_uncertainty_score":0.013146102},"labels":[],"label_agreement":null},{"id":"W1970787495","doi":"10.1016/j.rse.2008.08.009","title":"Scaling and assessment of GPP from MODIS using a combination of airborne lidar and eddy covariance measurements over jack pine forests","year":2008,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Queen's University","funders":"Oak Ridge National Laboratory; Natural Sciences and Engineering Research Council of Canada; Science and Technology Directorate","keywords":"Eddy covariance; Lidar; Environmental science; Remote sensing; Moderate-resolution imaging spectroradiometer; Canopy; Chronosequence; Atmospheric sciences; Geography; Geology; Ecosystem; Satellite; Soil science","score_opus":0.027026773295835645,"score_gpt":0.24365425076253294,"score_spread":0.2166274774666973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970787495","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9981439,0.000077494355,0.0011637786,0.000025659961,0.000006556954,0.000008882632,0.00015614912,0.00006179078,0.00035572588],"genre_scores_gemma":[0.99788123,0.000033250948,0.0018441295,0.0000051594093,0.00000465793,0.0000057003076,0.00016958639,0.000011168029,0.000045107277],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999782,0.000042649666,0.000017587921,0.00006384291,0.00006802767,0.000025831352],"domain_scores_gemma":[0.99915516,0.0002440316,0.000099073106,0.00012348534,0.00031236955,0.00006578151],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010726772,0.0004459354,0.00038170305,0.00051156257,0.000350229,0.0007144058,0.00046935305,0.0003742383,0.00020947844],"category_scores_gemma":[0.0020603724,0.0003422812,0.000410415,0.00064648077,0.00024960897,0.00085265964,0.0003085036,0.000348698,0.000110301626],"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":[0.0020311032,0.0011234374,0.53078544,0.00024744397,0.00056787435,0.0005264506,0.0005722186,0.18999705,0.1232839,0.0009555366,0.0015388348,0.1483707],"study_design_scores_gemma":[0.00010767083,0.00015947947,0.5807947,0.000013554255,0.00013958938,0.00008514946,0.00014097765,0.41203797,0.0057371533,0.00028545022,0.00045089578,0.000047391],"about_ca_topic_score_codex":0.043165646,"about_ca_topic_score_gemma":0.039550398,"teacher_disagreement_score":0.043165646,"about_ca_system_score_codex":0.00052105513,"about_ca_system_score_gemma":0.0004289698,"threshold_uncertainty_score":0.08582878},"labels":[],"label_agreement":null},{"id":"W1971057461","doi":"10.1016/j.rse.2010.11.009","title":"Submarine groundwater discharge in tidal flats revealed by space-borne synthetic aperture radar","year":2010,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Remote sensing; Synthetic aperture radar; Submarine groundwater discharge; Geology; Groundwater; Submarine; Radar; Environmental science; Oceanography; Aquifer; Geotechnical engineering; Computer science; Telecommunications","score_opus":0.0042576402397627765,"score_gpt":0.18879710252477253,"score_spread":0.18453946228500975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971057461","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9994324,0.000020851588,0.00012882058,0.000012459699,0.0000021573574,0.0000010231339,0.00006404227,0.000008926269,0.000329352],"genre_scores_gemma":[0.9997396,0.000015935748,0.00008255897,0.0000042324223,0.0000023864204,5.669694e-7,0.000057511745,0.0000013582975,0.00009597406],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999628,0.000004586533,0.000001991538,0.000010280615,0.0000090310195,0.000011186651],"domain_scores_gemma":[0.9999311,0.000013564198,0.000013580014,0.000007347439,0.000015956672,0.00001839464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000064122774,0.00013059958,0.00020054205,0.0003621919,0.0001817514,0.0002758795,0.00014470775,0.0002233035,0.00036994935],"category_scores_gemma":[0.00018689924,0.00011382851,0.00009557874,0.00041347454,0.00022778338,0.00020473615,0.0002199991,0.0001361064,0.00008712383],"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":[0.0009768805,0.00021130251,0.58802307,0.00007124856,0.00012132046,0.0016350936,0.0013189139,0.011040294,0.35609713,0.0005116875,0.0010054535,0.038987603],"study_design_scores_gemma":[0.00002297588,0.0000816602,0.9799207,0.000003962872,0.000025497795,0.00016963575,0.00038125023,0.01593303,0.0029547892,0.00012312425,0.00037102928,0.000012364664],"about_ca_topic_score_codex":0.009150692,"about_ca_topic_score_gemma":0.011863994,"teacher_disagreement_score":0.009150692,"about_ca_system_score_codex":0.00016239214,"about_ca_system_score_gemma":0.00019096205,"threshold_uncertainty_score":0.018194854},"labels":[],"label_agreement":null},{"id":"W1971612442","doi":"10.1016/s0034-4257(01)00280-2","title":"Estimating forest structure in wetlands using multitemporal SAR","year":2002,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":109,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Space Agency","keywords":"Thematic Mapper; Remote sensing; Environmental science; Synthetic aperture radar; Normalized Difference Vegetation Index; Wetland; Basal area; Canopy; Vegetation (pathology); Tree canopy; Floodplain; Satellite imagery; Leaf area index; Hydrology (agriculture); Geology; Geography; Cartography; Forestry; Ecology","score_opus":0.01694000679285365,"score_gpt":0.21960415889588752,"score_spread":0.20266415210303387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971612442","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9828892,0.00007718434,0.016490446,0.000025944235,0.000003497562,0.0000076980605,0.00007973109,0.00006816822,0.00035811446],"genre_scores_gemma":[0.9794611,0.00005981874,0.020080231,0.0000059891486,0.000006228834,0.0000047269928,0.00013858291,0.000009437524,0.00023402477],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99995565,0.000008976472,0.000002398153,0.000011081187,0.000011131179,0.000010868516],"domain_scores_gemma":[0.9997563,0.0000967131,0.000041475563,0.000025082321,0.00005108792,0.000029371395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022437015,0.00024296214,0.00018430858,0.0009103596,0.00020027242,0.00031918322,0.00023320277,0.00020967108,0.00026353414],"category_scores_gemma":[0.0006240752,0.00030319646,0.00018057472,0.00035672367,0.00016206883,0.0004790323,0.00017588287,0.00015442845,0.000059506965],"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":[0.0005370621,0.00035635266,0.3058031,0.00008364638,0.00015939045,0.00039083563,0.00022821572,0.4022813,0.100473635,0.0010872947,0.00075103797,0.18784808],"study_design_scores_gemma":[0.000023686704,0.00004675757,0.16457826,0.000004788253,0.000034096236,0.00009650668,0.000077959354,0.8299056,0.004309714,0.00071408955,0.00019602774,0.000012495165],"about_ca_topic_score_codex":0.014070824,"about_ca_topic_score_gemma":0.02870582,"teacher_disagreement_score":0.014070824,"about_ca_system_score_codex":0.00019724877,"about_ca_system_score_gemma":0.00034074578,"threshold_uncertainty_score":0.027977824},"labels":[],"label_agreement":null},{"id":"W1971855597","doi":"10.1016/j.rse.2011.11.023","title":"Subalpine zone delineation using LiDAR and Landsat imagery","year":2012,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":false,"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":"Norges Miljø- og Biovitenskapelige Universitet; Norges Forskningsråd","keywords":"Lidar; Remote sensing; Montane ecology; Environmental science; Tree canopy; Vegetation (pathology); Terrain; Canopy; Geology; Geography; Cartography; Ecology","score_opus":0.016022119965723076,"score_gpt":0.2341136283073686,"score_spread":0.2180915083416455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971855597","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9669233,0.00047070644,0.015170184,0.000075475444,0.000017613478,0.00010424486,0.0022803277,0.0016537956,0.013304396],"genre_scores_gemma":[0.9753288,0.00016433411,0.020604275,0.000014434638,0.0000068542845,0.000025245305,0.0015924217,0.000061629114,0.0022020019],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99990165,0.000009681848,0.0000062954136,0.000028081025,0.000031510437,0.000022776821],"domain_scores_gemma":[0.99981636,0.000027169719,0.000042746582,0.000015462443,0.00007211013,0.000026080368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021989067,0.00029093702,0.00023837916,0.0018022115,0.0004568436,0.0008506179,0.00039843578,0.00020845613,0.0016839019],"category_scores_gemma":[0.000566479,0.00014877434,0.00019387752,0.0009214586,0.000115918454,0.0003989116,0.00032328142,0.00015520345,0.00045297018],"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":[0.0010510058,0.00034752637,0.32851014,0.00030901353,0.00012882498,0.00088369625,0.0011685217,0.033223774,0.10099175,0.0018753195,0.0050244913,0.5264859],"study_design_scores_gemma":[0.00015683679,0.00007245758,0.80645573,0.00008786713,0.00013466555,0.00027773075,0.0009697871,0.15794872,0.01622604,0.00097418175,0.016644327,0.000051612755],"about_ca_topic_score_codex":0.099783316,"about_ca_topic_score_gemma":0.22845186,"teacher_disagreement_score":0.099783316,"about_ca_system_score_codex":0.0006169607,"about_ca_system_score_gemma":0.000812458,"threshold_uncertainty_score":0.19840497},"labels":[],"label_agreement":null},{"id":"W1975158286","doi":"10.1016/j.rse.2011.08.006","title":"Predicting gross primary production from the enhanced vegetation index and photosynthetically active radiation: Evaluation and calibration","year":2011,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":140,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Oak Ridge National Laboratory","keywords":"Primary production; Photosynthetically active radiation; Environmental science; Remote sensing; Moderate-resolution imaging spectroradiometer; Enhanced vegetation index; Vegetation (pathology); Leaf area index; Biome; Calibration; Evergreen; Mean squared error; Atmospheric sciences; Ecosystem; Satellite; Normalized Difference Vegetation Index; Vegetation Index; Mathematics; Geography; Statistics; Ecology; Geology","score_opus":0.010460970955085786,"score_gpt":0.1925808320690756,"score_spread":0.18211986111398984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975158286","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96098346,0.00023358542,0.03740347,0.00004953705,0.0000147589,0.000041872292,0.00017166245,0.00040738643,0.00069430104],"genre_scores_gemma":[0.983823,0.00009598235,0.015492753,0.0000079539195,0.000006777219,0.000026617572,0.00026900592,0.000026946616,0.000250943],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9995598,0.00015948732,0.000024453155,0.00012196694,0.000102954975,0.00003132696],"domain_scores_gemma":[0.9969332,0.0023788675,0.00012167661,0.00017956932,0.00032229428,0.00006427414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002743593,0.0011331851,0.00065859576,0.00066594855,0.0003345751,0.00079348596,0.00087958446,0.00096927944,0.0004393939],"category_scores_gemma":[0.0045686527,0.0006994795,0.0006334966,0.0005242352,0.00037320706,0.0008632458,0.00040810637,0.0007880117,0.00025310618],"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":[0.0006429197,0.00039358652,0.046029273,0.00005623757,0.00021425965,0.000040364415,0.00005200907,0.8585567,0.011078879,0.00030379702,0.00026020512,0.08237187],"study_design_scores_gemma":[0.000041113854,0.00006111242,0.00860428,0.00000202064,0.0000221367,0.000009873772,0.0000064993274,0.9874373,0.0035907403,0.00016191843,0.00005276445,0.000010108502],"about_ca_topic_score_codex":0.009680213,"about_ca_topic_score_gemma":0.007858283,"teacher_disagreement_score":0.009680213,"about_ca_system_score_codex":0.00079179404,"about_ca_system_score_gemma":0.0008342217,"threshold_uncertainty_score":0.01924771},"labels":[],"label_agreement":null},{"id":"W1975678535","doi":"10.1016/j.rse.2011.03.014","title":"PHOTOSYNSAT, photosynthesis from space: Theoretical foundations of a satellite concept and validation from tower and spaceborne data","year":2011,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":62,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Luonnontieteiden ja Tekniikan Tutkimuksen Toimikunta; Natural Environment Research Council; Sight Research UK","keywords":"Photochemical Reflectance Index; Remote sensing; Shadow (psychology); Satellite; Environmental science; Vegetation (pathology); Canopy; Biome; Reflectivity; Photosynthesis; Tower; Leaf area index; Computer science; Normalized Difference Vegetation Index; Meteorology; Physics; Ecosystem; Optics; Geography; Ecology; Chemistry","score_opus":0.025589228572505215,"score_gpt":0.21914325400100587,"score_spread":0.19355402542850064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975678535","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09502315,0.0014094132,0.879727,0.0013765049,0.00018783566,0.00013799124,0.001956462,0.0017597574,0.018421814],"genre_scores_gemma":[0.7268631,0.001737062,0.2635785,0.0002529584,0.00013278914,0.00022138393,0.0031369769,0.000440191,0.0036371166],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996177,0.00009055266,0.000012793724,0.00008794495,0.00016938306,0.000021718675],"domain_scores_gemma":[0.9991534,0.00039180386,0.00008717695,0.00016912639,0.00017188054,0.000026604359],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001716489,0.000490656,0.00035425156,0.0006614295,0.00047700305,0.0011236453,0.0010258318,0.000642233,0.0012539905],"category_scores_gemma":[0.0033206488,0.00034026828,0.00029911828,0.0010639228,0.0010869747,0.00240571,0.00081963016,0.00097232853,0.00043904866],"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":[0.00016365522,0.00014496171,0.014885624,0.00034222653,0.00012192481,0.00026883514,0.00053668104,0.19117932,0.022254467,0.56410575,0.009901769,0.19609469],"study_design_scores_gemma":[0.00004967773,0.00010784744,0.011340082,0.00010871475,0.00004703456,0.00021547478,0.0002206633,0.70243764,0.016222745,0.24304995,0.026127763,0.00007244408],"about_ca_topic_score_codex":0.0063563837,"about_ca_topic_score_gemma":0.0043774815,"teacher_disagreement_score":0.0063563837,"about_ca_system_score_codex":0.0008693417,"about_ca_system_score_gemma":0.0010658756,"threshold_uncertainty_score":0.012638807},"labels":[],"label_agreement":null},{"id":"W1977251521","doi":"10.1016/j.rse.2006.10.025","title":"Coincident high resolution optical-SAR image analysis for surface albedo estimation of first-year sea ice during summer melt","year":2007,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; ArcticNet; European Space Agency","keywords":"Remote sensing; Synthetic aperture radar; Sea ice; Albedo (alchemy); Geology; Environmental science; Image resolution; Backscatter (email); Melt pond; Arctic ice pack; Sea ice thickness; Climatology; Optics; Physics; Computer science","score_opus":0.009460356271837419,"score_gpt":0.21211911437714548,"score_spread":0.20265875810530806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977251521","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98879063,0.0001345513,0.007994174,0.000035043577,0.000039189385,0.000018544304,0.0010953961,0.00015460685,0.0017379707],"genre_scores_gemma":[0.98997086,0.000053425796,0.008300148,0.000011572489,0.000022832166,0.000015001644,0.0009976167,0.000019929443,0.0006086845],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999908,0.000014195699,0.0000064437077,0.000021922096,0.000026001497,0.000023422748],"domain_scores_gemma":[0.999824,0.000027629972,0.00003220591,0.000017836122,0.00007040002,0.000027916503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023133634,0.00021071121,0.0002551735,0.0012700151,0.00040390814,0.0003687137,0.00020735257,0.00023182142,0.00072128983],"category_scores_gemma":[0.0003688863,0.00019138951,0.00022863655,0.00086831936,0.000090179376,0.00024806612,0.00019097899,0.00014878655,0.00020648469],"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":[0.0029006102,0.0007862341,0.45339552,0.0002017073,0.0001889654,0.0005219507,0.00041280373,0.028120978,0.30175894,0.00057293405,0.004779298,0.20636006],"study_design_scores_gemma":[0.00006766993,0.00015227083,0.89782435,0.000009356282,0.00009793735,0.00014532625,0.00015236439,0.081962086,0.017910836,0.00010266008,0.0015510843,0.000024012279],"about_ca_topic_score_codex":0.006680417,"about_ca_topic_score_gemma":0.017826166,"teacher_disagreement_score":0.006680417,"about_ca_system_score_codex":0.00018341046,"about_ca_system_score_gemma":0.00043621086,"threshold_uncertainty_score":0.013283074},"labels":[],"label_agreement":null},{"id":"W1977422739","doi":"10.1016/j.rse.2012.08.027","title":"Continuous observation of tree leaf area index at ecosystem scale using upward-pointing digital cameras","year":2012,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":178,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Leaf area index; Remote sensing; Environmental science; Moderate-resolution imaging spectroradiometer; Phenology; Ecosystem; Canopy; Scale (ratio); Vegetation (pathology); Forest ecology; Geography; Ecology; Satellite; Cartography","score_opus":0.016417430650583154,"score_gpt":0.2011654787397813,"score_spread":0.18474804808919815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977422739","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98238075,0.00021175187,0.012864584,0.000027958593,0.00001967112,0.000036181867,0.0013677697,0.0002184434,0.0028729266],"genre_scores_gemma":[0.9808023,0.00015483884,0.016885502,0.000038020706,0.000020066289,0.000038306007,0.00085850985,0.000024175128,0.0011782439],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99981636,0.000016253887,0.000004583702,0.000072913186,0.00006631734,0.000023669754],"domain_scores_gemma":[0.99965405,0.000070717666,0.00005712006,0.000031320204,0.0001285254,0.000058266265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019072094,0.00026517644,0.00036479137,0.0007968639,0.00026143523,0.00035147864,0.00027127692,0.00029980665,0.00082267565],"category_scores_gemma":[0.00027613816,0.00020952758,0.0001863979,0.00081698835,0.0001377602,0.0005542936,0.00033127388,0.00043954368,0.0002767747],"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":[0.00058137823,0.000250771,0.25956178,0.00015835928,0.00009821974,0.00012254294,0.00036153616,0.0025968382,0.6593986,0.00017130477,0.0012000708,0.07549861],"study_design_scores_gemma":[0.00002442335,0.00011199279,0.954219,0.000010142983,0.000059472062,0.00018569792,0.0001477479,0.015262603,0.02879539,0.00010003808,0.0010557048,0.000027702534],"about_ca_topic_score_codex":0.0054529794,"about_ca_topic_score_gemma":0.016002635,"teacher_disagreement_score":0.0054529794,"about_ca_system_score_codex":0.00026559012,"about_ca_system_score_gemma":0.00025668734,"threshold_uncertainty_score":0.010842502},"labels":[],"label_agreement":null},{"id":"W1981435276","doi":"10.1016/j.rse.2008.06.010","title":"Developing clear-sky, cloud and cloud shadow mask for producing clear-sky composites at 250-meter spatial resolution for the seven MODIS land bands over Canada and North America","year":2008,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":233,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Remote sensing; Compositing; Sky; Cloud computing; Environmental science; Moderate-resolution imaging spectroradiometer; Image resolution; Cloud cover; Shadow (psychology); Scale (ratio); Computer science; Meteorology; Geology; Geography; Cartography; Physics; Computer vision; Satellite; Image (mathematics)","score_opus":0.014971171089385931,"score_gpt":0.19136937450013036,"score_spread":0.17639820341074444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981435276","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48927158,0.00023608819,0.48229137,0.0003507435,0.00011176734,0.0005779012,0.011452434,0.008689268,0.007018942],"genre_scores_gemma":[0.24767238,0.0001437792,0.7414267,0.000058362195,0.000010136258,0.00015223157,0.008385667,0.0004257807,0.0017249276],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998241,0.000011543004,0.000011856721,0.00003661704,0.00008397798,0.000031993128],"domain_scores_gemma":[0.9994906,0.000058324502,0.000035786077,0.000038320504,0.0003368018,0.00004015977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046967017,0.00052530266,0.00023305474,0.0007144633,0.0006528467,0.00053906895,0.0007769042,0.0003755485,0.0014793165],"category_scores_gemma":[0.0013933716,0.00057904463,0.000407328,0.0007194562,0.00016286602,0.0005139286,0.00038014085,0.00040357944,0.0007111724],"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":[0.00048122977,0.00047093508,0.044745915,0.00040920117,0.00016405043,0.00046336078,0.00056650775,0.12636466,0.37579036,0.0048898826,0.020853605,0.4248003],"study_design_scores_gemma":[0.00015275356,0.0001060812,0.11846847,0.000045427103,0.00015638855,0.0001778465,0.00038778142,0.67809606,0.18455529,0.0014960938,0.01625522,0.000102587845],"about_ca_topic_score_codex":0.30816394,"about_ca_topic_score_gemma":0.46022138,"teacher_disagreement_score":0.69183606,"about_ca_system_score_codex":0.0011948461,"about_ca_system_score_gemma":0.0054764193,"threshold_uncertainty_score":0.61274046},"labels":[],"label_agreement":null},{"id":"W1981897942","doi":"10.1016/s0034-4257(03)00013-0","title":"Stand delineation and composition estimation using semi-automated individual tree crown analysis","year":2003,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":216,"is_retracted":false,"has_abstract":false,"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":"","keywords":"Remote sensing; Computer science; Classifier (UML); Multispectral image; Forest inventory; Decision tree; Transect; Contextual image classification; Tree (set theory); Forest management; Artificial intelligence; Environmental science; Forestry; Mathematics; Geography; Ecology; Image (mathematics)","score_opus":0.01523593226211799,"score_gpt":0.24797932462904212,"score_spread":0.23274339236692412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981897942","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5014231,0.00017227222,0.49241734,0.000025720889,0.000023753451,0.000119234726,0.00071562023,0.003285037,0.0018179765],"genre_scores_gemma":[0.72127336,0.000052064886,0.2766077,0.00001840444,0.000016186379,0.00007846524,0.00082498055,0.00013877921,0.000990104],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970144,0.000048100315,0.000021432053,0.000079114856,0.00011382288,0.000036096422],"domain_scores_gemma":[0.99913895,0.00025169324,0.00010241992,0.00010255939,0.0003576624,0.00004662184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006228295,0.0005134262,0.00089398294,0.0024569412,0.00050023285,0.00070106384,0.000673143,0.0005169102,0.001409483],"category_scores_gemma":[0.0009351782,0.0004165191,0.0005339053,0.0013737279,0.00018744536,0.0006096497,0.00042835908,0.00026709624,0.00068258354],"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":[0.00050462724,0.0002913706,0.06747194,0.00021448989,0.0002470398,0.00017612774,0.0003005452,0.07802701,0.16235188,0.00070278975,0.0019484961,0.6877637],"study_design_scores_gemma":[0.000033256594,0.000060151786,0.07196798,0.000010732326,0.00011196168,0.00022182179,0.000073411546,0.9062162,0.019540556,0.00076381303,0.00096366386,0.000036344816],"about_ca_topic_score_codex":0.007397067,"about_ca_topic_score_gemma":0.023315791,"teacher_disagreement_score":0.007397067,"about_ca_system_score_codex":0.0002892044,"about_ca_system_score_gemma":0.0005362774,"threshold_uncertainty_score":0.014707983},"labels":[],"label_agreement":null},{"id":"W1982982210","doi":"10.1016/j.rse.2014.04.030","title":"Time-series analysis of Landsat-MSS/TM/OLI images over Amazonian waters impacted by gold mining activities","year":2014,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":156,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Siltation; Environmental science; Sediment; Atmospheric correction; Hydrology (agriculture); Surface water; Remote sensing; Drainage basin; Water quality; Satellite; Geology; Geography; Geomorphology","score_opus":0.00467514880816964,"score_gpt":0.18432072214962777,"score_spread":0.17964557334145814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982982210","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9983851,0.000036559177,0.000101423095,0.00003439507,0.000004561387,0.0000047049557,0.0010979328,0.000020718337,0.00031472172],"genre_scores_gemma":[0.99810886,0.00004631148,0.00029802453,0.000007846099,0.000006011431,0.0000044374747,0.0013105291,0.000004474036,0.00021345378],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999362,0.0000033945353,0.0000057344414,0.000016555143,0.000019188647,0.000018947558],"domain_scores_gemma":[0.99980444,0.000025234423,0.00006105715,0.000012145565,0.000072952105,0.000024196232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012721906,0.00018370994,0.0001243798,0.0008977229,0.0002086303,0.00032611072,0.00021157466,0.00022468163,0.0007705553],"category_scores_gemma":[0.00037451918,0.00008090705,0.00015241449,0.0014001603,0.00014948905,0.00024525067,0.00014333297,0.00014535926,0.00013834548],"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":[0.0008222974,0.00047214382,0.8470666,0.00020334101,0.00019088425,0.0015566258,0.0010167682,0.009340035,0.07732695,0.0005111349,0.004299656,0.05719358],"study_design_scores_gemma":[0.00000455237,0.00001833489,0.99117506,0.0000037128757,0.000023475475,0.00008126192,0.0003091624,0.005956122,0.0016942414,0.000019674293,0.00070807873,0.0000062228023],"about_ca_topic_score_codex":0.07045692,"about_ca_topic_score_gemma":0.09458372,"teacher_disagreement_score":0.07045692,"about_ca_system_score_codex":0.00061663793,"about_ca_system_score_gemma":0.00030000307,"threshold_uncertainty_score":0.14009362},"labels":[],"label_agreement":null},{"id":"W1983992248","doi":"10.1016/j.rse.2014.04.026","title":"Predicting fine-scale tree species abundance patterns using biotic variables derived from LiDAR and high spatial resolution imagery","year":2014,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":58,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Division of Emerging Frontiers in Research and Innovation; Natural Sciences and Engineering Research Council of Canada","keywords":"Lidar; Abundance (ecology); Vegetation (pathology); Remote sensing; Environmental science; Abiotic component; Scale (ratio); Multispectral image; Tree canopy; Forest inventory; Ecology; Canopy; Physical geography; Forest management; Geography; Cartography; Biology","score_opus":0.010220209757423548,"score_gpt":0.1951315622486692,"score_spread":0.18491135249124566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983992248","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9975017,0.00003067144,0.00204348,0.000017265575,0.000003818074,0.000006639367,0.00017662438,0.000033902306,0.00018595303],"genre_scores_gemma":[0.996591,0.000025218627,0.0028272104,0.000006689572,0.0000036286349,0.0000061859632,0.000378912,0.0000037238863,0.00015739458],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99993646,0.000010098256,0.0000045811753,0.000023936458,0.000012176826,0.000012781887],"domain_scores_gemma":[0.99967206,0.00015627248,0.00005065752,0.000021544787,0.00005618303,0.00004339799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022932714,0.00028637506,0.00022118451,0.0005978642,0.00018586637,0.00035133093,0.0002609113,0.00024858228,0.0005353474],"category_scores_gemma":[0.00065910345,0.00026302156,0.0002937126,0.00050035707,0.00011807402,0.00048848696,0.00019541256,0.00023397984,0.00018917858],"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":[0.00021101371,0.0004318032,0.84820145,0.000041690226,0.00014166508,0.00011369124,0.00007549139,0.070503354,0.018665893,0.00013127166,0.0006398894,0.06084283],"study_design_scores_gemma":[0.00002249531,0.000066778965,0.57101333,0.0000043918576,0.00003266126,0.000048015714,0.00014760897,0.42681518,0.0014409573,0.00023179714,0.00016563454,0.000011270519],"about_ca_topic_score_codex":0.014297274,"about_ca_topic_score_gemma":0.037784792,"teacher_disagreement_score":0.014297274,"about_ca_system_score_codex":0.00029089765,"about_ca_system_score_gemma":0.00025589904,"threshold_uncertainty_score":0.028428137},"labels":[],"label_agreement":null},{"id":"W1984047080","doi":"10.1016/j.rse.2012.01.007","title":"Generation of a novel 1km NDVI data set over Canada, the northern United States, and Greenland based on historical AVHRR data","year":2012,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":false,"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 Forest Service; Canadian Space Agency; University of British Columbia; National Oceanic and Atmospheric Administration; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Government of Canada","keywords":"Advanced very-high-resolution radiometer; Remote sensing; Normalized Difference Vegetation Index; Environmental science; Atmospheric correction; Satellite; Data set; Radiometry; Bidirectional reflectance distribution function; Geolocation; Meteorology; Geology; Climate change; Geography; Computer science; Reflectivity","score_opus":0.0623066598958801,"score_gpt":0.2345677166670011,"score_spread":0.172261056771121,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984047080","genre_codex":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84353065,0.0002178231,0.010770682,0.00031876643,0.00012593837,0.000410256,0.13621415,0.0013841474,0.0070275944],"genre_scores_gemma":[0.6754521,0.00024754825,0.041572377,0.00011804687,0.000044759923,0.0002957043,0.27786112,0.0002391588,0.0041691763],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99970275,0.000011171142,0.000010545112,0.000070452596,0.00013141907,0.000073608426],"domain_scores_gemma":[0.9994438,0.000028922273,0.000032471235,0.000072512055,0.00034977283,0.00007244839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031276906,0.00065249053,0.00049389724,0.0018200418,0.0010946149,0.0007072988,0.00081602857,0.00051514857,0.0015991125],"category_scores_gemma":[0.00060134946,0.00042045515,0.00051508326,0.002686779,0.00033880514,0.00034612967,0.0005869827,0.00049714075,0.0006769668],"study_design_candidate":"not_applicable","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":[0.0010090049,0.0024342574,0.29802454,0.0004744949,0.00062761834,0.0032339129,0.0010400964,0.19046594,0.09275467,0.0035943794,0.08882704,0.31751403],"study_design_scores_gemma":[0.00046810883,0.00013461453,0.68147355,0.00006647044,0.00025092275,0.00052148005,0.001070522,0.23047706,0.026848685,0.00087161403,0.057631455,0.00018551807],"about_ca_topic_score_codex":0.66826105,"about_ca_topic_score_gemma":0.79471374,"teacher_disagreement_score":0.33173895,"about_ca_system_score_codex":0.0033427323,"about_ca_system_score_gemma":0.005559834,"threshold_uncertainty_score":0.6673852},"labels":[],"label_agreement":null},{"id":"W1984839778","doi":"10.1016/s0034-4257(00)00101-2","title":"Local Maximum Filtering for the Extraction of Tree Locations and Basal Area from High Spatial Resolution Imagery","year":2000,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":392,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; Natural Resources Canada; Canadian Forest Service","funders":"","keywords":"Tree (set theory); Crown (dentistry); Image resolution; Basal area; Pixel; Mathematics; Spatial analysis; Remote sensing; Computer science; Statistics; Artificial intelligence; Geography; Forestry","score_opus":0.012069547919965888,"score_gpt":0.21550269038555198,"score_spread":0.20343314246558608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984839778","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027830668,0.00072523265,0.967061,0.0001201058,0.000040105522,0.00004181541,0.0005579518,0.0026971153,0.0009260174],"genre_scores_gemma":[0.11499121,0.00053416565,0.87871337,0.00006483403,0.00006571975,0.00016185113,0.0019455323,0.00033618294,0.003187105],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966,0.00007725288,0.000032465716,0.00007540922,0.00011786287,0.000037102265],"domain_scores_gemma":[0.9992212,0.00047361056,0.00006690078,0.00009498729,0.00011867503,0.000024620904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008925076,0.0006763387,0.00075221364,0.0016246035,0.00066292926,0.0007577499,0.00084758276,0.0008447426,0.0028712922],"category_scores_gemma":[0.0020910467,0.0006822356,0.0008485011,0.0021828546,0.00038100468,0.0010597076,0.00060410134,0.0007788148,0.0024243756],"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":[0.00053642434,0.00019977213,0.001285206,0.00033603888,0.000119030046,0.00012303307,0.00015224291,0.02941591,0.15239266,0.0030768584,0.008114695,0.80424803],"study_design_scores_gemma":[0.0000855861,0.00015774289,0.02102674,0.000050014092,0.0001896223,0.00024912026,0.00010049216,0.8743394,0.08332606,0.008703514,0.011685779,0.00008587029],"about_ca_topic_score_codex":0.00622006,"about_ca_topic_score_gemma":0.00984411,"teacher_disagreement_score":0.00622006,"about_ca_system_score_codex":0.00036265975,"about_ca_system_score_gemma":0.0007840011,"threshold_uncertainty_score":0.012367725},"labels":[],"label_agreement":null},{"id":"W1987288027","doi":"10.1016/j.rse.2011.08.014","title":"Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements","year":2011,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":750,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Snow; Special sensor microwave/imager; Environmental science; Radiometer; Satellite; Northern Hemisphere; Remote sensing; Data assimilation; Meteorology; Microwave radiometer; Climatology; Microwave; Geology; Computer science; Brightness temperature; Geography","score_opus":0.2587604766399174,"score_gpt":0.3121076577521853,"score_spread":0.05334718111226788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987288027","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9604069,0.0002386517,0.030206213,0.00015477113,0.00007266731,0.000054678243,0.0056407466,0.0007755291,0.002449753],"genre_scores_gemma":[0.9600062,0.00018761797,0.030714663,0.000025491394,0.000027679593,0.000039087412,0.007857722,0.00008958657,0.0010519675],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988186,0.000028739414,0.00000907583,0.000031766398,0.000032376447,0.000016140673],"domain_scores_gemma":[0.999813,0.0000321605,0.000021453421,0.00003790568,0.000077716235,0.000017744176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004053301,0.00037860143,0.00037922166,0.0006400861,0.00034823138,0.0004359839,0.00031826555,0.00029896575,0.0008406438],"category_scores_gemma":[0.0011343424,0.00024798125,0.0004754433,0.0008240816,0.00010311658,0.00068410323,0.0002702276,0.00026554632,0.00040834234],"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":[0.0006195267,0.0006129598,0.31700823,0.0001871286,0.00077048125,0.00031106724,0.0004148807,0.321633,0.06676412,0.0015203069,0.0127424,0.277416],"study_design_scores_gemma":[0.00016950809,0.00006651746,0.37253335,0.000018628234,0.0002188662,0.00003900988,0.0001901091,0.60949004,0.0103691425,0.001393027,0.005454197,0.00005757133],"about_ca_topic_score_codex":0.096923694,"about_ca_topic_score_gemma":0.16131487,"teacher_disagreement_score":0.096923694,"about_ca_system_score_codex":0.0005903465,"about_ca_system_score_gemma":0.001461334,"threshold_uncertainty_score":0.1927191},"labels":[],"label_agreement":null},{"id":"W1988090701","doi":"10.1016/j.rse.2010.02.012","title":"Characterizing temperate forest structural and spectral diversity with Hyperion EO-1 data","year":2010,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":55,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada; University of British Columbia; Canadian Forest Service","funders":"Canadian Forest Service; U.S. Geological Survey","keywords":"Temperate forest; Hyperspectral imaging; Temperate rainforest; Biodiversity; Temperate climate; Environmental science; Canonical correspondence analysis; Forest structure; Canonical correlation; Species diversity; Remote sensing; Geography; Ecology; Physical geography; Abundance (ecology); Mathematics; Ecosystem; Statistics; Biology; Canopy","score_opus":0.011266039452956622,"score_gpt":0.19435156971820508,"score_spread":0.18308553026524846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988090701","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9763907,0.00016190164,0.001884268,0.000082038925,0.000021842594,0.000035725796,0.01717773,0.00033464553,0.003911125],"genre_scores_gemma":[0.9658847,0.0001096783,0.0077134846,0.00005883884,0.000045909554,0.000051555955,0.025333468,0.00012121979,0.0006811866],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997141,0.000030714793,0.000018051202,0.00008666427,0.00007938159,0.00007112409],"domain_scores_gemma":[0.99962103,0.00005915163,0.00006878679,0.0000755076,0.00011099449,0.000064591055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049705093,0.000519977,0.00042705788,0.0024325855,0.00033131274,0.0005173647,0.000458764,0.0006466756,0.0018416212],"category_scores_gemma":[0.0004976936,0.00023553298,0.0004367934,0.0022947295,0.00024833024,0.0010448596,0.000510997,0.0003296976,0.00074572687],"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":[0.0014380165,0.0008316231,0.697073,0.0002916358,0.00035962864,0.00043125564,0.00055278186,0.034000423,0.15216301,0.0008686711,0.008857382,0.10313266],"study_design_scores_gemma":[0.00008442908,0.000067107176,0.9616842,0.000021613689,0.00007154444,0.00010523012,0.00018219733,0.026528493,0.006664927,0.00025147403,0.004296047,0.00004263119],"about_ca_topic_score_codex":0.008248415,"about_ca_topic_score_gemma":0.0218877,"teacher_disagreement_score":0.008248415,"about_ca_system_score_codex":0.0002898147,"about_ca_system_score_gemma":0.0002973072,"threshold_uncertainty_score":0.016400814},"labels":[],"label_agreement":null},{"id":"W1988456065","doi":"10.1016/j.rse.2003.12.011","title":"Airborne experimental measurements of the angular variations in surface temperature over urban areas: case study of Marseille (France)","year":2004,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":112,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Zenith; Nadir; Azimuth; Remote sensing; Environmental science; Brightness temperature; Range (aeronautics); Solar zenith angle; Brightness; Vegetation (pathology); Meteorology; Geography; Satellite; Optics; Materials science; Physics","score_opus":0.012776890415065925,"score_gpt":0.21938340148830662,"score_spread":0.20660651107324068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988456065","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9988642,0.000029117458,0.00042891316,0.000013499692,0.0000037251048,0.000008801558,0.0001950153,0.000026558919,0.00043022618],"genre_scores_gemma":[0.998669,0.000026195357,0.0007935554,0.000007473584,0.000004274122,0.000012862212,0.00027720304,0.0000050361946,0.00020436515],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996656,0.000069877206,0.000010453304,0.0000950725,0.000075544536,0.00008340927],"domain_scores_gemma":[0.9996569,0.00011400292,0.0000491519,0.000060265083,0.00008744709,0.000032225198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035661162,0.0006084238,0.0004098932,0.00042145877,0.0007013154,0.0005517551,0.00056956796,0.00078875956,0.00059255824],"category_scores_gemma":[0.00037191724,0.00020411125,0.00039234894,0.0006960833,0.00049339543,0.00030596554,0.00034395634,0.0003236166,0.0001743429],"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":[0.0040069316,0.0032840073,0.43611258,0.0003716432,0.0007640539,0.0037172863,0.004631608,0.11426773,0.35907543,0.0007702819,0.0026808414,0.07031762],"study_design_scores_gemma":[0.0002559872,0.0013027103,0.953487,0.00000946383,0.00010700136,0.00034191326,0.00082780625,0.02071289,0.021194084,0.00009394405,0.0016212692,0.00004593232],"about_ca_topic_score_codex":0.067460455,"about_ca_topic_score_gemma":0.07600258,"teacher_disagreement_score":0.067460455,"about_ca_system_score_codex":0.0006178045,"about_ca_system_score_gemma":0.0003423096,"threshold_uncertainty_score":0.13413554},"labels":[],"label_agreement":null},{"id":"W1988963177","doi":"10.1016/j.rse.2005.03.012","title":"Towards operational monitoring of a northern wetland using geomatics-based techniques","year":2005,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":189,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada","funders":"Natural Resources Canada; Canadian Space Agency; University of Calgary","keywords":"Geomatics; Flood myth; Remote sensing; Wetland; Vegetation (pathology); Flooding (psychology); Synthetic aperture radar; Multispectral image; Environmental science; Duration (music); Multispectral pattern recognition; Hydrology (agriculture); Satellite imagery; Geography; Cartography; Geology; Ecology","score_opus":0.01533671803850666,"score_gpt":0.25116653371517994,"score_spread":0.23582981567667327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988963177","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7035235,0.0004878942,0.28703377,0.00057438965,0.000039217924,0.0001921825,0.00047793894,0.0008796798,0.006791418],"genre_scores_gemma":[0.70608693,0.00029516453,0.29078245,0.000099832636,0.000030052648,0.000092170674,0.0005696479,0.000025943618,0.0020177676],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.999848,0.000027829346,0.000008594213,0.000032534008,0.000063468426,0.000019648423],"domain_scores_gemma":[0.999509,0.00006561556,0.000063631356,0.0000518566,0.00023751454,0.00007243369],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008355468,0.00026806796,0.0001729963,0.0005715809,0.00029974148,0.00071140524,0.0005059044,0.0005599769,0.00043656715],"category_scores_gemma":[0.0008616212,0.00012147087,0.00011057975,0.0004122965,0.0003055953,0.0007643135,0.000559736,0.00043509767,0.00023044695],"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":[0.00034253864,0.0004545551,0.11712772,0.00017540637,0.000044065062,0.00015876748,0.001204496,0.024676431,0.42019162,0.0028529828,0.0030958292,0.42967567],"study_design_scores_gemma":[0.00014106052,0.0008080448,0.45550776,0.00014409654,0.00016190847,0.00059469347,0.0018403807,0.40694678,0.09935322,0.0057567237,0.028645532,0.00009982657],"about_ca_topic_score_codex":0.02236107,"about_ca_topic_score_gemma":0.043385614,"teacher_disagreement_score":0.02236107,"about_ca_system_score_codex":0.0003155214,"about_ca_system_score_gemma":0.0010750935,"threshold_uncertainty_score":0.044461846},"labels":[],"label_agreement":null},{"id":"W1990160629","doi":"10.1016/j.rse.2013.12.016","title":"A decision-tree classification for low-lying complex land cover types within the zone of discontinuous permafrost","year":2014,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":100,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Waterloo; University of Lethbridge","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Permafrost; Land cover; Remote sensing; Watershed; Cohen's kappa; Vegetation (pathology); Plateau (mathematics); Environmental science; Decision tree; Contextual image classification; Classification scheme; Land use; Geology; Computer science; Data mining; Mathematics; Artificial intelligence; Machine learning; Image (mathematics); Ecology","score_opus":0.03993306927385216,"score_gpt":0.23909870852535936,"score_spread":0.1991656392515072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990160629","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72857857,0.00051025266,0.2584104,0.0006468309,0.00022948059,0.00052860705,0.004281893,0.0018343233,0.0049797064],"genre_scores_gemma":[0.83460367,0.0001044728,0.1572286,0.00008753612,0.000060211714,0.00015934925,0.005269137,0.000065993736,0.002421009],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994332,0.00011200594,0.00007841363,0.00014749604,0.00011994615,0.00010891068],"domain_scores_gemma":[0.9985611,0.00062903145,0.00006070016,0.00007343796,0.0005633624,0.00011233708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014471104,0.0005696754,0.00066864275,0.0018708871,0.0009160606,0.000921004,0.0011145019,0.00088842306,0.0029299816],"category_scores_gemma":[0.0022145957,0.00023684224,0.0008634914,0.0010276748,0.00022953167,0.0007165232,0.00065392366,0.0005874598,0.0007912846],"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":[0.0015783305,0.00093835086,0.07162212,0.00022771764,0.00017194898,0.00038851058,0.00032661253,0.05851375,0.029967465,0.0019265795,0.011807083,0.8225316],"study_design_scores_gemma":[0.000097066964,0.0002437249,0.029186524,0.000038160815,0.00010439708,0.00013361238,0.00027343887,0.9600531,0.0057951827,0.0018006574,0.002245816,0.000028261515],"about_ca_topic_score_codex":0.012310031,"about_ca_topic_score_gemma":0.013339926,"teacher_disagreement_score":0.012310031,"about_ca_system_score_codex":0.00064214424,"about_ca_system_score_gemma":0.0012900764,"threshold_uncertainty_score":0.024476767},"labels":[],"label_agreement":null},{"id":"W1990994937","doi":"10.1016/j.rse.2003.07.003","title":"A comparison of 18 winter seasons of in situ and passive microwave-derived snow water equivalent estimates in Western Canada","year":2003,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":133,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"University of Colorado Boulder","keywords":"Snowpack; Snow; Environmental science; Remote sensing; Special sensor microwave/imager; Brightness temperature; Microwave; Deciduous; Land cover; Microwave radiometer; Meteorology; Radiometer; Pixel; Climatology; Geology; Geography; Computer science; Land use","score_opus":0.02429630567821745,"score_gpt":0.23144072425019122,"score_spread":0.20714441857197377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990994937","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99793124,0.0001479676,0.00006321248,0.000026965563,0.000003379165,0.0000045553193,0.0011776052,0.000005412238,0.00063980906],"genre_scores_gemma":[0.9969842,0.00020040633,0.00017909106,0.000022540788,0.0000024250878,0.0000053780145,0.0016441031,0.0000065097565,0.0009554385],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99978906,0.000016807871,0.000015318696,0.000050660263,0.00005156234,0.00007645132],"domain_scores_gemma":[0.99910766,0.00008052729,0.000094326395,0.000018957568,0.0005629385,0.00013564983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037011958,0.00027943996,0.00028068956,0.0010524347,0.0015260559,0.0009927707,0.00048398468,0.00022257335,0.0006953736],"category_scores_gemma":[0.0007708501,0.00019940679,0.00023761783,0.0016138923,0.0003218465,0.00024241535,0.00038099993,0.00019173692,0.00010323415],"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":[0.0006154375,0.000049035283,0.98073065,0.0000668167,0.0001811262,0.00021081055,0.0024549193,0.00075589726,0.0041235425,0.00010737494,0.00087338145,0.009831025],"study_design_scores_gemma":[0.00000444715,0.000009034067,0.99803907,0.0000070046735,0.00002305201,0.000019710173,0.00080702035,0.00024348461,0.00023494088,0.0000058528244,0.00060277793,0.0000036697577],"about_ca_topic_score_codex":0.9894587,"about_ca_topic_score_gemma":0.99621564,"teacher_disagreement_score":0.01054132,"about_ca_system_score_codex":0.009447782,"about_ca_system_score_gemma":0.008400484,"threshold_uncertainty_score":0.0685488},"labels":[],"label_agreement":null},{"id":"W1991274690","doi":"10.1016/j.rse.2014.06.024","title":"Mapping of NiCu–PGE ore hosting ultramafic rocks using airborne and simulated EnMAP hyperspectral imagery, Nunavik, Canada","year":2014,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Endmember; Hyperspectral imaging; Remote sensing; Geology; Ultramafic rock; Geologic map; Subarctic climate; Environmental science; Geochemistry; Geomorphology","score_opus":0.009733080644048589,"score_gpt":0.18794859127232502,"score_spread":0.17821551062827642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991274690","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97156954,0.0003158337,0.0016100508,0.00024532215,0.00001947138,0.00007355434,0.008702741,0.00024957262,0.01721399],"genre_scores_gemma":[0.98361444,0.00021153531,0.0046383003,0.000028350276,0.0000036026734,0.000023412222,0.0038817257,0.00004671087,0.0075518778],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998888,0.0000034449322,0.0000025468091,0.000019416435,0.000054422497,0.00003142954],"domain_scores_gemma":[0.9997309,0.0000102125505,0.00001167317,0.00000996894,0.000207865,0.000029414432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014506695,0.00024716946,0.0001503631,0.0013400421,0.0012530402,0.0007904804,0.0006031222,0.00020566532,0.0012745999],"category_scores_gemma":[0.0003177053,0.00020905265,0.00018472485,0.0016625609,0.00037561095,0.00019220386,0.00032366536,0.00023084432,0.00025847816],"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":[0.0010002467,0.0003118445,0.70492935,0.00034189486,0.00023842943,0.0012099616,0.0024329082,0.055660807,0.067287825,0.003046289,0.026432239,0.13710825],"study_design_scores_gemma":[0.000043168064,0.000028283537,0.9409999,0.000049502625,0.000049874514,0.00016442925,0.0023901055,0.03066711,0.0061268853,0.00018522379,0.01925397,0.000041484163],"about_ca_topic_score_codex":0.98488414,"about_ca_topic_score_gemma":0.99618965,"teacher_disagreement_score":0.015115857,"about_ca_system_score_codex":0.0072738216,"about_ca_system_score_gemma":0.009488737,"threshold_uncertainty_score":0.052775502},"labels":[],"label_agreement":null},{"id":"W1991525894","doi":"10.1016/s0034-4257(99)00088-7","title":"Orbital Radar Studies of Paleodrainages in the Central Namib Desert","year":2000,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Geology; Fluvial; Radar; Surface runoff; Geomorphology; Remote sensing; Structural basin","score_opus":0.015625340947004542,"score_gpt":0.23751391414186607,"score_spread":0.22188857319486152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991525894","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.999342,0.00008290351,0.000013767782,0.000023105584,0.0000011701944,0.0000017047545,0.00003770482,6.602657e-7,0.0004968806],"genre_scores_gemma":[0.99926883,0.00013235104,0.00008217079,0.000025871552,0.000002780926,0.000004106138,0.00010283944,0.0000011714218,0.0003798979],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99991393,0.000023074928,0.000005912471,0.00001217971,0.00000947116,0.000035394165],"domain_scores_gemma":[0.9998447,0.000023139813,0.00004194849,0.000010567922,0.000033461252,0.000046208424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024086003,0.00019023602,0.00022084966,0.0010678474,0.00083583343,0.00058020593,0.00038733464,0.00027160818,0.00070424285],"category_scores_gemma":[0.00047943273,0.00015312813,0.000085364,0.0015118596,0.0005534748,0.00032901778,0.00049436523,0.00015986817,0.00010300474],"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":[0.0005664734,0.00012010948,0.93780315,0.000079954334,0.0000869996,0.0019500277,0.016480584,0.00047087902,0.020719133,0.000564653,0.00029888022,0.020859245],"study_design_scores_gemma":[0.000010704294,0.00003437331,0.9933415,0.000008786976,0.000012128952,0.0002404089,0.00466477,0.00018178471,0.00030597416,0.000043576936,0.0011521971,0.000003842078],"about_ca_topic_score_codex":0.1080696,"about_ca_topic_score_gemma":0.23802195,"teacher_disagreement_score":0.1080696,"about_ca_system_score_codex":0.0011668907,"about_ca_system_score_gemma":0.0005792825,"threshold_uncertainty_score":0.21488112},"labels":[],"label_agreement":null},{"id":"W1996585943","doi":"10.1016/j.rse.2013.04.019","title":"Automated reconstruction of tree and canopy structure for modeling the internal canopy radiation regime","year":2013,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; University of British Columbia","funders":"Canadian Forest Service; Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Remote sensing; Canopy; Radiative transfer; Photosynthetically active radiation; Geometric modeling; Laser scanning; Environmental science; Computer science; Vegetation (pathology); Range (aeronautics); Mathematics; Optics; Laser; Geometry; Physics; Ecology; Geography","score_opus":0.00800292559027017,"score_gpt":0.2096810084570082,"score_spread":0.20167808286673805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996585943","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2394574,0.0002216068,0.7554159,0.0000856877,0.00002463249,0.000040181283,0.0004825897,0.0024631151,0.0018088754],"genre_scores_gemma":[0.8444327,0.000090647365,0.15370315,0.000022993217,0.000012449466,0.000042100422,0.000557119,0.00015574312,0.0009830623],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988675,0.00002262041,0.000005049423,0.000034688415,0.00003384355,0.000017015092],"domain_scores_gemma":[0.9998079,0.000076869925,0.000022714235,0.00004181538,0.0000381008,0.0000125756005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022717935,0.0004414696,0.00050217204,0.0005837888,0.00035216537,0.00066472887,0.00068005675,0.0006411316,0.0010704818],"category_scores_gemma":[0.00067955337,0.00050688663,0.00054239534,0.00057698943,0.0002477154,0.00060200546,0.00037937984,0.00054253056,0.00035326343],"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":[0.00007119486,0.00010283188,0.004044368,0.000036514673,0.000051076644,0.000059397495,0.000061443556,0.8933731,0.017319625,0.0018700743,0.0008731055,0.08213732],"study_design_scores_gemma":[0.0000025871634,0.0000027745361,0.00048510867,8.2414056e-7,0.0000021267913,0.0000066474304,0.0000034564519,0.99819714,0.00084253517,0.00034074695,0.00011360717,0.0000023669866],"about_ca_topic_score_codex":0.012777315,"about_ca_topic_score_gemma":0.021618584,"teacher_disagreement_score":0.012777315,"about_ca_system_score_codex":0.00048898754,"about_ca_system_score_gemma":0.0009086035,"threshold_uncertainty_score":0.025405884},"labels":[],"label_agreement":null},{"id":"W1996828724","doi":"10.1016/j.rse.2012.09.022","title":"Landcover classification of the Lower Nhecolândia subregion of the Brazilian Pantanal Wetlands using ALOS/PALSAR, RADARSAT-2 and ENVISAT/ASAR imagery","year":2012,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":106,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Wetland; Habitat; Vegetation (pathology); Environmental science; Geography; Grassland; Biodiversity; Physical geography; Remote sensing; Ecology; Hydrology (agriculture); Geology","score_opus":0.018722230997571213,"score_gpt":0.2107248522293865,"score_spread":0.19200262123181527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996828724","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9961511,0.0001103879,0.00020202273,0.00005031386,0.000001941944,0.000011946032,0.00078306685,0.00002158671,0.002667692],"genre_scores_gemma":[0.99855155,0.00005747973,0.00032317045,0.000005867776,9.123842e-7,0.000005668303,0.00059308263,0.000003178108,0.00045916674],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998996,0.000013636871,0.000009860591,0.000022300006,0.00002235017,0.00003212708],"domain_scores_gemma":[0.99982613,0.000026108944,0.000049794664,0.000018761035,0.00005868286,0.000020507632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011762082,0.00014027933,0.00013074037,0.0016225892,0.00022716874,0.00042964707,0.00013257653,0.00011339764,0.0011783049],"category_scores_gemma":[0.00047523621,0.00008392302,0.00014585632,0.0014668215,0.00020711664,0.00017812314,0.00027993566,0.00006994683,0.0001895187],"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":[0.00013150054,0.00004459818,0.9265403,0.00009626264,0.000043486776,0.00023450489,0.0014582056,0.0008940228,0.012872045,0.00052494626,0.00074049976,0.05641966],"study_design_scores_gemma":[0.0000021124085,0.000007206302,0.9970553,0.000009361068,0.0000124328035,0.000053339,0.00046597302,0.0008431761,0.00021630581,0.000028330858,0.0013040351,0.000002530767],"about_ca_topic_score_codex":0.18457456,"about_ca_topic_score_gemma":0.37902573,"teacher_disagreement_score":0.18457456,"about_ca_system_score_codex":0.0006193007,"about_ca_system_score_gemma":0.0004429492,"threshold_uncertainty_score":0.3670004},"labels":[],"label_agreement":null},{"id":"W1997762178","doi":"10.1016/j.rse.2011.08.013","title":"Topographically based spatially averaging of SAR data improves performance of soil moisture models","year":2011,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Pothole (geology); Environmental science; Remote sensing; Transect; Water content; Backscatter (email); Radar; Soil science; Hydrology (agriculture); Spatial ecology; Geology; Geomorphology; Geotechnical engineering","score_opus":0.0266457524809316,"score_gpt":0.20249070057637863,"score_spread":0.17584494809544704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997762178","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8318066,0.0008195484,0.15513104,0.0003108081,0.00021829366,0.00003510601,0.0008043034,0.0069192247,0.0039550974],"genre_scores_gemma":[0.96702665,0.0002486417,0.0312474,0.000054128996,0.00007869866,0.000009207664,0.0005845444,0.0002679668,0.00048272166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997285,0.00008285399,0.000024232264,0.00007170197,0.000055643573,0.0000370798],"domain_scores_gemma":[0.9991447,0.0003185763,0.00007936862,0.00026813286,0.00016234799,0.000026906333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007800411,0.00071371335,0.0007181019,0.0006226001,0.0002654823,0.0006570357,0.00042429165,0.0004484186,0.00086919515],"category_scores_gemma":[0.0026321895,0.0003243607,0.0006281398,0.0007810017,0.00015906653,0.0010632545,0.00041289907,0.00042713198,0.00041375763],"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":[0.00038085558,0.00022821681,0.01632929,0.00008215602,0.00030521522,0.00009483282,0.00008849438,0.7205825,0.049680106,0.0006088255,0.0019810186,0.20963846],"study_design_scores_gemma":[0.000017012306,0.00005503457,0.00780733,0.0000032504245,0.00006763251,0.000024597846,0.000016872931,0.9845167,0.0066655343,0.00028130994,0.0005283216,0.00001650831],"about_ca_topic_score_codex":0.008344802,"about_ca_topic_score_gemma":0.012911549,"teacher_disagreement_score":0.008344802,"about_ca_system_score_codex":0.000203286,"about_ca_system_score_gemma":0.00039478575,"threshold_uncertainty_score":0.016592443},"labels":[],"label_agreement":null},{"id":"W1999261473","doi":"10.1016/j.rse.2009.11.017","title":"Treeline vegetation composition and change in Canada's western Subarctic from AVHRR and canopy reflectance modeling","year":2009,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Tree-ring climate responses","field":"Earth and Planetary Sciences","cited_by":47,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Subarctic climate; Tundra; Vegetation (pathology); Normalized Difference Vegetation Index; Environmental science; Taiga; Remote sensing; Enhanced vegetation index; Climate change; Advanced very-high-resolution radiometer; Physical geography; Canopy; Ecotone; Climatology; Arctic; Geology; Geography; Ecology; Vegetation Index; Satellite; Shrub","score_opus":0.021685525334186675,"score_gpt":0.2199770582008057,"score_spread":0.19829153286661905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999261473","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9951858,0.0003380507,0.000344861,0.0000830838,0.0000084054955,0.000009614604,0.0025018556,0.00008826699,0.0014399891],"genre_scores_gemma":[0.9959662,0.00018126122,0.0006756444,0.000028807895,0.0000032739943,0.00000587882,0.0020231896,0.00003056832,0.001085218],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997838,0.000022326172,0.000009510148,0.0000636712,0.00005457186,0.00006610422],"domain_scores_gemma":[0.9995933,0.000051553372,0.000033865,0.00002089813,0.00022706774,0.00007327784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047181325,0.0004386099,0.00035949357,0.0009375334,0.0013991864,0.001115833,0.00091964967,0.0004764886,0.00122835],"category_scores_gemma":[0.0007270908,0.00033536996,0.0005624689,0.0014897316,0.00035846158,0.0003653437,0.00022978676,0.00038657253,0.00034468243],"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":[0.00050508673,0.00020337745,0.85048723,0.00012669587,0.00045047712,0.000374898,0.00074470177,0.10525499,0.00717822,0.0011963934,0.005736295,0.027741633],"study_design_scores_gemma":[0.00005137566,0.00001496969,0.8790352,0.000045949004,0.0001968364,0.00008056185,0.0007826502,0.11405418,0.0013483911,0.00019338817,0.0041403733,0.000056200744],"about_ca_topic_score_codex":0.99336267,"about_ca_topic_score_gemma":0.99507964,"teacher_disagreement_score":0.01748752,"about_ca_system_score_codex":0.01748752,"about_ca_system_score_gemma":0.010111442,"threshold_uncertainty_score":0.12688142},"labels":[],"label_agreement":null},{"id":"W2000210684","doi":"10.1016/j.rse.2004.11.011","title":"Mapping PAR using MODIS atmosphere products","year":2005,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":76,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"University of Alberta","keywords":"Remote sensing; Environmental science; Satellite; Photosynthetically active radiation; Atmosphere (unit); Aerosol; Atmospheric correction; Vegetation (pathology); Meteorology; Geology; Geography; Physics","score_opus":0.014805197736208671,"score_gpt":0.2062179485286766,"score_spread":0.19141275079246792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000210684","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55518836,0.0007361892,0.3543065,0.00096070976,0.000512762,0.00026475676,0.017876148,0.020009808,0.050144814],"genre_scores_gemma":[0.7124347,0.0003455742,0.27299908,0.00012602632,0.000088311164,0.00008840657,0.008522031,0.0007920728,0.004603712],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980265,0.000025212154,0.0000066324433,0.00006592698,0.00006203233,0.00003756636],"domain_scores_gemma":[0.99977475,0.00003066251,0.00001742209,0.000063525906,0.000096262425,0.000017311224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035141382,0.00044650707,0.00036595284,0.0007101467,0.00029796184,0.00088483904,0.00044842696,0.0003408764,0.0027439636],"category_scores_gemma":[0.0008695248,0.00040446047,0.0004308451,0.001006309,0.00011196675,0.0008220318,0.00029616966,0.00030999575,0.0016818951],"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":[0.0005728163,0.00026688422,0.041615408,0.0002982788,0.0002705967,0.00033325615,0.00033970657,0.1163226,0.1410658,0.005271139,0.03786855,0.655775],"study_design_scores_gemma":[0.00022377142,0.000108486944,0.1291247,0.000043907912,0.00020213865,0.0003178611,0.00028226696,0.75297666,0.053743236,0.004992611,0.057881307,0.0001031384],"about_ca_topic_score_codex":0.014994065,"about_ca_topic_score_gemma":0.015137302,"teacher_disagreement_score":0.014994065,"about_ca_system_score_codex":0.00030166254,"about_ca_system_score_gemma":0.0004309182,"threshold_uncertainty_score":0.029813588},"labels":[],"label_agreement":null},{"id":"W2000496987","doi":"10.1016/j.rse.2012.10.003","title":"A preliminary evaluation of the impact of assimilating AVHRR data on sea ice concentration analyses","year":2012,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada; University of Waterloo","funders":"Bowel Disease Research Foundation; National Aeronautics and Space Administration","keywords":"Remote sensing; Sea ice; Environmental science; Geology; Oceanography","score_opus":0.09565353040453518,"score_gpt":0.3273635795811395,"score_spread":0.23171004917660432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000496987","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96496814,0.00035207416,0.020828128,0.00062553096,0.00009988404,0.00023911172,0.003758831,0.0008212301,0.008307164],"genre_scores_gemma":[0.9434541,0.00024026043,0.049434863,0.00025265405,0.000051430234,0.00007214965,0.0025329688,0.00013824759,0.003823391],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986994,0.00056729664,0.00007191248,0.00021750653,0.00037319178,0.00007064171],"domain_scores_gemma":[0.99142647,0.0058711027,0.00016413211,0.0004877542,0.0018840584,0.00016645342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004274209,0.0007442364,0.00032268066,0.00037995534,0.0006795142,0.0010829582,0.00065231865,0.00079301157,0.003701825],"category_scores_gemma":[0.010643017,0.00020539392,0.0006056513,0.0006475918,0.00031352497,0.00088048587,0.0004626106,0.00042107064,0.000722226],"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":[0.013127067,0.0019342933,0.18452173,0.00062212785,0.00088143774,0.0010378503,0.0007267753,0.18213105,0.33998066,0.0013681875,0.004281104,0.26938778],"study_design_scores_gemma":[0.0012154027,0.004787225,0.40929,0.000114371964,0.0009495493,0.00030808544,0.0012765646,0.38764265,0.17950596,0.001301158,0.013394064,0.00021488126],"about_ca_topic_score_codex":0.034684964,"about_ca_topic_score_gemma":0.044566836,"teacher_disagreement_score":0.034684964,"about_ca_system_score_codex":0.00045331832,"about_ca_system_score_gemma":0.000821182,"threshold_uncertainty_score":0.06896615},"labels":[],"label_agreement":null},{"id":"W2001072978","doi":"10.1016/s0034-4257(01)00193-6","title":"Caribou habitat mapping and fragmentation analysis using Landsat MSS, TM, and GIS data in the North Columbia Mountains, British Columbia, Canada","year":2001,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Rangeland and Wildlife Management","field":"Environmental Science","cited_by":94,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Mount Revelstoke National Park; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Parks Canada; University of Calgary","keywords":"Thematic Mapper; Habitat; Physical geography; Geography; Multispectral Scanner; Thematic map; Remote sensing; Environmental science; Satellite imagery; Fragmentation (computing); Vegetation (pathology); Cartography; Ecology","score_opus":0.013855716992528789,"score_gpt":0.19848748697299048,"score_spread":0.18463176998046169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001072978","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99069715,0.00067225617,0.0003035904,0.00028783877,0.000010712341,0.00006320151,0.0038063102,0.000034446817,0.0041245865],"genre_scores_gemma":[0.98913026,0.0006593927,0.0012452677,0.00004948592,0.0000046347986,0.00005535177,0.0029851743,0.000018154573,0.005852376],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995472,0.00006365042,0.000037298585,0.0000741389,0.00015872162,0.000118956945],"domain_scores_gemma":[0.9977464,0.00016622928,0.00012318643,0.000065143766,0.0016117719,0.00028726555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062443217,0.00025334398,0.0002878019,0.0030981663,0.002261949,0.0013293156,0.00087394123,0.00020712138,0.0019452714],"category_scores_gemma":[0.0026901453,0.00029788885,0.00022801873,0.0054898676,0.00045124633,0.00033807982,0.00068287674,0.00036474096,0.00026123566],"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":[0.00019219697,0.000098284225,0.9318348,0.00011930686,0.00013352511,0.00034795338,0.0022210053,0.002286368,0.0012998912,0.00040494927,0.01063663,0.050425023],"study_design_scores_gemma":[0.000014738125,0.000008968996,0.9924297,0.000052755004,0.000045413508,0.000055305434,0.0024833959,0.0020392763,0.0001680123,0.00004866507,0.002637847,0.000015826667],"about_ca_topic_score_codex":0.9977926,"about_ca_topic_score_gemma":0.99944025,"teacher_disagreement_score":0.015606362,"about_ca_system_score_codex":0.015606362,"about_ca_system_score_gemma":0.02270512,"threshold_uncertainty_score":0.11323261},"labels":[],"label_agreement":null},{"id":"W2001462218","doi":"10.1016/j.rse.2009.12.005","title":"Continuous wavelet analysis for the detection of green attack damage due to mountain pine beetle infestation","year":2010,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":187,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Mountain pine beetle; Multispectral image; Infestation; Wavelet; Environmental science; Remote sensing; Pinus contorta; Continuous wavelet transform; Dendroctonus; Wavelet transform; Forestry; Horticulture; Geography; Biology; Discrete wavelet transform; Computer science; Bark beetle; Artificial intelligence","score_opus":0.00918876301033444,"score_gpt":0.22644537515569615,"score_spread":0.2172566121453617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001462218","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91796196,0.0005004071,0.079665706,0.000080746366,0.0000393862,0.000022626653,0.00017745279,0.00013555148,0.0014162153],"genre_scores_gemma":[0.9810431,0.0002408538,0.018030286,0.000016082642,0.000022163778,0.000012668214,0.00010594379,0.000011373857,0.0005173528],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99990296,0.000022803628,0.0000046788377,0.000015414838,0.000037491813,0.000016601372],"domain_scores_gemma":[0.999603,0.00022620428,0.00004433579,0.000023506127,0.00007370974,0.000029192315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030742402,0.0002154591,0.00020024569,0.00078729825,0.00013152137,0.0002825096,0.00014302264,0.00028622302,0.00067098776],"category_scores_gemma":[0.00092144986,0.00010592718,0.00019744577,0.00047982633,0.00013845554,0.00024324864,0.00013966578,0.00028531166,0.00015034991],"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":[0.0026367067,0.00042415762,0.030941622,0.0002859383,0.00014902373,0.00041965683,0.00019115812,0.024292162,0.46034315,0.001174183,0.0015519696,0.47759035],"study_design_scores_gemma":[0.00009337905,0.0006479068,0.22918248,0.000030833045,0.00019325408,0.0005198613,0.00023399983,0.7188548,0.04806392,0.00090331153,0.0012261253,0.000050237486],"about_ca_topic_score_codex":0.0012226525,"about_ca_topic_score_gemma":0.0016157558,"teacher_disagreement_score":0.0012226525,"about_ca_system_score_codex":0.00013537123,"about_ca_system_score_gemma":0.00014946538,"threshold_uncertainty_score":0.0024310946},"labels":[],"label_agreement":null},{"id":"W2005839781","doi":"10.1016/j.rse.2015.03.009","title":"Assimilation of surface albedo and vegetation states from satellite observations and their impact on numerical weather prediction","year":2015,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":106,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Seventh Framework Programme; Natural Resources Canada; BIOCAP Canada; Environment Canada; Biological and Environmental Research; Natural Sciences and Engineering Research Council of Canada; Canadian Foundation for Climate and Atmospheric Sciences","keywords":"Albedo (alchemy); Environmental science; Leaf area index; Climatology; Data assimilation; Anomaly (physics); Numerical weather prediction; Atmospheric sciences; Eddy covariance; Satellite; Vegetation (pathology); Latent heat; Meteorology; Geology; Ecosystem; Geography","score_opus":0.016795167369203667,"score_gpt":0.20628793229797238,"score_spread":0.1894927649287687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005839781","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.987074,0.0002044225,0.01038154,0.00025247337,0.00015127029,0.000014508955,0.00046428922,0.00016741938,0.0012900219],"genre_scores_gemma":[0.9941048,0.00010931626,0.004788049,0.000030162713,0.000025175801,0.000012198803,0.00052943046,0.000019561792,0.00038132936],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983656,0.00004234704,0.0000163524,0.000044196055,0.000038759375,0.000021744074],"domain_scores_gemma":[0.9991425,0.0004191453,0.00007821041,0.00012880829,0.00018127258,0.000049983682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007740057,0.00047181145,0.00046131705,0.00029958008,0.00036986775,0.00071803044,0.0005108446,0.00094713527,0.00058260845],"category_scores_gemma":[0.0029543764,0.00048156962,0.00055687653,0.0004868871,0.00039813924,0.00092594756,0.00045767834,0.00080170494,0.00015851691],"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":[0.00059180293,0.0004395042,0.03889686,0.00006576542,0.00023592998,0.0000721924,0.00012887864,0.89015925,0.02255361,0.0011210772,0.0014311632,0.044303995],"study_design_scores_gemma":[0.000028244718,0.000024615287,0.01716466,0.0000044804515,0.000024511768,0.0000050799954,0.00001290756,0.9799129,0.0023329635,0.00030910945,0.00016537013,0.000015273372],"about_ca_topic_score_codex":0.046054687,"about_ca_topic_score_gemma":0.03747452,"teacher_disagreement_score":0.046054687,"about_ca_system_score_codex":0.000578515,"about_ca_system_score_gemma":0.00092781935,"threshold_uncertainty_score":0.09157324},"labels":[],"label_agreement":null},{"id":"W2009726216","doi":"10.1016/j.rse.2009.01.013","title":"Prediction and assessment of bark beetle-induced mortality of lodgepole pine using estimates of stand vigor derived from remotely sensed data","year":2009,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":78,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; University of British Columbia","funders":"Natural Resources Canada; U.S. Geological Survey; Canadian Forest Service; Universidad de León; Government of Canada","keywords":"Mountain pine beetle; Pinus contorta; Thinning; Canopy; Dendroctonus; Environmental science; Bark beetle; Forestry; Leaf area index; Crown (dentistry); Bark (sound); Geography; Biology; Agronomy; Botany","score_opus":0.05649669137526761,"score_gpt":0.29440119309959756,"score_spread":0.23790450172432995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009726216","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9983851,0.000033717042,0.0013285653,0.000008459348,0.0000015308354,0.0000060201523,0.00008255545,0.000025961845,0.00012818683],"genre_scores_gemma":[0.99882144,0.000017628863,0.00089625857,0.0000025600198,0.0000017642797,0.0000032949706,0.00016761568,0.0000018211816,0.00008757397],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99984574,0.00005663584,0.000009508795,0.000035592773,0.000032423537,0.000019997005],"domain_scores_gemma":[0.9982994,0.0011140524,0.00021864455,0.00006327639,0.00019265526,0.00011204395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011258395,0.00051976467,0.00023749936,0.0005526788,0.00016020176,0.0003792144,0.00029565816,0.00031252584,0.00020802884],"category_scores_gemma":[0.0015706964,0.00019878395,0.0002887278,0.00016464447,0.000117962496,0.00033175206,0.00017537111,0.00024448303,0.000098776756],"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":[0.0008768131,0.0005010766,0.8284643,0.000046889585,0.00019340045,0.00014693207,0.000092900904,0.11771125,0.023856923,0.00013164662,0.00020498996,0.027772838],"study_design_scores_gemma":[0.000033484142,0.00049634936,0.6040226,0.0000047671756,0.0000636871,0.000057846475,0.00006142675,0.39050734,0.0045354753,0.00012039281,0.000074366784,0.000022164364],"about_ca_topic_score_codex":0.008064894,"about_ca_topic_score_gemma":0.014643213,"teacher_disagreement_score":0.008064894,"about_ca_system_score_codex":0.00044985462,"about_ca_system_score_gemma":0.00022404715,"threshold_uncertainty_score":0.016035914},"labels":[],"label_agreement":null},{"id":"W2011500029","doi":"10.1016/j.rse.2014.02.015","title":"Good practices for estimating area and assessing accuracy of land change","year":2014,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":2924,"is_retracted":false,"has_abstract":false,"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":"","keywords":"Computer science; Data mining; Sampling (signal processing); Sampling design; Sample (material); Reference data; Set (abstract data type); Range (aeronautics); Sample size determination; Systematic sampling; Remote sensing; Statistics; Mathematics","score_opus":0.03890573690532747,"score_gpt":0.2790296876952497,"score_spread":0.24012395078992224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011500029","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064810067,0.0029636563,0.97851336,0.0027420477,0.00025620704,0.00044808508,0.0005868683,0.0023224996,0.0056862975],"genre_scores_gemma":[0.03139443,0.0012540339,0.9650234,0.00022571813,0.00008023743,0.00029455256,0.00026993523,0.00038802304,0.0010697533],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95426655,0.01705553,0.007003642,0.0029923567,0.01797813,0.00070390035],"domain_scores_gemma":[0.82826996,0.058407806,0.008611025,0.04125619,0.06220467,0.0012503943],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.037829798,0.00297866,0.0023959717,0.010913445,0.0030001125,0.005792218,0.0067920964,0.0040821256,0.0025004807],"category_scores_gemma":[0.14111787,0.0024714496,0.0021833272,0.0077385693,0.0049000876,0.0060195467,0.0030440642,0.0053494126,0.0032681248],"study_design_candidate":"theoretical_or_conceptual","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":[0.0001821554,0.00055717124,0.035184182,0.0028286425,0.0010803132,0.0005614298,0.0049408814,0.050386947,0.022453563,0.032242566,0.028313048,0.82126915],"study_design_scores_gemma":[0.0002972959,0.0006510778,0.117754795,0.0079700025,0.0014947357,0.0041272296,0.0072064167,0.14001136,0.12661682,0.3100917,0.28211713,0.0016614685],"about_ca_topic_score_codex":0.03263315,"about_ca_topic_score_gemma":0.056125704,"teacher_disagreement_score":0.9621702,"about_ca_system_score_codex":0.0028698305,"about_ca_system_score_gemma":0.0034707354,"threshold_uncertainty_score":0.2000655},"labels":[],"label_agreement":null},{"id":"W2011947156","doi":"10.1016/j.rse.2009.08.012","title":"Modelling daytime thermal infrared directional anisotropy over Toulouse city centre","year":2009,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":146,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Daytime; Canyon; Anisotropy; Nadir; Remote sensing; Environmental science; Infrared; Brightness; Thermal; Meteorology; Ranging; Geology; Atmospheric sciences; Geodesy; Optics; Physics; Geomorphology; Astronomy","score_opus":0.011220051493020017,"score_gpt":0.1957017653139186,"score_spread":0.1844817138208986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011947156","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99660635,0.00007550176,0.0010663567,0.000115909934,0.000020101175,0.000011192482,0.00074832333,0.00011087778,0.0012454762],"genre_scores_gemma":[0.9979644,0.000044338398,0.0008798587,0.000011809514,0.000008063949,0.000008998103,0.000561308,0.00002734881,0.00049391814],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998331,0.000031956824,0.000005402458,0.000038839466,0.000017022066,0.00007371307],"domain_scores_gemma":[0.9996635,0.0001624298,0.000026115953,0.000043198674,0.000050089646,0.000054770968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033462836,0.0008675878,0.00064511935,0.0004996758,0.0007258737,0.0011200669,0.00092859805,0.0013901262,0.0012578683],"category_scores_gemma":[0.00076146587,0.0006101016,0.0011367552,0.0008173664,0.00051960564,0.0004736197,0.0004147603,0.00082809094,0.000193479],"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":[0.00015288232,0.000093558534,0.008845708,0.000019904892,0.00005995477,0.00015605848,0.000046726676,0.9871891,0.0011916172,0.00017415958,0.00040164514,0.0016687666],"study_design_scores_gemma":[0.00011298419,0.00003887586,0.019797994,0.0000061549385,0.000033680626,0.00001747646,0.0000960585,0.97893244,0.00054213597,0.000088426,0.00030997716,0.000023775512],"about_ca_topic_score_codex":0.34004715,"about_ca_topic_score_gemma":0.2236102,"teacher_disagreement_score":0.34004715,"about_ca_system_score_codex":0.00188278,"about_ca_system_score_gemma":0.0015042794,"threshold_uncertainty_score":0.6761357},"labels":[],"label_agreement":null},{"id":"W2015923654","doi":"10.1016/j.rse.2002.08.001","title":"Large area forest classification and biophysical parameter estimation using the 5-Scale canopy reflectance model in Multiple-Forward-Mode","year":2003,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":76,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Remote sensing; Scale (ratio); Computer science; Bidirectional reflectance distribution function; Mode (computer interface); Tree canopy; Environmental science; Reflectivity; Data mining; Canopy; Geology; Geography; Cartography; Optics","score_opus":0.025496092267258023,"score_gpt":0.25204377914367027,"score_spread":0.22654768687641225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015923654","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45047316,0.000101375976,0.5461481,0.00013694746,0.000035450787,0.000032871434,0.00029931567,0.0013168872,0.0014558815],"genre_scores_gemma":[0.85870427,0.000043302312,0.13846736,0.00002007934,0.000012514492,0.000026901022,0.00052161,0.00006390811,0.0021401516],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976784,0.000075745025,0.000010216172,0.000054674183,0.00005828196,0.000033161],"domain_scores_gemma":[0.9996718,0.00013948596,0.000030744766,0.00006331204,0.00007467165,0.000019926028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008286427,0.00037242056,0.00040774955,0.00041588687,0.00026562458,0.00036562295,0.00045618715,0.00050853204,0.0013446254],"category_scores_gemma":[0.0012576203,0.00029554204,0.0005823761,0.00036177554,0.00016340222,0.00085041736,0.0002807241,0.0004841626,0.00056451],"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":[0.00064891466,0.0003588522,0.017322816,0.000059622223,0.00013257725,0.00015116514,0.00011510363,0.58230644,0.041356128,0.0026541988,0.0024177337,0.3524764],"study_design_scores_gemma":[0.00000962562,0.000014791586,0.005450504,0.0000015259938,0.000009460117,0.000022397402,0.000012015147,0.9913709,0.0024171337,0.0005628849,0.00012175567,0.000007025752],"about_ca_topic_score_codex":0.009182568,"about_ca_topic_score_gemma":0.013993287,"teacher_disagreement_score":0.009182568,"about_ca_system_score_codex":0.000316855,"about_ca_system_score_gemma":0.00046172997,"threshold_uncertainty_score":0.018258274},"labels":[],"label_agreement":null},{"id":"W2016428929","doi":"10.1016/j.rse.2007.03.011","title":"Integrating LIDAR data and multispectral imagery for enhanced classification of rangeland vegetation: A meta analysis","year":2007,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":235,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Alberta","funders":"University of Alberta","keywords":"Multispectral image; Lidar; Remote sensing; Shrubland; Vegetation (pathology); Digital elevation model; Vegetation classification; Multispectral pattern recognition; Environmental science; Rangeland; Geography; Ecology; Agroforestry; Ecosystem","score_opus":0.039322283819570855,"score_gpt":0.289043691653765,"score_spread":0.2497214078341941,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016428929","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9323984,0.003290797,0.060916197,0.00038645335,0.00005201399,0.00006573593,0.0011255635,0.00049187127,0.001272954],"genre_scores_gemma":[0.9553889,0.0005279601,0.042344395,0.000049822247,0.000032341086,0.00003174524,0.0009192845,0.0000829912,0.0006225623],"study_design_codex":"design_other","study_design_gemma":"meta_analysis","domain_scores_codex":[0.9993111,0.0002947745,0.000044856082,0.0001417836,0.00014864959,0.000058726124],"domain_scores_gemma":[0.9988991,0.00052282185,0.00012799905,0.00013391957,0.00025478748,0.00006128058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024515954,0.0008998279,0.0009715974,0.0039561456,0.00044606716,0.0016305875,0.0007558755,0.0004701844,0.0008453723],"category_scores_gemma":[0.0019499497,0.0004994151,0.0031290625,0.0025993166,0.0002169578,0.0013563902,0.0006584464,0.0003536486,0.00020861658],"study_design_candidate":"meta_analysis","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":[0.0021026721,0.0016456165,0.3457582,0.00084459706,0.024064146,0.00046055636,0.0005757554,0.08641703,0.083912686,0.0014269591,0.0031527092,0.44963914],"study_design_scores_gemma":[0.00023649051,0.00086135673,0.3939407,0.00020199153,0.022254337,0.000379438,0.001338787,0.549504,0.023291482,0.0037780323,0.0039955648,0.00021793447],"about_ca_topic_score_codex":0.009675776,"about_ca_topic_score_gemma":0.013827091,"teacher_disagreement_score":0.009675776,"about_ca_system_score_codex":0.00063687976,"about_ca_system_score_gemma":0.0005288127,"threshold_uncertainty_score":0.01923889},"labels":[],"label_agreement":null},{"id":"W2016688866","doi":"10.1016/j.rse.2004.07.009","title":"Estimating time since forest harvest using segmented Landsat ETM+ imagery","year":2004,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":92,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"Canadian Forest Service; Government of Canada","keywords":"Disturbance (geology); Environmental science; Pinus contorta; Forest inventory; Remote sensing; Multivariate statistics; Pixel; Forest dynamics; Segmentation; Satellite imagery; Forest management; Forestry; Computer science; Statistics; Geography; Mathematics; Agroforestry; Ecology; Artificial intelligence; Geology","score_opus":0.011303756492053056,"score_gpt":0.22432895582037243,"score_spread":0.21302519932831937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016688866","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9893665,0.00022542827,0.007520525,0.000022859109,0.00001805809,0.000011259306,0.0018255165,0.00021291402,0.0007968777],"genre_scores_gemma":[0.9855779,0.000120911536,0.00955703,0.000009522444,0.000017947264,0.000010052019,0.0038651212,0.000028686321,0.00081281824],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999471,0.0000036744739,0.0000043986543,0.000019609353,0.00001106215,0.000014098196],"domain_scores_gemma":[0.99962044,0.00012090435,0.00007633774,0.000027357039,0.00010993642,0.000044955632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023963819,0.00032291946,0.0002737073,0.0017554298,0.0002167963,0.0003280606,0.00028830476,0.00029904104,0.001487465],"category_scores_gemma":[0.0006982833,0.00021401403,0.0003155232,0.00090962957,0.000078311736,0.00056705537,0.00016893135,0.0001599801,0.00068797375],"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":[0.0011012354,0.00021242247,0.81616586,0.00010999272,0.00021910698,0.0003314379,0.00015266913,0.045296226,0.026458386,0.00037342354,0.0021211642,0.10745816],"study_design_scores_gemma":[0.00003503205,0.000107721244,0.8261781,0.0000128697375,0.000108561755,0.00020449095,0.0001721144,0.16609487,0.0054996153,0.0003444707,0.0012153165,0.000026849095],"about_ca_topic_score_codex":0.022802388,"about_ca_topic_score_gemma":0.052611,"teacher_disagreement_score":0.022802388,"about_ca_system_score_codex":0.00033675082,"about_ca_system_score_gemma":0.0002773823,"threshold_uncertainty_score":0.045339346},"labels":[],"label_agreement":null},{"id":"W2016780045","doi":"10.1016/j.rse.2013.03.032","title":"Effects of irradiance and photosynthetic downregulation on the photochemical reflectance index in Douglas-fir and ponderosa pine","year":2013,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":53,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Department of Agriculture; National Aeronautics and Space Administration","keywords":"Photochemical Reflectance Index; Photosynthesis; Canopy; Irradiance; Stomatal conductance; Environmental science; Evergreen; Atmospheric sciences; Xanthophyll; Remote sensing; Botany; Chlorophyll fluorescence; Biology; Geology; Physics; Optics","score_opus":0.003840469426401174,"score_gpt":0.17595051652166485,"score_spread":0.17211004709526367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016780045","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9995127,0.00012758041,0.000063161104,0.00001644303,0.000003022558,0.0000021357894,0.00004680753,0.0000032375958,0.00022499627],"genre_scores_gemma":[0.99921906,0.000055898,0.00007636723,0.00002972168,0.0000023712407,0.000004077223,0.000101146696,0.000004028884,0.00050732325],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998858,0.000026875136,0.00000885648,0.0000309954,0.000014080699,0.00003341738],"domain_scores_gemma":[0.99960643,0.00015292814,0.00005546291,0.000040213097,0.00003448181,0.00011055013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024914605,0.00021429335,0.00041638373,0.00013565253,0.00032249183,0.0002900079,0.00030024713,0.0003443576,0.0010448497],"category_scores_gemma":[0.00037756574,0.0002949352,0.00022094758,0.00014994788,0.0004245876,0.00033277934,0.00022275465,0.0007865551,0.00012614227],"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":[0.010122022,0.00033711168,0.020534229,0.0000471023,0.00004706602,0.00019884593,0.00012879589,0.00055622694,0.96492577,0.00013682847,0.00008961287,0.0028764557],"study_design_scores_gemma":[0.00014101087,0.0015050954,0.88308203,0.000006396401,0.00008551616,0.00024692024,0.00034809348,0.0031112032,0.110782534,0.0001770296,0.0004890052,0.000025179928],"about_ca_topic_score_codex":0.010388903,"about_ca_topic_score_gemma":0.01058579,"teacher_disagreement_score":0.010388903,"about_ca_system_score_codex":0.00067338656,"about_ca_system_score_gemma":0.00030318895,"threshold_uncertainty_score":0.020656884},"labels":[],"label_agreement":null},{"id":"W2017318709","doi":"10.1016/j.rse.2015.04.006","title":"The grazing impacts of four barren ground caribou herds (Rangifer tarandus groenlandicus) on their summer ranges: An application of archived remotely sensed vegetation productivity data","year":2015,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Rangeland Management and Livestock Ecology","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Government of Northwest Territories; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Council","keywords":"Overgrazing; Exclosure; Herd; Vegetation (pathology); Grazing; Environmental science; Productivity; Range (aeronautics); Ecology; Seasonality; Ungulate; Geography; Physical geography; Biology; Habitat","score_opus":0.052455604632751104,"score_gpt":0.2584863789243124,"score_spread":0.2060307742915613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017318709","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9997719,0.000013175942,0.000038809132,0.000005183748,5.202466e-7,0.0000011794925,0.00006991363,0.0000028281122,0.000096492855],"genre_scores_gemma":[0.9992526,0.000034427358,0.0002959682,0.000003963666,0.0000014188093,0.0000041402177,0.0002970603,0.0000021453886,0.00010825927],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998574,0.0000466289,0.000010534498,0.00002691929,0.000025762198,0.00003275063],"domain_scores_gemma":[0.99945074,0.00030887025,0.00008190243,0.00003958462,0.00005559815,0.000063316525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038363758,0.00029003998,0.00021633893,0.0009332831,0.00052953773,0.0007200382,0.00044180747,0.0004413678,0.0006078882],"category_scores_gemma":[0.0009969358,0.00025645277,0.00040092244,0.0008775305,0.0003448433,0.00039676487,0.00036803188,0.0002150097,0.000076413206],"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":[0.0004581751,0.00032236124,0.97422,0.00002963525,0.00023617898,0.00035582605,0.0008103299,0.006896536,0.00418324,0.000090064685,0.00019110493,0.012206561],"study_design_scores_gemma":[0.000015729443,0.00008954033,0.98680264,0.000006739745,0.00010205884,0.000113422226,0.0010895868,0.010933997,0.0005660532,0.000056130408,0.00021343227,0.000010728522],"about_ca_topic_score_codex":0.08220736,"about_ca_topic_score_gemma":0.22658391,"teacher_disagreement_score":0.08220736,"about_ca_system_score_codex":0.0007716116,"about_ca_system_score_gemma":0.00030914813,"threshold_uncertainty_score":0.16345769},"labels":[],"label_agreement":null},{"id":"W2018140562","doi":"10.1016/j.rse.2009.08.018","title":"Estimating forest canopy height and terrain relief from GLAS waveform metrics","year":2009,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":174,"is_retracted":false,"has_abstract":false,"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 Victoria","funders":"","keywords":"Remote sensing; Lidar; Environmental science; Waveform; Altimeter; Canopy; Terrain; Vegetation (pathology); Tree canopy; Biomass (ecology); Forest ecology; Range (aeronautics); Ecosystem; Geology; Computer science; Radar; Geography; Ecology","score_opus":0.00830456948276425,"score_gpt":0.21486603956495723,"score_spread":0.20656147008219297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018140562","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8656527,0.00025576353,0.12589034,0.00007875484,0.000024079694,0.000058829988,0.0018110711,0.0017756482,0.004452858],"genre_scores_gemma":[0.91774625,0.00014298434,0.079284735,0.000025006102,0.000013412211,0.000030185478,0.0017607873,0.000104281266,0.00089224783],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99983346,0.000025118252,0.0000097253,0.000031016843,0.00006948215,0.00003107007],"domain_scores_gemma":[0.99966383,0.00009810542,0.00005383028,0.00004533993,0.000111916765,0.000026949881],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003155055,0.00054754136,0.00034026534,0.0021896046,0.00018885579,0.0006450302,0.00035961473,0.00046797845,0.0010920105],"category_scores_gemma":[0.0013691873,0.00032257332,0.0003103799,0.0013697383,0.00013379482,0.0009381947,0.00047155112,0.00023887104,0.00072408875],"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":[0.00042564,0.00015469443,0.21448898,0.00015960206,0.00017709218,0.0003441643,0.00025995934,0.1167007,0.09019944,0.0012188099,0.0026699286,0.57320094],"study_design_scores_gemma":[0.000043998323,0.00011057772,0.2361461,0.000029183942,0.00011739882,0.0002556849,0.0002606183,0.7376244,0.021916177,0.0017268453,0.0017097424,0.00005919092],"about_ca_topic_score_codex":0.0074809454,"about_ca_topic_score_gemma":0.019688591,"teacher_disagreement_score":0.0074809454,"about_ca_system_score_codex":0.00036293486,"about_ca_system_score_gemma":0.00046921129,"threshold_uncertainty_score":0.014874816},"labels":[],"label_agreement":null},{"id":"W2018454632","doi":"10.1016/j.rse.2007.05.009","title":"Assessing canopy PRI for water stress detection with diurnal airborne imagery","year":2007,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":278,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Photochemical Reflectance Index; Normalized Difference Vegetation Index; Environmental science; Remote sensing; Canopy; Orchard; Multispectral image; Enhanced vegetation index; Vegetation (pathology); Leaf area index; Geology; Geography; Botany; Vegetation Index; Horticulture","score_opus":0.008365137691935637,"score_gpt":0.21891558500579858,"score_spread":0.21055044731386294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018454632","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.965948,0.0002794026,0.030121801,0.00006820158,0.000023762223,0.000043996642,0.00047905886,0.000373329,0.0026624424],"genre_scores_gemma":[0.974606,0.00010127929,0.024373025,0.000031928357,0.000013133371,0.000023152494,0.0004595783,0.000038409293,0.00035356352],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998062,0.00004936162,0.000008735375,0.000056844907,0.000045288867,0.000033618202],"domain_scores_gemma":[0.9992446,0.00034663643,0.000069036454,0.00007634849,0.00019068558,0.00007268694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005956491,0.00040296838,0.00044174894,0.0007119811,0.0002867152,0.00051795307,0.0003799787,0.0006446879,0.0007436838],"category_scores_gemma":[0.001511936,0.00028702262,0.0002451595,0.00042302898,0.00011366353,0.00077946373,0.00029098778,0.00032979914,0.00027635333],"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":[0.0014145337,0.0005981293,0.41841248,0.00036650518,0.00034656856,0.00028367943,0.00045078652,0.025839606,0.30749673,0.0005057991,0.002618172,0.24166697],"study_design_scores_gemma":[0.000036451373,0.0004685094,0.68930626,0.000021991307,0.00016613775,0.00042903222,0.00021699673,0.2714683,0.03631085,0.00042470708,0.0011075432,0.00004313707],"about_ca_topic_score_codex":0.0025746515,"about_ca_topic_score_gemma":0.0072764256,"teacher_disagreement_score":0.0025746515,"about_ca_system_score_codex":0.00014436361,"about_ca_system_score_gemma":0.0002364553,"threshold_uncertainty_score":0.005119264},"labels":[],"label_agreement":null},{"id":"W2019850143","doi":"10.1016/j.rse.2009.03.003","title":"Assessing FPAR source and parameter optimization scheme in application of a diagnostic carbon flux model","year":2009,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Lawrence Livermore National Laboratory; Goddard Space Flight Center; Biological and Environmental Research; National Nuclear Security Administration; National Aeronautics and Space Administration; U.S. Department of Energy","keywords":"Eddy covariance; Environmental science; Biome; Photosynthetically active radiation; SeaWiFS; Ecosystem respiration; Flux (metallurgy); Primary production; Carbon cycle; Remote sensing; Atmospheric sciences; Satellite; Ecosystem; Ecology; Geology","score_opus":0.007812014743913795,"score_gpt":0.20772249707057922,"score_spread":0.19991048232666542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019850143","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69990844,0.00016640943,0.29418758,0.00035075005,0.000034507957,0.00015494062,0.00034131992,0.001171904,0.0036840145],"genre_scores_gemma":[0.9426261,0.000025866108,0.05678213,0.000028680935,0.000004062809,0.000051487474,0.00012732351,0.000060225906,0.00029409974],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970216,0.00015051004,0.000020058209,0.000043612974,0.000059151353,0.000024605473],"domain_scores_gemma":[0.9981173,0.0012599592,0.000092840906,0.0001571565,0.00033548113,0.000037220518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019312524,0.0005854777,0.0005667544,0.00041620497,0.00048328188,0.0006024466,0.00093129295,0.001370732,0.00076584204],"category_scores_gemma":[0.0053984243,0.00029726585,0.00039027372,0.0003833347,0.00027884092,0.0009850266,0.0004898788,0.0006788744,0.000116407275],"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":[0.00029255784,0.00009125369,0.002756447,0.000055589324,0.000029244724,0.00005233571,0.00003636269,0.9687697,0.0047649117,0.0011056028,0.00020609886,0.021839825],"study_design_scores_gemma":[0.000016363128,0.000016076938,0.00031222525,0.0000016724983,0.000006525939,0.000004071955,0.0000035774274,0.9980757,0.0013962865,0.00012853218,0.000035768528,0.0000030901585],"about_ca_topic_score_codex":0.01256324,"about_ca_topic_score_gemma":0.006003097,"teacher_disagreement_score":0.01256324,"about_ca_system_score_codex":0.00079326745,"about_ca_system_score_gemma":0.0008189295,"threshold_uncertainty_score":0.024980187},"labels":[],"label_agreement":null},{"id":"W2022349500","doi":"10.1016/j.rse.2015.02.024","title":"A new satellite-based monthly precipitation downscaling algorithm with non-stationary relationship between precipitation and land surface characteristics","year":2015,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":195,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Geospatial-Intelligence Agency; Chinese Academy of Sciences; National Natural Science Foundation of China; National Aeronautics and Space Administration","keywords":"Downscaling; Precipitation; Normalized Difference Vegetation Index; Environmental science; Quantitative precipitation estimation; Satellite; Remote sensing; Algorithm; Climatology; Rain gauge; Vegetation (pathology); Meteorology; Computer science; Climate change; Geology; Geography","score_opus":0.0328635121938099,"score_gpt":0.21871706943691357,"score_spread":0.18585355724310368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022349500","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021114916,0.00033265777,0.9747867,0.000081983235,0.0002500153,0.000079984864,0.00036551795,0.0020169432,0.00097125286],"genre_scores_gemma":[0.06859062,0.00025154714,0.9243734,0.00011767827,0.00017953121,0.00016267005,0.0019457994,0.00023192953,0.004146769],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996538,0.000026799804,0.00002595748,0.00010785027,0.00015993505,0.00002568959],"domain_scores_gemma":[0.9996371,0.000039049064,0.000030330255,0.000048087866,0.00022382062,0.000021578026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004642429,0.00062340195,0.0010152168,0.0009768923,0.00043757836,0.0005596611,0.0011521605,0.0005218439,0.002018719],"category_scores_gemma":[0.00081018853,0.00046597797,0.000681946,0.0014408669,0.00015560236,0.00093974243,0.0007209339,0.0006413605,0.0012244806],"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":[0.00020271672,0.00016105594,0.0037580936,0.000094102266,0.0001804653,0.00006400616,0.00003530629,0.037280932,0.051056053,0.0013383133,0.010295012,0.89553404],"study_design_scores_gemma":[0.0000833955,0.000057087113,0.0059199296,0.000006898361,0.00006822042,0.00012967706,0.000012960271,0.9723803,0.0111135915,0.00078703515,0.009404592,0.000036319045],"about_ca_topic_score_codex":0.005093036,"about_ca_topic_score_gemma":0.009239269,"teacher_disagreement_score":0.005093036,"about_ca_system_score_codex":0.00021410751,"about_ca_system_score_gemma":0.0009980677,"threshold_uncertainty_score":0.01012677},"labels":[],"label_agreement":null},{"id":"W2023519189","doi":"10.1016/j.rse.2010.08.023","title":"The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficienciesA review and meta-analysis","year":2010,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":634,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Photochemical Reflectance Index; Environmental science; Normalized Difference Vegetation Index; Canopy; Biomass (ecology); Ecosystem; Remote sensing; Leaf area index; Vegetation (pathology); Spectroradiometer; Photosynthetic capacity; Photosynthetically active radiation; Atmospheric sciences; Photosynthesis; Ecology; Reflectivity; Biology; Geography; Botany","score_opus":0.015567842051301722,"score_gpt":0.23376918914105574,"score_spread":0.21820134708975403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023519189","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011219395,0.9803237,0.006513162,0.0002515412,0.000092231916,0.000025181567,0.000661649,0.000046670884,0.0008664949],"genre_scores_gemma":[0.11886705,0.8637686,0.014789736,0.00039051808,0.00034697776,0.00008424366,0.0012136392,0.000053901334,0.00048537075],"study_design_codex":"design_other","study_design_gemma":"meta_analysis","domain_scores_codex":[0.99834657,0.00041751697,0.00017700998,0.00062357984,0.00039050812,0.00004487048],"domain_scores_gemma":[0.9937308,0.0044714105,0.0007202228,0.00037052998,0.0006545225,0.00005254371],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055568824,0.0022289676,0.004993909,0.0045522265,0.0002488576,0.0022411793,0.0014412024,0.00084246084,0.0013520393],"category_scores_gemma":[0.006345966,0.00081679726,0.0040053045,0.00982452,0.00057379546,0.002046456,0.0007819963,0.00081769354,0.00034425783],"study_design_candidate":"meta_analysis","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":[0.00045010413,0.00012877377,0.03874633,0.040308688,0.023485914,0.00014743583,0.00019808313,0.005699938,0.007873235,0.0015471742,0.002808515,0.8786058],"study_design_scores_gemma":[0.00030562823,0.0018979667,0.44609568,0.0346501,0.16969153,0.003814954,0.0010471486,0.035396893,0.03246039,0.015894042,0.25775543,0.0009901808],"about_ca_topic_score_codex":0.0064345347,"about_ca_topic_score_gemma":0.007020044,"teacher_disagreement_score":0.0064345347,"about_ca_system_score_codex":0.0010578139,"about_ca_system_score_gemma":0.0017011913,"threshold_uncertainty_score":0.02938795},"labels":[],"label_agreement":null},{"id":"W2023967740","doi":"10.1016/j.rse.2014.10.008","title":"Corrigendum to “Mapping maximum urban air temperature on hot summer days” [Remote Sensing of Environment 154 (2014) 38–45]","year":2014,"lang":"en","type":"erratum","venue":"Remote Sensing of Environment","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Centre for Disease Control; University of British Columbia; Simon Fraser University","funders":"","keywords":"Remote sensing; Environmental science; Meteorology; Air temperature; Geography","score_opus":0.016310355691994252,"score_gpt":0.20820703248496528,"score_spread":0.191896676792971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023967740","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011631502,0.0014974817,0.0011014361,0.026682269,0.9583468,0.00007817583,0.0027018315,0.000840455,0.008635194],"genre_scores_gemma":[0.0041002007,0.008463737,0.0056713987,0.089618914,0.22246787,0.00043088512,0.009393176,0.0027973512,0.6570565],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99560565,0.0005252593,0.0006632412,0.00060117466,0.0021990077,0.00040570294],"domain_scores_gemma":[0.9760276,0.0020775294,0.0007698503,0.0011932126,0.019213952,0.0007178415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002799332,0.0039248755,0.0036648333,0.006642755,0.004331808,0.004534205,0.004847838,0.0064981095,0.09623524],"category_scores_gemma":[0.026339555,0.0017933206,0.0030307553,0.0047512073,0.0017371716,0.0026856202,0.0031876764,0.0071926136,0.07555331],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010580005,0.00000950218,0.00003654792,0.00004782918,0.0000045974307,0.00003482379,0.0000047889307,0.000046778772,0.000035350633,0.00011574292,0.99660254,0.0030509601],"study_design_scores_gemma":[0.000029572995,0.000033612407,0.0018442249,0.00020461314,0.000040344315,0.0000959652,0.00005946875,0.000504284,0.00032604916,0.0007731477,0.99603784,0.00005079409],"about_ca_topic_score_codex":0.15251872,"about_ca_topic_score_gemma":0.19925253,"teacher_disagreement_score":0.15251872,"about_ca_system_score_codex":0.005262979,"about_ca_system_score_gemma":0.0075025135,"threshold_uncertainty_score":0.321939},"labels":[],"label_agreement":null},{"id":"W2024662065","doi":"10.1016/j.rse.2015.01.012","title":"Comparing land surface phenology with leafing and flowering observations from the PlantWatch citizen network","year":2015,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":78,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Phenology; Vegetation (pathology); Boreal; Geography; Remote sensing; Woody plant; Environmental science; Physical geography; Biology; Botany; Archaeology","score_opus":0.032856680099363614,"score_gpt":0.19436962265258514,"score_spread":0.16151294255322152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024662065","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99334043,0.00002505727,0.00035303374,0.00003759953,0.000006356792,0.000010564168,0.0055734944,0.00003576045,0.00061768905],"genre_scores_gemma":[0.9748941,0.000035259593,0.0016439502,0.000036631463,0.000010920121,0.00003721947,0.022636732,0.000024436093,0.0006807674],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999678,0.00007347622,0.000012534673,0.00011479214,0.00006342895,0.00005785217],"domain_scores_gemma":[0.9994288,0.00015311694,0.00010023484,0.00008364487,0.00015438734,0.000079817604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049852283,0.00018612403,0.0002573058,0.0007450855,0.0002196983,0.00031652322,0.0003376067,0.00036677098,0.0007579052],"category_scores_gemma":[0.0012254308,0.00013995226,0.00028333985,0.0012011,0.00011915713,0.00049035513,0.00047331935,0.00021687507,0.00040635865],"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":[0.00061147206,0.00023966515,0.961188,0.00006733326,0.0002723081,0.00008946397,0.00042251326,0.004601016,0.007935587,0.0001667042,0.0044144234,0.01999149],"study_design_scores_gemma":[0.000021952834,0.000048774913,0.98583883,0.0000072135635,0.000035889057,0.000035214038,0.00039111037,0.011152995,0.0006374882,0.000062113126,0.0017584399,0.000009911304],"about_ca_topic_score_codex":0.06900283,"about_ca_topic_score_gemma":0.16467203,"teacher_disagreement_score":0.06900283,"about_ca_system_score_codex":0.0005000884,"about_ca_system_score_gemma":0.00038328627,"threshold_uncertainty_score":0.13720238},"labels":[],"label_agreement":null},{"id":"W2024695456","doi":"10.1016/j.rse.2009.08.015","title":"Landsat-based inventory of glaciers in western Canada, 1985–2005","year":2009,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":584,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Glacier; Thematic Mapper; Geology; Physical geography; Snow; Glacier mass balance; Altitude (triangle); Aerial photography; Glacier morphology; Debris; Snow line; Climatology; Snow cover; Remote sensing; Satellite imagery; Geography; Oceanography; Cryosphere; Ice stream; Geomorphology; Sea ice","score_opus":0.013056945259723503,"score_gpt":0.18526210938357676,"score_spread":0.17220516412385325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024695456","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79050374,0.004931809,0.0005884266,0.0004938104,0.00006183244,0.00009224383,0.19286224,0.00023103584,0.010234892],"genre_scores_gemma":[0.8931856,0.0038239653,0.0018571101,0.00019048044,0.000021556376,0.000054622036,0.08361345,0.000035675304,0.017217565],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99961734,0.000012463372,0.00004058866,0.000060336937,0.00017995595,0.0000892584],"domain_scores_gemma":[0.9977449,0.000035819096,0.0002585804,0.000039632774,0.0016836092,0.00023744631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003307324,0.0005937435,0.00038050703,0.0036506427,0.0018147852,0.0009337741,0.0011457262,0.00024760937,0.0017790425],"category_scores_gemma":[0.000804693,0.00032803623,0.0003902724,0.007927697,0.00041332518,0.00044337014,0.00048193146,0.0003997857,0.00047164125],"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":[0.0003687457,0.00009135953,0.90123975,0.00057441613,0.0005723819,0.000388969,0.0017661039,0.0039336006,0.002104796,0.000948814,0.04071332,0.047297634],"study_design_scores_gemma":[0.000010356951,0.000009011525,0.9811412,0.00009776552,0.000078609715,0.000097195094,0.000662058,0.0011615398,0.00035033474,0.00003806407,0.016337356,0.000016613729],"about_ca_topic_score_codex":0.9984713,"about_ca_topic_score_gemma":0.9994468,"teacher_disagreement_score":0.031485897,"about_ca_system_score_codex":0.031485897,"about_ca_system_score_gemma":0.045439783,"threshold_uncertainty_score":0.22844726},"labels":[],"label_agreement":null},{"id":"W2025299137","doi":"10.1016/j.rse.2013.01.006","title":"Evaluation of polarimetric Radarsat-2 SAR data for development of soil moisture retrieval algorithms over a chronosequence of black spruce boreal forests","year":2013,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Natural Resources Canada; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency; National Aeronautics and Space Administration","keywords":"Chronosequence; Polarimetry; Remote sensing; Taiga; Environmental science; Black spruce; Water content; Backscatter (email); Moisture; Mean squared error; Synthetic aperture radar; Algorithm; Soil science; Meteorology; Scattering; Mathematics; Geology; Computer science; Geography; Physics; Soil water; Optics; Forestry; Statistics","score_opus":0.031069837239194417,"score_gpt":0.26489419195252434,"score_spread":0.2338243547133299,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025299137","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9941854,0.000111174864,0.00424985,0.000029198705,0.000011537265,0.000034887235,0.00051498367,0.00013574847,0.00072729],"genre_scores_gemma":[0.9833823,0.000084358944,0.014681151,0.00001648847,0.000008896024,0.000018581832,0.0014671157,0.000025631485,0.00031541233],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997962,0.00004643926,0.000023348677,0.000041671577,0.00007000793,0.000022315742],"domain_scores_gemma":[0.9989073,0.0004164833,0.00008266523,0.00008260486,0.0004362945,0.000074621865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014155639,0.00045334361,0.0002093784,0.00070260314,0.0003201102,0.0005301183,0.00044759054,0.00038948635,0.0004893545],"category_scores_gemma":[0.0017661453,0.0002121866,0.00025324352,0.0004893776,0.00014898146,0.0005491734,0.00018145426,0.00018334456,0.00014676453],"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":[0.002724444,0.0017048657,0.2526002,0.00032432488,0.00044178954,0.0007373759,0.0003864889,0.3231385,0.20400634,0.0007755707,0.0016539922,0.21150619],"study_design_scores_gemma":[0.0002747124,0.00082785013,0.30977207,0.000025250833,0.0002447718,0.00016417599,0.0001925541,0.6332478,0.0531845,0.00016739125,0.00185466,0.000044239274],"about_ca_topic_score_codex":0.019177219,"about_ca_topic_score_gemma":0.02791771,"teacher_disagreement_score":0.019177219,"about_ca_system_score_codex":0.0003780235,"about_ca_system_score_gemma":0.00061272946,"threshold_uncertainty_score":0.038131237},"labels":[],"label_agreement":null},{"id":"W2027824608","doi":"10.1016/j.rse.2008.01.001","title":"The contribution of AMSR-E 18.7 and 10.7 GHz measurements to improved boreal forest snow water equivalent retrievals","year":2008,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":138,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada","funders":"Suomen Ympäristökeskus; Manitoba Hydro","keywords":"Snow; Environmental science; Taiga; Boreal; Water equivalent; Brightness temperature; Vegetation (pathology); Radiometer; Physical geography; Remote sensing; Range (aeronautics); Climatology; Atmospheric sciences; Brightness; Meteorology; Geography; Forestry; Geology; Medicine","score_opus":0.040026910263812075,"score_gpt":0.21970062218869105,"score_spread":0.17967371192487896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027824608","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93489856,0.0027925887,0.04722558,0.00091029506,0.0005901487,0.000039853287,0.0038213357,0.0015009188,0.008220745],"genre_scores_gemma":[0.9364934,0.0010208897,0.055946734,0.00026608742,0.00042732034,0.000021406684,0.0035922437,0.00025654666,0.0019753082],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99946564,0.0001064175,0.000027126644,0.00014097357,0.00015781922,0.00010194328],"domain_scores_gemma":[0.999154,0.00026193773,0.000074926065,0.00011480312,0.00035792062,0.00003639662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009258329,0.0008100677,0.00048818442,0.00062693824,0.00026684804,0.00069173594,0.0005526411,0.0005199755,0.0017966408],"category_scores_gemma":[0.0024284972,0.00036181626,0.00034100088,0.00066226907,0.00019861701,0.0014502137,0.00039094882,0.000629917,0.0006470463],"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":[0.0017586747,0.0002430189,0.1303661,0.0005467968,0.0004440109,0.00016200566,0.00027295927,0.034570567,0.3806889,0.0016291164,0.0074689197,0.44184887],"study_design_scores_gemma":[0.00048481082,0.0004573527,0.58546793,0.00013549408,0.0011886109,0.00046685737,0.0002286058,0.24218914,0.11489659,0.002420291,0.051810995,0.00025330723],"about_ca_topic_score_codex":0.011012725,"about_ca_topic_score_gemma":0.032403927,"teacher_disagreement_score":0.011012725,"about_ca_system_score_codex":0.00025578367,"about_ca_system_score_gemma":0.0006005172,"threshold_uncertainty_score":0.021897256},"labels":[],"label_agreement":null},{"id":"W2028901390","doi":"10.1016/j.rse.2014.10.004","title":"Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data","year":2014,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":386,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Ministère des Affaires Etrangères; Agence Nationale de la Recherche","keywords":"Lidar; Canopy; Remote sensing; Basal area; Environmental science; Metric (unit); Range (aeronautics); Deciduous; Understory; Forest inventory; Robustness (evolution); Leaf area index; Computer science; Mathematics; Forest management; Geography; Forestry; Ecology","score_opus":0.060938058434659825,"score_gpt":0.23865939216755216,"score_spread":0.17772133373289234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028901390","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4289782,0.000549729,0.5660735,0.00026686245,0.000062732455,0.00008843142,0.00078577973,0.0013059855,0.0018889161],"genre_scores_gemma":[0.9617334,0.00023609982,0.035772618,0.00005336042,0.000050450428,0.000068907204,0.0007983042,0.00008483932,0.001201982],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970335,0.00008259008,0.000017674203,0.00010108566,0.00005799549,0.000037292248],"domain_scores_gemma":[0.9988017,0.0008246512,0.00008274751,0.00012369017,0.00014085762,0.000026332675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014900664,0.00074096443,0.0008242045,0.0010562012,0.00033268958,0.0009667579,0.00147198,0.00086037425,0.000925473],"category_scores_gemma":[0.0030364345,0.0007249069,0.0011716568,0.0013742187,0.00037105384,0.001463802,0.0005978058,0.0010248843,0.0002815102],"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":[0.0000134495795,0.00002562628,0.0014764298,0.000008364815,0.000025962276,0.000019692152,0.0000113783935,0.98686963,0.00027663028,0.00032190242,0.00013335251,0.010817601],"study_design_scores_gemma":[9.588329e-7,0.0000023418006,0.00024660272,7.7172336e-7,0.0000029613243,0.0000025682393,0.0000022772203,0.99926287,0.00004037078,0.00041247133,0.000024676563,0.0000011181107],"about_ca_topic_score_codex":0.03617929,"about_ca_topic_score_gemma":0.034381963,"teacher_disagreement_score":0.03617929,"about_ca_system_score_codex":0.0008365142,"about_ca_system_score_gemma":0.00080485025,"threshold_uncertainty_score":0.07193744},"labels":[],"label_agreement":null},{"id":"W2029767925","doi":"10.1016/j.rse.2012.12.018","title":"The longwave infrared (3–14μm) spectral properties of rock encrusting lichens based on laboratory spectra and airborne SEBASS imagery","year":2013,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Lichen; Endmember; Remote sensing; Hyperspectral imaging; Longwave; Spectral signature; Geology; Environmental science; Radiative transfer; Physics; Ecology","score_opus":0.00977160308543757,"score_gpt":0.17092983087849745,"score_spread":0.16115822779305988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029767925","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9980124,0.000043856562,0.00064698496,0.0000062947656,0.0000011763012,0.0000027954545,0.00016122723,0.00003459619,0.0010906646],"genre_scores_gemma":[0.9983583,0.000039416544,0.00092087995,0.0000071292397,0.0000013401515,0.0000033843176,0.0002649146,0.000007071305,0.00039754194],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9999609,0.000003838655,0.0000026111304,0.000010593097,0.000011967933,0.000010109854],"domain_scores_gemma":[0.9998586,0.00003001833,0.000029671519,0.000008686601,0.00004540918,0.000027525517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010997436,0.00013845025,0.00009232671,0.0005942862,0.00021140931,0.00028817615,0.00014791757,0.000117396594,0.0013568605],"category_scores_gemma":[0.00015480514,0.00009613394,0.00008731821,0.00043508175,0.00018513075,0.00034125926,0.00014247574,0.00014722947,0.00026437393],"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":[0.00058694935,0.00017237263,0.47151104,0.000103379156,0.000048183272,0.0001806946,0.00047227918,0.0025358568,0.47814563,0.00021386714,0.0005864999,0.04544339],"study_design_scores_gemma":[0.0000070939277,0.00004432509,0.97445357,0.000005446033,0.00002052567,0.00010417674,0.00035546394,0.00320041,0.021330465,0.00004096501,0.00043035162,0.0000071401223],"about_ca_topic_score_codex":0.007433616,"about_ca_topic_score_gemma":0.023272742,"teacher_disagreement_score":0.007433616,"about_ca_system_score_codex":0.00014567035,"about_ca_system_score_gemma":0.00011986413,"threshold_uncertainty_score":0.0147807},"labels":[],"label_agreement":null},{"id":"W2030139686","doi":"10.1016/s0034-4257(00)00099-7","title":"New Capabilities of the “SHOALS” Airborne Lidar Bathymeter","year":2000,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":79,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Optech (Canada)","funders":"","keywords":"Bathymetry; Shoal; Remote sensing; Lidar; Global Positioning System; Shore; Underwater; Geology; Boundary (topology); Real Time Kinematic; Environmental science; Computer science; Oceanography; GNSS applications","score_opus":0.007391216403594211,"score_gpt":0.19702829920103668,"score_spread":0.18963708279744246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030139686","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19339292,0.006480098,0.6552096,0.007492432,0.0027279563,0.00019463194,0.0025932617,0.0068175923,0.12509151],"genre_scores_gemma":[0.58766353,0.003148435,0.37322134,0.0021971005,0.0014459138,0.000119609824,0.002357799,0.0005454239,0.029300818],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991047,0.00012062653,0.000028970062,0.0001188824,0.00055174134,0.000075114105],"domain_scores_gemma":[0.99912184,0.00013372033,0.000049481147,0.00023342112,0.00039708518,0.00006446989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007996758,0.00042247545,0.0003035534,0.00062585913,0.00032697772,0.0011634495,0.00089838775,0.00058921304,0.0051934267],"category_scores_gemma":[0.0014973618,0.00033779067,0.00036716054,0.00057699904,0.00044272293,0.0019979721,0.0020993203,0.00088929717,0.0021712729],"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":[0.0004089025,0.00009068668,0.007268705,0.00030719762,0.00006161488,0.00022430466,0.00062349625,0.004428952,0.28524846,0.032233384,0.025962682,0.6431416],"study_design_scores_gemma":[0.00015925597,0.0004609668,0.019454522,0.0001939342,0.00017066521,0.0021154697,0.0004713294,0.072916806,0.102183424,0.01595322,0.7856874,0.00023308088],"about_ca_topic_score_codex":0.001958764,"about_ca_topic_score_gemma":0.004381621,"teacher_disagreement_score":0.0051934267,"about_ca_system_score_codex":0.00024012239,"about_ca_system_score_gemma":0.00064433744,"threshold_uncertainty_score":0.017373681},"labels":[],"label_agreement":null},{"id":"W2031779014","doi":"10.1016/j.rse.2008.04.013","title":"Application of satellite remote sensing techniques for estimating air–sea CO2 fluxes in Hudson Bay, Canada during the ice-free season","year":2008,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary; University of Manitoba","funders":"Canada Research Chairs","keywords":"Bay; Environmental science; Sink (geography); Extrapolation; Remote sensing; Satellite; Sea surface temperature; Sea ice; Wind speed; Meteorology; Climatology; Oceanography; Geology; Geography","score_opus":0.007804491806923757,"score_gpt":0.1821971036767859,"score_spread":0.17439261186986216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031779014","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9973239,0.00023365882,0.00016328046,0.000039098242,0.000008622235,0.000013586534,0.0010367396,0.000017403927,0.0011636096],"genre_scores_gemma":[0.99707437,0.00020691878,0.00074397086,0.000020722142,0.000003447122,0.000008411991,0.00094254006,0.000007838759,0.0009918252],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998047,0.000016031738,0.000014801715,0.000049106635,0.000063594416,0.000051795963],"domain_scores_gemma":[0.99944633,0.00008783893,0.000050945557,0.0000150651085,0.00033513398,0.000064555934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004991802,0.0004978414,0.00034478563,0.0014125481,0.001311584,0.0010387691,0.00072614144,0.00033044588,0.00076394866],"category_scores_gemma":[0.0009962958,0.0003054881,0.0002871507,0.0018716297,0.00042285238,0.00024003357,0.0003175533,0.0002808513,0.00012212404],"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":[0.00048338945,0.00014421101,0.93443227,0.00012677794,0.00022854879,0.00032313765,0.0011077934,0.01789818,0.0072270357,0.00019877066,0.0016526937,0.036177073],"study_design_scores_gemma":[0.000029380948,0.000020348582,0.98368603,0.000022501239,0.00005092798,0.000036668407,0.0009693688,0.012513066,0.0012670505,0.000037212332,0.0013500673,0.000017254077],"about_ca_topic_score_codex":0.9915513,"about_ca_topic_score_gemma":0.99455196,"teacher_disagreement_score":0.010289025,"about_ca_system_score_codex":0.010289025,"about_ca_system_score_gemma":0.010228795,"threshold_uncertainty_score":0.07465249},"labels":[],"label_agreement":null},{"id":"W2034537637","doi":"10.1016/j.rse.2011.02.015","title":"The effect of the temporal resolution of NDVI data on season onset dates and trends across Canadian broadleaf forests","year":2011,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":89,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta; McGill University","funders":"","keywords":"Normalized Difference Vegetation Index; Advanced very-high-resolution radiometer; Environmental science; Phenology; Satellite; Remote sensing; Temporal resolution; Growing season; Climatology; Scale (ratio); Climate change; Geography; Geology; Cartography","score_opus":0.016808874610178732,"score_gpt":0.23014985701876275,"score_spread":0.213340982408584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034537637","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9918168,0.0012172451,0.00040184936,0.000733548,0.000038028793,0.000013356735,0.002445855,0.000022137534,0.0033111419],"genre_scores_gemma":[0.99752814,0.00022450673,0.00028407987,0.00008941561,0.000008592633,0.0000041748112,0.0008219503,0.000015539925,0.0010236604],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978123,0.00033581327,0.00013600482,0.00057734177,0.00047833356,0.00066029606],"domain_scores_gemma":[0.9842276,0.0062756045,0.0015204181,0.0006879119,0.0061222496,0.001166172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003903655,0.00037233552,0.00038518754,0.0012266646,0.0020961212,0.002192372,0.0013658219,0.0005955154,0.0020105862],"category_scores_gemma":[0.015749848,0.0005002548,0.0006424669,0.0022926324,0.0011390911,0.001010387,0.0007153521,0.0008415996,0.00015042133],"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":[0.0003890163,0.000031348445,0.9810253,0.00006495853,0.00035535273,0.00010511,0.0011336848,0.0022550682,0.0036167023,0.0004143514,0.0012262616,0.009382735],"study_design_scores_gemma":[0.000003153903,0.000005678888,0.9983595,0.00000797683,0.000036734593,0.000012629323,0.00030238397,0.0005910522,0.00012999421,0.000023211474,0.00052187033,0.000005819578],"about_ca_topic_score_codex":0.98511314,"about_ca_topic_score_gemma":0.9935608,"teacher_disagreement_score":0.014886856,"about_ca_system_score_codex":0.013649901,"about_ca_system_score_gemma":0.016941823,"threshold_uncertainty_score":0.09903747},"labels":[],"label_agreement":null},{"id":"W2035792042","doi":"10.1016/j.rse.2011.08.017","title":"Predicting satellite-derived patterns of large-scale disturbances in forests of the Pacific Northwest Region in response to recent climatic variation","year":2011,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Environmental science; Ecoregion; Disturbance (geology); Range (aeronautics); Climate change; Scale (ratio); Satellite; Physical geography; Climatology; Atmospheric sciences; Ecology; Geography; Geology","score_opus":0.01101195597557531,"score_gpt":0.2013269983102726,"score_spread":0.1903150423346973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035792042","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9995265,0.00002719777,0.00016491272,0.000015332735,0.0000027882165,0.0000018450863,0.00015614471,0.0000072162375,0.00009803068],"genre_scores_gemma":[0.99931645,0.000030750634,0.00019926896,0.0000057557536,0.0000036887761,0.0000026717848,0.00035763878,0.0000022855668,0.00008148725],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999167,0.000016705199,0.0000081897215,0.000029803874,0.000011740311,0.000016941603],"domain_scores_gemma":[0.999169,0.00038917968,0.00016958159,0.000048388305,0.00009083682,0.00013292649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050519407,0.00026280773,0.0001861938,0.00061281037,0.00024253814,0.0005482665,0.00030682815,0.00037760823,0.000591376],"category_scores_gemma":[0.0015918002,0.00023943356,0.0003177168,0.0006692816,0.00024840742,0.0003350098,0.00023464335,0.00032443894,0.000104383005],"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":[0.00017038103,0.00010375499,0.96843576,0.00001377783,0.000121443656,0.000072895426,0.00007352687,0.023646567,0.0013893654,0.000047312245,0.00023136048,0.005693908],"study_design_scores_gemma":[0.0000161316,0.00002748213,0.9307928,0.0000050947583,0.000028717483,0.00004905503,0.00016559752,0.06844106,0.00027230906,0.00008263606,0.00011273982,0.000006511773],"about_ca_topic_score_codex":0.07108883,"about_ca_topic_score_gemma":0.12364052,"teacher_disagreement_score":0.07108883,"about_ca_system_score_codex":0.00059541955,"about_ca_system_score_gemma":0.00041190092,"threshold_uncertainty_score":0.14135009},"labels":[],"label_agreement":null},{"id":"W2036821603","doi":"10.1016/j.rse.2008.01.013","title":"Estimating chlorophyll concentration in conifer needles with hyperspectral data: An assessment at the needle and canopy level","year":2008,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":145,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Forest Research Institute; York University","funders":"York University","keywords":"Hyperspectral imaging; Canopy; Remote sensing; Environmental science; Atmospheric radiative transfer codes; Tree canopy; Leaf area index; Radiative transfer; Vegetation (pathology); Photochemical Reflectance Index; Botany; Normalized Difference Vegetation Index; Geology; Biology","score_opus":0.03383126152179899,"score_gpt":0.25809370202902837,"score_spread":0.22426244050722938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036821603","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9951975,0.00008428685,0.0042248894,0.0000137870065,0.0000021882806,0.0000064664882,0.000056947905,0.00001762084,0.00039642295],"genre_scores_gemma":[0.9943553,0.00008203113,0.0051420936,0.000014903162,0.0000029285957,0.000005362504,0.00016734094,0.000004520041,0.00022539994],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99987674,0.000017651799,0.0000043167215,0.000034768153,0.000051993953,0.000014510504],"domain_scores_gemma":[0.999775,0.000079710815,0.000025863068,0.000013049118,0.0000849484,0.000021360986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032858585,0.00019876556,0.00015449783,0.00021653248,0.00026267546,0.00030587922,0.0002103158,0.00028174193,0.00022405056],"category_scores_gemma":[0.00041842717,0.000154172,0.000105103536,0.00023121727,0.00016786372,0.00040905748,0.00015442359,0.00018652165,0.00007010804],"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":[0.00034945973,0.00017132495,0.5014027,0.00006540745,0.00007036294,0.000098368626,0.00021192357,0.008943861,0.4371046,0.00017880481,0.00018806462,0.05121504],"study_design_scores_gemma":[0.000019558258,0.00013474532,0.8768315,0.0000075009543,0.000066551904,0.00013813212,0.00020924049,0.060639463,0.06143922,0.00015416674,0.00034151235,0.000018509407],"about_ca_topic_score_codex":0.015826425,"about_ca_topic_score_gemma":0.04042084,"teacher_disagreement_score":0.015826425,"about_ca_system_score_codex":0.0002782662,"about_ca_system_score_gemma":0.000261901,"threshold_uncertainty_score":0.03146863},"labels":[],"label_agreement":null},{"id":"W2037087060","doi":"10.1016/j.rse.2011.04.030","title":"Monitoring agricultural soil moisture extremes in Canada using passive microwave remote sensing","year":2011,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":57,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph; Agriculture and Agri-Food Canada","funders":"Canadian Space Agency; Vrije Universiteit Amsterdam","keywords":"Environmental science; Water content; Remote sensing; Satellite; Moisture; Microwave; Soil science; Data assimilation; Meteorology; Geology; Geography; Computer science","score_opus":0.019384630032701092,"score_gpt":0.1944301266440926,"score_spread":0.17504549661139152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037087060","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9973054,0.0001309044,0.0002904961,0.00009396258,0.000003883358,0.000014529848,0.0005959664,0.000036242247,0.0015286669],"genre_scores_gemma":[0.99825567,0.000087271066,0.00043629418,0.000024197117,0.000002243448,0.00000493491,0.0003385705,0.000004277702,0.00084662327],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99981743,0.000009648303,0.0000059680324,0.00003385121,0.00007314044,0.000060003305],"domain_scores_gemma":[0.9996038,0.000036855516,0.00003926689,0.000008726388,0.00024134656,0.00006999112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024556022,0.00025871486,0.00023822715,0.0007342607,0.0016927425,0.0007097418,0.00059025176,0.00028832673,0.00050402887],"category_scores_gemma":[0.000491325,0.00016236334,0.00012648145,0.0012363561,0.00035534106,0.00027903155,0.00031116477,0.00036141634,0.000086363],"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":[0.0007236892,0.00031394925,0.8834301,0.000093684896,0.00016313915,0.0005296133,0.0012126358,0.012940611,0.026380146,0.0005029455,0.0035426023,0.07016672],"study_design_scores_gemma":[0.000045860023,0.00003626484,0.9808482,0.000009296828,0.000036481328,0.00004316857,0.0007226211,0.013582357,0.00216673,0.00006233638,0.002419865,0.000026830214],"about_ca_topic_score_codex":0.99120367,"about_ca_topic_score_gemma":0.996406,"teacher_disagreement_score":0.0124744475,"about_ca_system_score_codex":0.0124744475,"about_ca_system_score_gemma":0.008623179,"threshold_uncertainty_score":0.09050888},"labels":[],"label_agreement":null},{"id":"W2037382110","doi":"10.1016/j.rse.2007.03.021","title":"Inversion of a passive microwave snow emission model for water equivalent estimation using airborne and satellite data","year":2007,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Snow; Remote sensing; Special sensor microwave/imager; Environmental science; Inversion (geology); Water equivalent; Brightness temperature; Satellite; Meteorology; Microwave; Geology; Computer science","score_opus":0.06161931462086222,"score_gpt":0.253336350929458,"score_spread":0.19171703630859577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037382110","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85409105,0.00012054931,0.14064461,0.00018715218,0.000059527312,0.000037452177,0.00071424124,0.0009846068,0.00316083],"genre_scores_gemma":[0.9657386,0.00004875955,0.032129794,0.000024458755,0.000018009594,0.000023402494,0.00072737655,0.000063071864,0.0012267067],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99994695,0.000009810433,0.0000029323353,0.0000139446065,0.000016690783,0.000009606572],"domain_scores_gemma":[0.9998988,0.000031985568,0.000012402004,0.000016447717,0.0000317432,0.000008671389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018612528,0.00035449347,0.0003046435,0.00027184913,0.00021889289,0.00030379312,0.0004709963,0.0004452169,0.0007868561],"category_scores_gemma":[0.00055428594,0.00034371164,0.00044531922,0.0003428841,0.00014032201,0.0006047454,0.0002850178,0.00035729748,0.00023575878],"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":[0.0002495285,0.00018477204,0.007646852,0.00004446547,0.000095259,0.0001026154,0.00006398608,0.8925557,0.0384898,0.001215083,0.0009922661,0.058359675],"study_design_scores_gemma":[0.00003746521,0.000014849812,0.0018259573,0.0000013239179,0.000011831684,0.0000064204196,0.00000977937,0.9952306,0.002346617,0.00026605668,0.00024435532,0.0000047692033],"about_ca_topic_score_codex":0.0184509,"about_ca_topic_score_gemma":0.019772854,"teacher_disagreement_score":0.0184509,"about_ca_system_score_codex":0.00032317944,"about_ca_system_score_gemma":0.00063781854,"threshold_uncertainty_score":0.036687016},"labels":[],"label_agreement":null},{"id":"W2037615034","doi":"10.1016/j.rse.2011.11.012","title":"Remote sensing of canopy light use efficiency in temperate and boreal forests of North America using MODIS imagery","year":2011,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":56,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Environment and Climate Change Canada; Centre de Géomatique du Québec; McMaster University; University of Toronto","funders":"Oak Ridge National Laboratory; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Photosynthetically active radiation; Environmental science; Moderate-resolution imaging spectroradiometer; Enhanced vegetation index; Temperate forest; Remote sensing; Taiga; Canopy; Temperate rainforest; Spectroradiometer; Vegetation (pathology); Temperate climate; Boreal; Evergreen; Mean squared error; Leaf area index; Atmospheric sciences; Ecosystem; Normalized Difference Vegetation Index; Reflectivity; Forestry; Geography; Mathematics; Ecology; Statistics; Vegetation Index; Geology","score_opus":0.015894772477801345,"score_gpt":0.20297380282668762,"score_spread":0.18707903034888626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037615034","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99837327,0.0001374753,0.00030979118,0.00001838724,0.0000034275688,0.000007970628,0.000564923,0.000032204138,0.0005524122],"genre_scores_gemma":[0.99675083,0.00009997666,0.0018598769,0.000019211138,0.000004808321,0.000017129521,0.00097512867,0.000007464025,0.00026566407],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99987245,0.000017045026,0.0000127942385,0.000046289057,0.000031543615,0.000019836032],"domain_scores_gemma":[0.9996817,0.00006886343,0.00006173337,0.000026695352,0.00011431123,0.0000467851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044353525,0.0002984836,0.000293098,0.00071262766,0.00046840508,0.0003341951,0.00023915955,0.00017134643,0.0004487485],"category_scores_gemma":[0.0004520928,0.00023506343,0.00019847127,0.00077857444,0.00021111724,0.00044667887,0.00023591841,0.00014826622,0.000080697624],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072561397,0.00020399477,0.89270324,0.00013782826,0.0001803301,0.00014063437,0.0009083151,0.005803686,0.051990055,0.0002162082,0.0015918047,0.045398414],"study_design_scores_gemma":[0.000014871655,0.000011494117,0.9960854,0.0000042011566,0.000024991416,0.00003785118,0.00015995598,0.0026124825,0.00064583204,0.000028205703,0.00036898235,0.000005682918],"about_ca_topic_score_codex":0.11725135,"about_ca_topic_score_gemma":0.2674315,"teacher_disagreement_score":0.88274866,"about_ca_system_score_codex":0.0005960032,"about_ca_system_score_gemma":0.0004490354,"threshold_uncertainty_score":0.23313773},"labels":[],"label_agreement":null},{"id":"W2038485292","doi":"10.1016/j.rse.2004.02.011","title":"Leaf area index measurements in a tropical moist forest: A case study from Costa Rica","year":2004,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":79,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Leaf area index; Canopy; Vegetation (pathology); Remote sensing; Range (aeronautics); Regression; Environmental science; Tropical forest; Index (typography); Nonparametric statistics; Mathematics; Geography; Statistics; Ecology; Computer science","score_opus":0.02483125443404668,"score_gpt":0.22914937870547752,"score_spread":0.20431812427143084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038485292","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9990721,0.00006723487,0.00007605411,0.000029892599,5.6152146e-7,0.000012290452,0.00009271089,0.0000035817156,0.00064555515],"genre_scores_gemma":[0.999292,0.000085137726,0.0003142362,0.000015991625,0.0000013883891,0.0000073593014,0.00008767198,0.0000024290143,0.00019367934],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99969983,0.00012851058,0.000019024405,0.000046974168,0.00005544694,0.000050199305],"domain_scores_gemma":[0.99949694,0.00018609376,0.00008945139,0.000068602545,0.000110722656,0.000048193546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005084428,0.00028665178,0.0002509632,0.00058601424,0.0007587673,0.0005004921,0.0006229215,0.0005072404,0.00029298404],"category_scores_gemma":[0.0010771506,0.00013809005,0.00022226182,0.0012664082,0.0005758047,0.00024631925,0.00047229652,0.00018859074,0.00007567587],"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":[0.00020670185,0.00027516164,0.95349425,0.000119199605,0.00013439485,0.0068899924,0.0065243035,0.0021253063,0.009832514,0.00027021448,0.00042982263,0.019698216],"study_design_scores_gemma":[0.00001464304,0.000058628553,0.9909855,0.000016927263,0.00005334882,0.00070584205,0.0038870643,0.0025676591,0.00083527394,0.000044764947,0.0008196841,0.000010630641],"about_ca_topic_score_codex":0.25359046,"about_ca_topic_score_gemma":0.44867313,"teacher_disagreement_score":0.25359046,"about_ca_system_score_codex":0.0012974648,"about_ca_system_score_gemma":0.0005611853,"threshold_uncertainty_score":0.50422883},"labels":[],"label_agreement":null},{"id":"W2040123287","doi":"10.1016/j.rse.2007.11.018","title":"Operational estimation of primary production at large geographical scales","year":2008,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":72,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; Bedford Institute of Oceanography","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency; National Centre for Earth Observation; Natural Environment Research Council; Sight Research UK; National Aeronautics and Space Administration","keywords":"Remote sensing; Environmental science; Data assimilation; Pixel; Context (archaeology); Phytoplankton; Stratification (seeds); Mode (computer interface); Computer science; Production (economics); Meteorology; Geology; Ecology; Geography","score_opus":0.010052977773448638,"score_gpt":0.17415382542283164,"score_spread":0.164100847649383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040123287","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.792054,0.00038354934,0.20334552,0.00013667229,0.00003241914,0.000029041463,0.0011153171,0.00036442434,0.0025391472],"genre_scores_gemma":[0.97457176,0.00007835384,0.024434477,0.000009153607,0.000014884091,0.000012971765,0.000690022,0.000017362907,0.00017096191],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975806,0.00006189873,0.000018173756,0.00007033316,0.000059386242,0.000032233067],"domain_scores_gemma":[0.99910897,0.00045301244,0.00011968913,0.00015375542,0.0001233316,0.000041142237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005479998,0.00038934674,0.0003606442,0.00047486534,0.00018369628,0.0008993813,0.0004483449,0.00035911525,0.00043936228],"category_scores_gemma":[0.0022436834,0.00019480626,0.00019877427,0.00080853957,0.0003556047,0.0010490293,0.00060023513,0.0002647783,0.00016247174],"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":[0.0006208816,0.00034941465,0.1895653,0.00025200596,0.0002499105,0.00020767604,0.00024699082,0.37509775,0.13580637,0.0063365507,0.0014942655,0.2897729],"study_design_scores_gemma":[0.000032266347,0.000087778484,0.18636122,0.0000099662175,0.000039517952,0.00007939373,0.00014204683,0.7896228,0.01811579,0.004153213,0.0013244093,0.000031665477],"about_ca_topic_score_codex":0.0058467803,"about_ca_topic_score_gemma":0.0072311508,"teacher_disagreement_score":0.0058467803,"about_ca_system_score_codex":0.00033670553,"about_ca_system_score_gemma":0.0005313496,"threshold_uncertainty_score":0.011625469},"labels":[],"label_agreement":null},{"id":"W2040383628","doi":"10.1016/j.rse.2007.04.001","title":"Development of a simulation model to predict LiDAR interception in forested environments","year":2007,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Commonwealth Scientific and Industrial Research Organisation","keywords":"Lidar; Interception; Remote sensing; Environmental science; Range (aeronautics); Canopy; Laser scanning; Ranging; Tree canopy; Computer science; Geography; Laser; Ecology","score_opus":0.022183831325274708,"score_gpt":0.26209287057735864,"score_spread":0.23990903925208393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040383628","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6300869,0.0003022411,0.3481661,0.0007452228,0.00016823446,0.0002320946,0.0013859676,0.001811356,0.017101854],"genre_scores_gemma":[0.9587867,0.00015113318,0.03723515,0.00005641847,0.00001768041,0.00018674492,0.0005773924,0.00008986374,0.0028988544],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987674,0.000037132617,0.000009320955,0.000023465032,0.00002850444,0.00002491996],"domain_scores_gemma":[0.9989992,0.00061951665,0.00007465378,0.000042552998,0.00018757759,0.000076489625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049308606,0.00068223104,0.00078273355,0.0006289749,0.000853608,0.0008344026,0.0011417653,0.0015588929,0.0027839125],"category_scores_gemma":[0.0019552575,0.00068996387,0.0007899755,0.0005536516,0.00045673846,0.00086643087,0.00052402983,0.0008164358,0.00035355266],"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":[0.0000072304892,0.000011717521,0.00045400308,0.000003474518,0.0000046344953,0.000013830572,0.000004032622,0.9985936,0.000093854986,0.00026888124,0.000053412077,0.0004913923],"study_design_scores_gemma":[0.0000033403776,0.0000029024193,0.000039395978,6.736679e-7,0.000001591768,0.0000014253256,0.0000018879579,0.99975294,0.00006663499,0.00008138411,0.000046640926,0.0000012002835],"about_ca_topic_score_codex":0.055094846,"about_ca_topic_score_gemma":0.025489181,"teacher_disagreement_score":0.055094846,"about_ca_system_score_codex":0.0012274915,"about_ca_system_score_gemma":0.0019664809,"threshold_uncertainty_score":0.10954833},"labels":[],"label_agreement":null},{"id":"W2040812919","doi":"10.1016/j.rse.2007.02.033","title":"On detection of the thermophysical state of landfast first-year sea ice using in-situ microwave emission during spring melt","year":2007,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"In situ; Spring (device); Microwave; Remote sensing; Sea ice; Environmental science; Geology; Oceanography; Meteorology; Telecommunications; Geography; Physics","score_opus":0.006575001008011922,"score_gpt":0.1800315934845955,"score_spread":0.17345659247658357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040812919","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9919099,0.00020007187,0.0056528286,0.000052525666,0.000021906795,0.000011514713,0.00027598467,0.000040469487,0.0018347471],"genre_scores_gemma":[0.9949798,0.00021718947,0.0036377197,0.000034208722,0.00002541592,0.000008427271,0.00029687557,0.000014807185,0.00078566646],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9999516,0.000009595006,0.0000018688588,0.000015762884,0.000010104709,0.0000109737475],"domain_scores_gemma":[0.9997584,0.0001518816,0.000019302617,0.000018645345,0.00003658378,0.000015136096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014872753,0.0002495628,0.0001886695,0.00032978685,0.0002404186,0.0003561482,0.0003465974,0.00034238253,0.0008206426],"category_scores_gemma":[0.0003325875,0.00015787131,0.0001651566,0.00022131487,0.00017362574,0.00031335527,0.0002000352,0.00018153785,0.00018348097],"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":[0.0015196246,0.00016559479,0.1024832,0.0001208909,0.000085847045,0.00018576431,0.00023424119,0.0034016783,0.850136,0.00027952957,0.00043538972,0.04095227],"study_design_scores_gemma":[0.00007473616,0.0004104629,0.53198636,0.000023655988,0.00017296078,0.00029287304,0.00027409464,0.07169342,0.39157245,0.0003754902,0.0030889646,0.000034528017],"about_ca_topic_score_codex":0.0033997775,"about_ca_topic_score_gemma":0.00788134,"teacher_disagreement_score":0.0033997775,"about_ca_system_score_codex":0.00015878255,"about_ca_system_score_gemma":0.00012472791,"threshold_uncertainty_score":0.006760001},"labels":[],"label_agreement":null},{"id":"W2042038556","doi":"10.1016/s0034-4257(99)00098-x","title":"High Spatial Resolution Remote Sensing Data for Forest Ecosystem Classification An Examination of Spatial Scale","year":2000,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":139,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Forest ecology; Environmental science; Remote sensing; Ecosystem; Taiga; Spatial ecology; Forest management; Understory; Ecology; Geography; Canopy; Forestry; Agroforestry","score_opus":0.026302930088640365,"score_gpt":0.24258276687314093,"score_spread":0.21627983678450055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042038556","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9584446,0.0025083837,0.026126424,0.0007097009,0.000038668855,0.000046715126,0.0013792185,0.00014074509,0.0106056705],"genre_scores_gemma":[0.9818001,0.0006546778,0.0155590335,0.0000852087,0.000031502695,0.000015893103,0.0007182087,0.000026559344,0.0011088606],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994294,0.00017730052,0.000033487926,0.00006752398,0.00025874586,0.00003343616],"domain_scores_gemma":[0.9929069,0.005120247,0.0003842453,0.00067749916,0.0008529832,0.000058146128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025433032,0.00013031645,0.00015057603,0.0008766079,0.00019840295,0.0006868319,0.00022290251,0.0002571366,0.0011379129],"category_scores_gemma":[0.011174005,0.00022429785,0.00020690466,0.0011464402,0.00032761836,0.0013990366,0.00041230556,0.00033412184,0.00022213894],"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":[0.0005404083,0.00032901537,0.44155607,0.0003986097,0.00031503916,0.00040310147,0.00080378633,0.060353223,0.054068528,0.011594568,0.0038921793,0.4257455],"study_design_scores_gemma":[0.00003324912,0.00016430081,0.8568544,0.00006631702,0.00019495728,0.00038064126,0.0007319605,0.11812859,0.008351796,0.006634271,0.008415848,0.00004372795],"about_ca_topic_score_codex":0.004655711,"about_ca_topic_score_gemma":0.00968265,"teacher_disagreement_score":0.004655711,"about_ca_system_score_codex":0.00025609822,"about_ca_system_score_gemma":0.00029728966,"threshold_uncertainty_score":0.013450444},"labels":[],"label_agreement":null},{"id":"W2042063023","doi":"10.1016/j.rse.2005.03.007","title":"Detection of red attack stage mountain pine beetle infestation with high spatial resolution satellite imagery","year":2005,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":132,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia; Government of British Columbia; Canadian Forest Service","funders":"","keywords":"Multispectral image; Remote sensing; Mountain pine beetle; Satellite imagery; Environmental science; Multispectral pattern recognition; Geology; Geography; Forestry","score_opus":0.008047018857101585,"score_gpt":0.2050405244939191,"score_spread":0.19699350563681753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042063023","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9952727,0.000073972245,0.0015671204,0.000033384764,0.000006328385,0.000023705874,0.0007152682,0.00009221125,0.0022153857],"genre_scores_gemma":[0.99211556,0.00006177292,0.0059974496,0.000019524325,0.000009257384,0.00001087532,0.0010316594,0.0000061148776,0.0007478021],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999167,0.000011267355,0.000004088545,0.000016826796,0.000031364456,0.000019796846],"domain_scores_gemma":[0.9997975,0.000042818887,0.000045875357,0.0000172102,0.000058138576,0.000038466154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019772384,0.0001498254,0.00014689082,0.0010604332,0.00016491993,0.00022201973,0.00014542525,0.00022343299,0.0007681147],"category_scores_gemma":[0.0003183056,0.0001561078,0.00010804184,0.0003973786,0.00007938664,0.00019625225,0.0001463732,0.00012709053,0.00014286372],"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":[0.0011950816,0.00036430397,0.5623612,0.00020637228,0.0002237703,0.0007161213,0.00055619195,0.0073218276,0.28447014,0.00042432727,0.00521174,0.13694893],"study_design_scores_gemma":[0.000020601614,0.00010457405,0.9726241,0.000008878907,0.000045632158,0.00029024616,0.00014422716,0.016567646,0.009142619,0.000038983155,0.0010011158,0.000011346681],"about_ca_topic_score_codex":0.007747586,"about_ca_topic_score_gemma":0.024200913,"teacher_disagreement_score":0.007747586,"about_ca_system_score_codex":0.00012620761,"about_ca_system_score_gemma":0.00012754944,"threshold_uncertainty_score":0.015404999},"labels":[],"label_agreement":null},{"id":"W2043679409","doi":"10.1016/j.rse.2014.03.020","title":"Spectrographic measurement of plant pigments from 300 to 800nm","year":2014,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Botanical Research and Applications","field":"Agricultural and Biological Sciences","cited_by":44,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; University of Saskatchewan","keywords":"Pigment; Anthocyanin; Absorption (acoustics); Chlorophyll; Chromatography; Chemistry; Column chromatography; Absorption spectroscopy; Biological pigment; Chlorophyll a; Resolution (logic); Remote sensing; Optics; Computer science; Food science; Geology; Artificial intelligence; Organic chemistry; Biochemistry; Physics","score_opus":0.02886607095952912,"score_gpt":0.21294224774968806,"score_spread":0.18407617679015895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043679409","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96053535,0.00066228845,0.019425822,0.00015641064,0.000031970012,0.00003385244,0.0005837038,0.0006030412,0.017967654],"genre_scores_gemma":[0.98140115,0.0003970423,0.011996005,0.00010892289,0.0000074579807,0.00003046554,0.00032593132,0.000051420626,0.005681441],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999312,0.000005604765,0.0000019268236,0.000019859119,0.00003237554,0.000008979181],"domain_scores_gemma":[0.999887,0.000031524047,0.000011000763,0.0000122096435,0.000045422254,0.000012933235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006904653,0.00019748024,0.000095998636,0.00035176607,0.00030751812,0.00019061133,0.0001674623,0.00022904197,0.0015774334],"category_scores_gemma":[0.00013644932,0.00012545075,0.00008757596,0.00033102353,0.00014339284,0.0002402953,0.00014863594,0.00036209956,0.00035306343],"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":[0.000111332665,0.000032085034,0.002783337,0.000047377293,0.000006951599,0.000017438026,0.00008179593,0.00014284454,0.9824541,0.00018580296,0.0003426267,0.013794287],"study_design_scores_gemma":[0.0000069130115,0.00009933385,0.050285764,0.000011389588,0.00002075984,0.00015123423,0.00010142643,0.0017538182,0.94387597,0.0001359111,0.0035458016,0.000011751759],"about_ca_topic_score_codex":0.0018186918,"about_ca_topic_score_gemma":0.0034694292,"teacher_disagreement_score":0.0018186918,"about_ca_system_score_codex":0.00033622567,"about_ca_system_score_gemma":0.00020701694,"threshold_uncertainty_score":0.0052770376},"labels":[],"label_agreement":null},{"id":"W2046907634","doi":"10.1016/j.rse.2006.06.007","title":"Integrating remotely sensed and ancillary data sources to characterize a mountain pine beetle infestation","year":2006,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":65,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; University of British Columbia","funders":"Natural Resources Canada; Government of Canada","keywords":"Mountain pine beetle; Dendroctonus; Basal area; Environmental science; Forest inventory; Terrain; Bark beetle; Range (aeronautics); Remote sensing; Ecology; Forestry; Forest management; Geography; Cartography; Agroforestry; Biology","score_opus":0.013565865877250846,"score_gpt":0.21399095897878848,"score_spread":0.20042509310153764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046907634","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9953244,0.00009108269,0.0032249826,0.00002319389,0.0000072688294,0.000017563172,0.000530648,0.00010754346,0.0006732652],"genre_scores_gemma":[0.9919734,0.000042421496,0.0065062516,0.000014154104,0.00001002621,0.000011963534,0.0011268038,0.000008953141,0.0003060961],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998172,0.00003848023,0.000015289936,0.000046534842,0.000053321943,0.000029239143],"domain_scores_gemma":[0.99936587,0.00020323366,0.0001099297,0.000070635346,0.00020764073,0.000042720887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042708882,0.0003769035,0.000278397,0.0011128326,0.00021647058,0.0005271429,0.00019214228,0.0003164001,0.0006103436],"category_scores_gemma":[0.0010255656,0.00015839725,0.00022748495,0.00076337345,0.000058460548,0.00052485365,0.00020246777,0.00016949106,0.000139952],"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":[0.0007030533,0.0007387647,0.73786914,0.00011230372,0.0003006778,0.0003395711,0.00027211738,0.019825054,0.0861998,0.00014982022,0.0009930335,0.15249662],"study_design_scores_gemma":[0.000034309287,0.0002107443,0.81548667,0.000011671878,0.00018500169,0.00021107514,0.00022432586,0.17263633,0.010056589,0.00016066652,0.000756099,0.000026432082],"about_ca_topic_score_codex":0.006540975,"about_ca_topic_score_gemma":0.022779426,"teacher_disagreement_score":0.006540975,"about_ca_system_score_codex":0.00018693399,"about_ca_system_score_gemma":0.00019884972,"threshold_uncertainty_score":0.013005793},"labels":[],"label_agreement":null},{"id":"W2047768727","doi":"10.1016/j.rse.2013.09.024","title":"Estimation of diffuse attenuation of ultraviolet light in optically shallow Florida Keys waters from MODIS measurements","year":2013,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Goddard Space Flight Center; U.S. Geological Survey; National Oceanic and Atmospheric Administration; Florida Fish and Wildlife Conservation Commission; U.S. Environmental Protection Agency","keywords":"Colored dissolved organic matter; Environmental science; Water column; Attenuation coefficient; Remote sensing; Attenuation; Dissolved organic carbon; Benthic zone; Ultraviolet; Absorption (acoustics); Waves and shallow water; Coral reef; Wavelength; Water quality; Environmental chemistry; Oceanography; Chemistry; Geology; Optics; Ecology; Physics; Biology","score_opus":0.013869006914194485,"score_gpt":0.1785611927518271,"score_spread":0.16469218583763262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047768727","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9976299,0.000057906123,0.0013754329,0.000013683104,0.00000236819,0.000007066995,0.00030630003,0.000026570939,0.0005806644],"genre_scores_gemma":[0.99524033,0.000068068606,0.0039422805,0.000010636608,0.0000024635622,0.000010184641,0.00051097706,0.000005491718,0.00020947261],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99993396,0.000005562389,0.0000034942643,0.00002134544,0.000019847168,0.000015746753],"domain_scores_gemma":[0.99985623,0.0000339951,0.000028413888,0.0000093176695,0.000053200998,0.000018865807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016137876,0.00033256275,0.00014235798,0.00071251317,0.00025952817,0.00034597068,0.0002002602,0.00021301628,0.0004038165],"category_scores_gemma":[0.00048566156,0.00020610211,0.00021873726,0.00045349903,0.00011542959,0.00038805537,0.00024610705,0.00020120453,0.00010871617],"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":[0.00061677507,0.00013781182,0.74247557,0.000117989155,0.00011762493,0.00009758817,0.0003632131,0.016411198,0.17891642,0.0002854931,0.00069773826,0.059762467],"study_design_scores_gemma":[0.000028993089,0.00007869354,0.94045275,0.000020060204,0.00007500002,0.000058085934,0.00035809624,0.041948672,0.016089998,0.00008289323,0.00077243225,0.000034368957],"about_ca_topic_score_codex":0.06998284,"about_ca_topic_score_gemma":0.10851454,"teacher_disagreement_score":0.06998284,"about_ca_system_score_codex":0.00060751574,"about_ca_system_score_gemma":0.0004662466,"threshold_uncertainty_score":0.13915098},"labels":[],"label_agreement":null},{"id":"W2048903028","doi":"10.1016/j.rse.2008.09.010","title":"Seasonal snow extent and snow mass in South America using SMMR and SSM/I passive microwave data (1979–2006)","year":2008,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":66,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Oceanic and Atmospheric Administration","keywords":"Snow; Environmental science; Snow cover; Snow line; Satellite; Climatology; Snow field; Special sensor microwave/imager; Radiometer; Snowpack; Physical geography; Meteorology; Remote sensing; Microwave; Geography; Geology; Brightness temperature","score_opus":0.042985211748197354,"score_gpt":0.2170650168047599,"score_spread":0.17407980505656254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048903028","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99637336,0.00018266398,0.00004991446,0.000038484744,0.000004658291,0.0000044198337,0.0027326192,0.000008641525,0.0006052073],"genre_scores_gemma":[0.9947313,0.0001897682,0.00025473666,0.00002649404,0.000009271095,0.00001107067,0.004253974,0.000005856085,0.0005175938],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99993,0.000010111857,0.000008811563,0.000024176365,0.000012848851,0.000014012652],"domain_scores_gemma":[0.99956256,0.00005556456,0.00015656535,0.000025851457,0.00012883884,0.000070597394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030568652,0.00025602596,0.00019115169,0.0007604608,0.0003372919,0.00034361958,0.0002867822,0.00025875185,0.00085679744],"category_scores_gemma":[0.00048894045,0.00017007581,0.000292795,0.0010738085,0.00015815395,0.0004295871,0.00027930224,0.00022648447,0.00018090697],"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":[0.00015257658,0.00003418329,0.9915537,0.000048964273,0.00016820984,0.00009491691,0.00035510538,0.0005792164,0.0027783823,0.00003326804,0.00086154364,0.0033399556],"study_design_scores_gemma":[0.0000024109784,0.0000044629783,0.99915385,0.0000034978507,0.000019521876,0.000020275029,0.000088249035,0.00019603733,0.00009385858,0.0000031989991,0.00041304983,0.0000016175894],"about_ca_topic_score_codex":0.21297625,"about_ca_topic_score_gemma":0.4589373,"teacher_disagreement_score":0.21297625,"about_ca_system_score_codex":0.0007030928,"about_ca_system_score_gemma":0.0005225624,"threshold_uncertainty_score":0.42347318},"labels":[],"label_agreement":null},{"id":"W2049698898","doi":"10.1016/j.rse.2007.02.004","title":"Comparison of MODIS gross primary production estimates for forests across the U.S.A. with those generated by a simple process model, 3-PGS","year":2007,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":78,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"U.S. Environmental Protection Agency; National Aeronautics and Space Administration","keywords":"Primary production; Environmental science; Soil water; Photosynthetically active radiation; Precipitation; Radiometer; Atmospheric sciences; Satellite; Climatology; Remote sensing; Meteorology; Ecosystem; Soil science; Geology; Geography; Ecology","score_opus":0.017292862207844144,"score_gpt":0.27919406138348885,"score_spread":0.2619011991756447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049698898","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9664823,0.0004528085,0.0070583657,0.00045531613,0.00007440911,0.000030611962,0.019605482,0.00062768406,0.00521291],"genre_scores_gemma":[0.98369956,0.00015081513,0.006856645,0.000045814333,0.000015213598,0.000020663476,0.008823095,0.00007120001,0.0003169167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982446,0.00003733691,0.000017722325,0.00006126041,0.000046577985,0.000012634955],"domain_scores_gemma":[0.9993649,0.00019859393,0.000085680396,0.000075032636,0.00024804508,0.000027813354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067845744,0.00032264745,0.00020974525,0.00085259345,0.00018406604,0.00041396258,0.00030024227,0.00033759957,0.0010065851],"category_scores_gemma":[0.0018744601,0.0001850194,0.0004998909,0.0013842448,0.00017382287,0.00067744625,0.00020466668,0.0001990808,0.00023543643],"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":[0.0010058176,0.00034998744,0.34503937,0.0004585688,0.0010143861,0.0003145588,0.00028894472,0.5030063,0.010609438,0.00528238,0.019279478,0.113350764],"study_design_scores_gemma":[0.00022768852,0.00008146339,0.64953864,0.00007573739,0.00018342397,0.00014887324,0.00025171594,0.33354703,0.0035312374,0.0027902091,0.009555514,0.00006854699],"about_ca_topic_score_codex":0.094703965,"about_ca_topic_score_gemma":0.09374995,"teacher_disagreement_score":0.094703965,"about_ca_system_score_codex":0.000805134,"about_ca_system_score_gemma":0.0005076264,"threshold_uncertainty_score":0.1883055},"labels":[],"label_agreement":null},{"id":"W2053548517","doi":"10.1016/j.rse.2013.10.016","title":"RADARSAT-2 D-InSAR for ground displacement in permafrost terrain, validation from Iqaluit Airport, Baffin Island, Canada","year":2013,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":122,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université Laval; Center for Northern Studies; Geological Survey of Canada","funders":"Natural Resources Canada; Canadian Space Agency","keywords":"Interferometric synthetic aperture radar; Permafrost; Geology; Remote sensing; Synthetic aperture radar; Terrain; Ground truth; Geodesy; Oceanography; Geography; Cartography","score_opus":0.01783794421747726,"score_gpt":0.20186535047731105,"score_spread":0.1840274062598338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053548517","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9286046,0.00055211864,0.0063634496,0.00029524247,0.00007821274,0.00023502375,0.036378227,0.0012163726,0.026276633],"genre_scores_gemma":[0.93045187,0.00025251808,0.010241853,0.000105502186,0.000013758668,0.00009077924,0.044650707,0.00023584819,0.013957103],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995739,0.000023231818,0.000014016855,0.00006691698,0.00020901003,0.00011294412],"domain_scores_gemma":[0.99884784,0.0000618829,0.000033791435,0.00007007412,0.0008979921,0.00008829018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055083184,0.00067170855,0.00040201336,0.0010657533,0.0018827415,0.0008578226,0.000961048,0.00035204805,0.0029572027],"category_scores_gemma":[0.0010651773,0.00026813138,0.0003136057,0.001559766,0.00036663847,0.00038637983,0.00032183732,0.0005804567,0.0013456368],"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":[0.0011151733,0.0008181725,0.5697417,0.00032758174,0.0003740869,0.0009521574,0.0011491111,0.105274774,0.048276216,0.001594369,0.06590674,0.20446996],"study_design_scores_gemma":[0.00021802606,0.00006960176,0.87622476,0.00007598736,0.00007367202,0.00011993887,0.0007776995,0.09406162,0.008086058,0.00015111543,0.020066973,0.00007461562],"about_ca_topic_score_codex":0.97586876,"about_ca_topic_score_gemma":0.98660094,"teacher_disagreement_score":0.024131238,"about_ca_system_score_codex":0.0035880713,"about_ca_system_score_gemma":0.009294848,"threshold_uncertainty_score":0.048546672},"labels":[],"label_agreement":null},{"id":"W2055433187","doi":"10.1016/j.rse.2008.03.002","title":"Regional mapping of gross light-use efficiency using MODIS spectral indices","year":2008,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":160,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; University of British Columbia; University of Manitoba; Université Laval","funders":"Canadian Forest Service; Natural Sciences and Engineering Research Council of Canada; U.S. Forest Service; Canadian Foundation for Climate and Atmospheric Sciences; Goddard Space Flight Center; University of Maryland, Baltimore County; National Aeronautics and Space Administration","keywords":"Photosynthetically active radiation; Environmental science; Eddy covariance; Remote sensing; Canopy; Photochemical Reflectance Index; Leaf area index; Atmospheric sciences; Backscatter (email); Flux (metallurgy); Growing season; Normalized Difference Vegetation Index; Meteorology; Ecosystem; Photosynthesis; Geography; Physics; Computer science; Ecology","score_opus":0.03092813561954958,"score_gpt":0.21226156586951717,"score_spread":0.1813334302499676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055433187","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99394894,0.00012226116,0.0023707638,0.000034342676,0.0000066689686,0.000015740898,0.0014857609,0.00025351855,0.0017620146],"genre_scores_gemma":[0.9903743,0.000048126745,0.007754307,0.0000064836413,0.0000056588715,0.000014228843,0.0012233362,0.00002471486,0.0005488225],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998796,0.000014912992,0.000006159934,0.00005084404,0.000028780985,0.00001966967],"domain_scores_gemma":[0.9996449,0.00006948706,0.00004812774,0.00005294299,0.00014489835,0.000039594084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004149252,0.00024173794,0.00027955242,0.0008587619,0.00022590562,0.00042746193,0.00029250092,0.0002337192,0.0008482921],"category_scores_gemma":[0.0005533295,0.00019642747,0.00036429422,0.00080158236,0.0001754842,0.0003295326,0.00024633735,0.00017244884,0.00033649665],"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":[0.003803524,0.00081966916,0.48075777,0.00031040463,0.00056901085,0.0004971049,0.00094412646,0.15885401,0.19342814,0.0024119015,0.004532455,0.15307179],"study_design_scores_gemma":[0.00015710226,0.00014354805,0.8769004,0.000020385964,0.0001483394,0.00011779507,0.00023507226,0.1110919,0.008090153,0.00034653593,0.0026972874,0.00005149056],"about_ca_topic_score_codex":0.030266758,"about_ca_topic_score_gemma":0.03013054,"teacher_disagreement_score":0.030266758,"about_ca_system_score_codex":0.00047197472,"about_ca_system_score_gemma":0.0003860233,"threshold_uncertainty_score":0.0601812},"labels":[],"label_agreement":null},{"id":"W2055851115","doi":"10.1016/j.rse.2012.10.030","title":"Spatial and temporal variation in primary productivity (NDVI) of coastal Alaskan tundra: Decreased vegetation growth following earlier snowmelt","year":2012,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":173,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Division of Arctic Sciences; Natural Sciences and Engineering Research Council of Canada; Natural Resources Conservation Service; International Arctic Research Center, University of Alaska, Fairbanks; Alberta Innovates - Technology Futures; Oak Ridge National Laboratory; National Oceanic and Atmospheric Administration; Desert Research Institute; U.S. Department of Agriculture; National Aeronautics and Space Administration; National Science Foundation","keywords":"Tundra; Snowmelt; Normalized Difference Vegetation Index; Environmental science; Growing season; Vegetation (pathology); Arctic; Productivity; Snow; Physical geography; Arctic vegetation; Hydrology (agriculture); Climate change; Ecology; Geography; Oceanography; Geology","score_opus":0.015238962738980016,"score_gpt":0.20021982644500527,"score_spread":0.18498086370602526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055851115","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99962175,0.000017916811,0.000014927102,0.000009990635,0.0000017323858,5.234568e-7,0.00018256663,0.0000016757153,0.00014883668],"genre_scores_gemma":[0.9995479,0.000015184909,0.000023141594,0.0000055209716,0.0000017094496,0.000001526834,0.00022311529,8.5934147e-7,0.00018093998],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99993336,0.0000090797175,0.0000070330902,0.000026957454,0.000007528011,0.000016001202],"domain_scores_gemma":[0.9995191,0.00012335398,0.00013607569,0.00003540371,0.000071555936,0.00011448639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022809766,0.0001244479,0.00012812603,0.00044532557,0.00027228464,0.00038201793,0.00020887633,0.00034928072,0.0014023153],"category_scores_gemma":[0.00066238677,0.00016707856,0.00021714882,0.00045384583,0.00025896312,0.00026907376,0.00025232576,0.0002349747,0.00018250107],"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":[0.00044457155,0.000079987505,0.9874824,0.000015383797,0.00008579231,0.00020915509,0.0003521658,0.0005528404,0.008489034,0.00004292265,0.00016729119,0.0020783907],"study_design_scores_gemma":[9.857456e-7,0.0000083827845,0.99964416,7.6766133e-7,0.0000050600797,0.000017547669,0.00008789295,0.00012700364,0.000074895084,0.0000034463958,0.000028829692,9.3777544e-7],"about_ca_topic_score_codex":0.057532962,"about_ca_topic_score_gemma":0.09725828,"teacher_disagreement_score":0.057532962,"about_ca_system_score_codex":0.00041948835,"about_ca_system_score_gemma":0.00027220236,"threshold_uncertainty_score":0.114396155},"labels":[],"label_agreement":null},{"id":"W2057498579","doi":"10.1016/j.rse.2007.08.004","title":"A new model of gross primary productivity for North American ecosystems based solely on the enhanced vegetation index and land surface temperature from MODIS","year":2007,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":466,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"Oak Ridge National Laboratory; National Aeronautics and Space Administration","keywords":"Primary production; Environmental science; Remote sensing; Enhanced vegetation index; Vegetation (pathology); Photosynthetically active radiation; Leaf area index; Eddy covariance; Shrub; Scale (ratio); Vegetation Index; Atmospheric sciences; Ecosystem; Normalized Difference Vegetation Index; Geography; Geology","score_opus":0.007388255464689466,"score_gpt":0.1839219946594296,"score_spread":0.17653373919474014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057498579","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21854122,0.00064044085,0.7521333,0.0015673582,0.00047179978,0.00008156834,0.0016877265,0.0010039748,0.023872616],"genre_scores_gemma":[0.9058066,0.0004862604,0.07368099,0.00023064537,0.00018099385,0.0002319455,0.001062224,0.00030502529,0.018015398],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984443,0.000032992124,0.000006577001,0.0000656982,0.000028379969,0.000021957987],"domain_scores_gemma":[0.9996965,0.0001168273,0.000031489224,0.000024328416,0.000094097995,0.000036766283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056461315,0.0006362279,0.00061311445,0.00040669268,0.0007553077,0.0010834917,0.0019790188,0.00093147304,0.0028966293],"category_scores_gemma":[0.0010521173,0.0005120494,0.0007542069,0.00058207,0.00061880954,0.0021198692,0.0006314459,0.00089960283,0.00047290965],"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":[0.00002008127,0.000025404504,0.0010880849,0.00001742094,0.000041766598,0.000044993707,0.00003205821,0.9831665,0.0010239207,0.009511675,0.00090072374,0.0041271597],"study_design_scores_gemma":[0.0000052118967,0.0000037304699,0.00030154857,0.0000014711783,0.00000871545,0.000009622508,0.00000454224,0.99633265,0.000062160565,0.0027561947,0.0005086563,0.0000055394544],"about_ca_topic_score_codex":0.04260504,"about_ca_topic_score_gemma":0.045482293,"teacher_disagreement_score":0.95739496,"about_ca_system_score_codex":0.0017715439,"about_ca_system_score_gemma":0.0014945086,"threshold_uncertainty_score":0.084714115},"labels":[],"label_agreement":null},{"id":"W2057582245","doi":"10.1016/j.rse.2012.07.005","title":"Detecting rock glacier flow structures using Gabor filters and IKONOS imagery","year":2012,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":55,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; International Development Research Centre","keywords":"Terrain; Receiver operating characteristic; Geology; Support vector machine; Artificial intelligence; Random forest; Glacier; Pattern recognition (psychology); Remote sensing; Rock glacier; Texture (cosmology); Gabor filter; Elevation (ballistics); Computer science; Geomorphology; Feature extraction; Mathematics; Cartography; Image (mathematics); Geography; Geometry; Machine learning","score_opus":0.03329147924264924,"score_gpt":0.22508599863452722,"score_spread":0.19179451939187797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057582245","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9489875,0.00027777717,0.045094877,0.00016304078,0.00006986285,0.00005183805,0.00081739074,0.00083264837,0.003705056],"genre_scores_gemma":[0.9494743,0.0002694815,0.04776264,0.000048146107,0.00004548685,0.000016806876,0.0009862457,0.000051290575,0.0013455219],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99991477,0.0000069121866,0.0000036651927,0.000022163846,0.000023004044,0.000029473818],"domain_scores_gemma":[0.99986696,0.000028167326,0.000025554305,0.000012649913,0.00003895392,0.000027677524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020338237,0.00041513925,0.00037197364,0.0020383068,0.00025878297,0.00075218646,0.00024720788,0.0004435604,0.00089213136],"category_scores_gemma":[0.00034110507,0.00026526698,0.00036610078,0.0009869895,0.00017686484,0.0005367067,0.00022621943,0.00028836055,0.0005008715],"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":[0.0010383498,0.00055754156,0.105322525,0.00019158474,0.00015650626,0.0005651424,0.00034095452,0.028453879,0.38735232,0.0011809854,0.0052022017,0.469638],"study_design_scores_gemma":[0.00016369736,0.00021165618,0.4235236,0.000043529846,0.00025118113,0.0003292656,0.0004837974,0.51889515,0.051492598,0.0012380766,0.003291432,0.000076135635],"about_ca_topic_score_codex":0.0069028186,"about_ca_topic_score_gemma":0.01302602,"teacher_disagreement_score":0.0069028186,"about_ca_system_score_codex":0.00028031305,"about_ca_system_score_gemma":0.0004708269,"threshold_uncertainty_score":0.013725281},"labels":[],"label_agreement":null},{"id":"W2057952408","doi":"10.1016/j.rse.2006.02.003","title":"An assessment of needles clumping within shoots when modeling radiative transfer within homogeneous canopies","year":2006,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Shoot; Radiative transfer; Canopy; Leaf area index; Atmospheric radiative transfer codes; Remote sensing; Transmittance; Albedo (alchemy); Environmental science; Bidirectional reflectance distribution function; Botany; Reflectivity; Optics; Physics; Geology; Biology","score_opus":0.010476023432615387,"score_gpt":0.2337159118139528,"score_spread":0.2232398883813374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057952408","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9780562,0.000106794134,0.019952452,0.00007895124,0.00001213068,0.000047519945,0.00010229402,0.00013212,0.0015114638],"genre_scores_gemma":[0.9948277,0.000033335196,0.0048442446,0.000015058947,0.0000037846255,0.000012649983,0.000035422378,0.000035742443,0.00019207355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996903,0.000085071726,0.000025804951,0.00007488575,0.000073810865,0.000050158156],"domain_scores_gemma":[0.9961934,0.0027603966,0.00033417036,0.00021556941,0.00032104837,0.00017539716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010475847,0.00069929316,0.0004506918,0.0005354158,0.0007163908,0.0009746556,0.0011816826,0.0015590488,0.00055185024],"category_scores_gemma":[0.006678389,0.0004957748,0.0007124402,0.00057807134,0.00047063862,0.0010422271,0.0005389377,0.00071914634,0.000091854694],"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":[0.00015880329,0.00010175573,0.022277273,0.000039756942,0.000042476713,0.00017848946,0.00010579035,0.95805883,0.013532431,0.0003040969,0.00007137376,0.0051289503],"study_design_scores_gemma":[0.000014694899,0.000063336614,0.005783206,0.0000050580625,0.000022796115,0.000029342902,0.000060270173,0.98999083,0.0038663768,0.000088864224,0.00006442827,0.000010923613],"about_ca_topic_score_codex":0.028426135,"about_ca_topic_score_gemma":0.02842722,"teacher_disagreement_score":0.028426135,"about_ca_system_score_codex":0.00089917093,"about_ca_system_score_gemma":0.0006068795,"threshold_uncertainty_score":0.056521356},"labels":[],"label_agreement":null},{"id":"W2059377398","doi":"10.1016/j.rse.2006.06.010","title":"Intra- and inter-class spectral variability of tropical tree species at La Selva, Costa Rica: Implications for species identification using HYDICE imagery","year":2006,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":220,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Hyperspectral imaging; Remote sensing; Wavelet; Identification (biology); Tree (set theory); Spectral signature; Variation (astronomy); Wavelet transform; Environmental science; Geography; Mathematics; Computer science; Ecology; Biology; Artificial intelligence; Physics","score_opus":0.01277741086868583,"score_gpt":0.21903355854373993,"score_spread":0.2062561476750541,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059377398","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9986853,0.000047517657,0.00014759086,0.000045281125,9.879295e-7,0.000003668371,0.00025586633,0.000011577411,0.00080211676],"genre_scores_gemma":[0.9994948,0.000022879149,0.00013739605,0.000010225135,0.0000014771683,0.0000034358527,0.00016615777,0.0000035512662,0.00016009102],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99989414,0.000032252952,0.0000061632145,0.000028277149,0.000013803754,0.000025258722],"domain_scores_gemma":[0.99963784,0.00011406686,0.00004984334,0.00004463865,0.00011856237,0.00003506673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034784834,0.00011302855,0.00013765397,0.0006130832,0.0002839283,0.0005492977,0.00022418263,0.000194103,0.0004534223],"category_scores_gemma":[0.0007373467,0.00009041479,0.0001106174,0.00053330284,0.00026670942,0.00035489432,0.00029400954,0.0001289562,0.00011125073],"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":[0.00014044887,0.000043119788,0.9671765,0.000028462686,0.00006893099,0.00009009299,0.0012762954,0.0017866369,0.010783835,0.00021333035,0.0005669014,0.017825466],"study_design_scores_gemma":[0.0000019837921,0.000003016924,0.99740416,0.00000281675,0.000009815048,0.00001986595,0.00031264155,0.0018682225,0.000175541,0.0000137903,0.00018605868,0.0000020228977],"about_ca_topic_score_codex":0.08071266,"about_ca_topic_score_gemma":0.21228817,"teacher_disagreement_score":0.08071266,"about_ca_system_score_codex":0.00047802995,"about_ca_system_score_gemma":0.00021210527,"threshold_uncertainty_score":0.16048574},"labels":[],"label_agreement":null},{"id":"W2059671870","doi":"10.1016/j.rse.2010.07.003","title":"Interpretation of Aura satellite observations of CO and aerosol index related to the December 2006 Australia fires","year":2010,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Global Health Research; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; California Institute of Technology; National Aeronautics and Space Administration","keywords":"Remote sensing; Satellite; Aura; Environmental science; Aerosol; Index (typography); Interpretation (philosophy); Meteorology; Climatology; Geology; Geography; Computer science","score_opus":0.015710010826039764,"score_gpt":0.22667489833817456,"score_spread":0.2109648875121348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059671870","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9939546,0.00008915606,0.00038737615,0.000107346306,0.00003658842,0.000023354476,0.00115961,0.000054436478,0.004187485],"genre_scores_gemma":[0.99726295,0.00006055867,0.00065121514,0.000024708692,0.000013618666,0.000005507216,0.000964639,0.000009150991,0.0010075166],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999037,0.000011458741,0.000009579424,0.000021112564,0.000033308363,0.00002089023],"domain_scores_gemma":[0.9997037,0.000040170708,0.00006751558,0.000027046633,0.00012569118,0.00003569543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020289756,0.00026936177,0.00016071476,0.0007072769,0.00034688617,0.0005378397,0.00018769647,0.0003057285,0.00064425514],"category_scores_gemma":[0.0006796052,0.00017182056,0.00018478933,0.0005014471,0.000118826945,0.0002196294,0.0002594918,0.00025646228,0.00018725457],"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":[0.0013739181,0.00034213671,0.8425586,0.00015530206,0.00023982854,0.0013894276,0.0011690975,0.012258692,0.07040051,0.00046960803,0.006468785,0.063174024],"study_design_scores_gemma":[0.00001145848,0.00003480817,0.9881273,0.000008113764,0.000029800987,0.00008481993,0.00022504976,0.0074074278,0.0023577602,0.000049933587,0.0016540868,0.000009389324],"about_ca_topic_score_codex":0.060169455,"about_ca_topic_score_gemma":0.13393347,"teacher_disagreement_score":0.060169455,"about_ca_system_score_codex":0.000634081,"about_ca_system_score_gemma":0.00043295228,"threshold_uncertainty_score":0.11963844},"labels":[],"label_agreement":null},{"id":"W2060285670","doi":"10.1016/j.rse.2005.07.004","title":"Multispectral imaging contributions to global land ice measurements from space","year":2005,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":277,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge; University of Alberta","funders":"","keywords":"Glacier; Advanced Spaceborne Thermal Emission and Reflection Radiometer; Remote sensing; Geology; Multispectral image; Cryosphere; Climate change; Glaciology; Glacier morphology; Climatology; Ice stream; Sea ice; Geomorphology; Digital elevation model; Hydrogeology; Oceanography","score_opus":0.017362524718941473,"score_gpt":0.23128511127463916,"score_spread":0.21392258655569768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060285670","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9021795,0.0023983035,0.06979524,0.0004928742,0.00025736704,0.000030809275,0.0019152815,0.0005305095,0.02240013],"genre_scores_gemma":[0.9668232,0.0016345837,0.025123963,0.0001121114,0.00028006383,0.0000280209,0.002137905,0.0003858848,0.0034742588],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9997745,0.000040967665,0.000009956709,0.00004819861,0.00008826402,0.000038023118],"domain_scores_gemma":[0.9993868,0.00027861685,0.00005116527,0.00009408382,0.00016643875,0.000022946402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039847606,0.00079429086,0.00020292851,0.0010601191,0.00038133882,0.00082614075,0.00018541323,0.00032001198,0.0018672086],"category_scores_gemma":[0.0017776873,0.0003390116,0.00039915316,0.001363365,0.0002480418,0.0012334525,0.00065945496,0.00041768514,0.0005135885],"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":[0.0006851184,0.00017671718,0.10440205,0.00035968603,0.00024208867,0.0004420823,0.00065008545,0.036201965,0.4051614,0.005541177,0.003392714,0.442745],"study_design_scores_gemma":[0.00005404153,0.000099232784,0.49905765,0.000115201,0.00069121923,0.00084362674,0.00032215784,0.27567405,0.1942055,0.006474423,0.022342205,0.000120666075],"about_ca_topic_score_codex":0.0027977363,"about_ca_topic_score_gemma":0.008737649,"teacher_disagreement_score":0.0027977363,"about_ca_system_score_codex":0.00024352269,"about_ca_system_score_gemma":0.00025687463,"threshold_uncertainty_score":0.0062464476},"labels":[],"label_agreement":null},{"id":"W2063312318","doi":"10.1016/j.rse.2013.08.004","title":"The global distribution of phytoplankton size spectrum and size classes from their light-absorption spectra derived from satellite data","year":2013,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":92,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Environment Research Council; Canadian Space Agency; Sight Research UK; Royal Society; European Space Agency; National Aeronautics and Space Administration","keywords":"Phytoplankton; Particle size; Population; Chlorophyll a; Absorption (acoustics); Particle-size distribution; Statistics; Physics; Mathematics; Chemistry; Biology; Optics; Ecology; Botany","score_opus":0.010275251014054373,"score_gpt":0.1798312302715504,"score_spread":0.16955597925749602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063312318","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99609935,0.00007329205,0.0010060997,0.000026125614,0.0000028074503,0.0000031570735,0.0014090372,0.00003811822,0.0013418957],"genre_scores_gemma":[0.99561936,0.00005607145,0.0009303944,0.000011135395,0.0000047293343,0.0000054626516,0.003028878,0.000022316128,0.00032161913],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999157,0.000010941273,0.0000041208395,0.000041631232,0.000014037141,0.000013440385],"domain_scores_gemma":[0.99946505,0.00020579316,0.00009057468,0.00006323692,0.00012867169,0.000046726807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003503018,0.00018865817,0.00017014377,0.0011906157,0.00013830735,0.00027041676,0.00014779533,0.00020670018,0.0013869287],"category_scores_gemma":[0.00082618324,0.00018159706,0.00035573725,0.00093378965,0.00023354522,0.000503254,0.00034744715,0.00020158745,0.00043021885],"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":[0.000244911,0.000026346665,0.9468439,0.000036687514,0.000107962514,0.00006468426,0.00019550847,0.005273069,0.020758746,0.00027847817,0.0006684292,0.02550135],"study_design_scores_gemma":[0.0000054431443,0.000016416318,0.99509037,0.0000040255286,0.000021668693,0.000041948453,0.000058770765,0.0036507857,0.0005575885,0.00011008616,0.00043768648,0.000005266778],"about_ca_topic_score_codex":0.0035452822,"about_ca_topic_score_gemma":0.0061423173,"teacher_disagreement_score":0.0035452822,"about_ca_system_score_codex":0.000193572,"about_ca_system_score_gemma":0.000113239235,"threshold_uncertainty_score":0.007049322},"labels":[],"label_agreement":null},{"id":"W2064497440","doi":"10.1016/j.rse.2002.06.007","title":"Systematic corrections of AVHRR image composites for temporal studies","year":2003,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":72,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Advanced very-high-resolution radiometer; Remote sensing; Pixel; Environmental science; Normalized Difference Vegetation Index; Radiometry; Atmospheric correction; Image resolution; Vegetation (pathology); Satellite; Geology; Computer science; Physics","score_opus":0.014989121188622244,"score_gpt":0.24296979109403477,"score_spread":0.22798066990541252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064497440","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36153844,0.004784253,0.5951568,0.00053534174,0.0016556112,0.00022783791,0.0071306988,0.011790516,0.017180476],"genre_scores_gemma":[0.6523701,0.002238392,0.32390246,0.00022686174,0.00021301142,0.0002288738,0.0057534953,0.004207665,0.010859127],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99953425,0.00007966153,0.00003792877,0.00010055507,0.0001900429,0.000057592348],"domain_scores_gemma":[0.998092,0.00032653406,0.00017347469,0.0006269684,0.00074049085,0.000040499683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074943324,0.00063363067,0.00029440815,0.001610247,0.00061596156,0.0007961718,0.00044751613,0.00038173288,0.0029064268],"category_scores_gemma":[0.003824618,0.0003817258,0.00043778116,0.0024027193,0.00021272786,0.0008164323,0.00041626944,0.0006099435,0.0010413632],"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":[0.00044518575,0.00016617458,0.021096405,0.00047071866,0.00017240181,0.00013769974,0.0003834931,0.018903188,0.4349632,0.0053318893,0.011889601,0.5060399],"study_design_scores_gemma":[0.00008041495,0.00020175311,0.27621064,0.00013376768,0.0006519463,0.00046494688,0.00042506945,0.17324346,0.43890432,0.005708338,0.1037974,0.00017790173],"about_ca_topic_score_codex":0.007167307,"about_ca_topic_score_gemma":0.026307814,"teacher_disagreement_score":0.007167307,"about_ca_system_score_codex":0.0003718534,"about_ca_system_score_gemma":0.001432499,"threshold_uncertainty_score":0.014251173},"labels":[],"label_agreement":null},{"id":"W2069889975","doi":"10.1016/j.rse.2013.07.020","title":"Monitoring and modeling spatial and temporal patterns of grassland dynamics using time-series MODIS NDVI with climate and stocking data","year":2013,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":116,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Environment; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; University of Saskatchewan; University of Kansas","keywords":"Grassland; Normalized Difference Vegetation Index; Environmental science; Moderate-resolution imaging spectroradiometer; Spatial variability; Growing season; Physical geography; Precipitation; Pasture; Grazing; Temporal scales; Spatial ecology; Productivity; Climate change; Geography; Ecology; Satellite; Meteorology; Forestry; Mathematics; Statistics","score_opus":0.016574666538385557,"score_gpt":0.2136288692011434,"score_spread":0.19705420266275786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069889975","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9957546,0.00004058952,0.003425128,0.00005893374,0.000008390311,0.000013900346,0.00028303766,0.00008353733,0.00033193585],"genre_scores_gemma":[0.99201643,0.000048140155,0.0073190513,0.0000064531505,0.000005412076,0.000019704557,0.00037851604,0.000007769715,0.0001984092],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998723,0.000021968799,0.000013170719,0.000047811653,0.000022047718,0.000022765189],"domain_scores_gemma":[0.999607,0.00016749495,0.00007516872,0.000047005073,0.000067935245,0.000035395144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006510572,0.00043984232,0.0003186171,0.0005102224,0.00035771483,0.00050698954,0.0006862463,0.00046780516,0.00035196988],"category_scores_gemma":[0.0009973315,0.0004801315,0.0005015984,0.00083388673,0.0002337789,0.00089883094,0.00027747406,0.0002990527,0.00006579854],"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":[0.0001958148,0.000438661,0.10641749,0.000054282908,0.00017102479,0.000098226825,0.00006846059,0.8640987,0.007420138,0.0004254112,0.00043303435,0.020178718],"study_design_scores_gemma":[0.000012590741,0.000019359852,0.01705814,0.0000012824589,0.00002018487,0.000007441528,0.000015591517,0.98187596,0.0007995249,0.000109144705,0.00007677546,0.0000041327553],"about_ca_topic_score_codex":0.07068322,"about_ca_topic_score_gemma":0.06670287,"teacher_disagreement_score":0.07068322,"about_ca_system_score_codex":0.001072972,"about_ca_system_score_gemma":0.0007866593,"threshold_uncertainty_score":0.14054358},"labels":[],"label_agreement":null},{"id":"W2070203790","doi":"10.1016/j.rse.2008.04.005","title":"Leaf chlorophyll content retrieval from airborne hyperspectral remote sensing imagery","year":2008,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":230,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Forest Research Institute; York University; University of Toronto","funders":"Midlands Asthma and Allergy Research Association; York University","keywords":"Remote sensing; Hyperspectral imaging; Environmental science; Canopy; Reflectivity; Red edge; Black spruce; Taiga; Geology; Geography; Optics; Physics; Forestry","score_opus":0.02177188193848995,"score_gpt":0.20122100729248826,"score_spread":0.17944912535399832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070203790","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9508359,0.0002374367,0.043096352,0.00013121744,0.000027002769,0.000027825863,0.0009757695,0.0009123379,0.003756096],"genre_scores_gemma":[0.95190686,0.00019770951,0.0430934,0.00006790629,0.000021146734,0.000022732787,0.0021631664,0.00010717568,0.002419858],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9999567,0.0000038877156,0.000001544048,0.000011434524,0.000018281085,0.000008164112],"domain_scores_gemma":[0.9999504,0.000011537979,0.000006281849,0.000008966927,0.000017606997,0.000005085561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007956952,0.0002632265,0.00021249028,0.00031577388,0.00014342033,0.00030997064,0.00021289587,0.0002787989,0.0009686231],"category_scores_gemma":[0.00021623643,0.00016823372,0.00017510727,0.0004086989,0.000085380525,0.00046364102,0.00015345628,0.00025326695,0.00047269784],"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":[0.00038518212,0.00021963882,0.017532635,0.00012966046,0.00006876641,0.00010938486,0.00012518451,0.043101348,0.72745985,0.00074662414,0.003075908,0.20704584],"study_design_scores_gemma":[0.00013434759,0.00008157831,0.1291176,0.00001672655,0.00007512107,0.00014085161,0.00013152912,0.7278389,0.13841534,0.00083516026,0.0031689573,0.000043921948],"about_ca_topic_score_codex":0.0064044464,"about_ca_topic_score_gemma":0.008668911,"teacher_disagreement_score":0.0064044464,"about_ca_system_score_codex":0.00029874893,"about_ca_system_score_gemma":0.00029102358,"threshold_uncertainty_score":0.012734354},"labels":[],"label_agreement":null},{"id":"W2070624879","doi":"10.1016/j.rse.2008.01.010","title":"Multi-temporal analysis of high spatial resolution imagery for disturbance monitoring","year":2008,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":114,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada; University of British Columbia; Canadian Forest Service","funders":"Natural Resources Canada","keywords":"Panchromatic film; Remote sensing; Population; Environmental science; Satellite; Tree (set theory); Geography; Mathematics; Multispectral image; Physics","score_opus":0.0179444924241732,"score_gpt":0.22513838326159324,"score_spread":0.20719389083742004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070624879","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8377681,0.0026224903,0.14231509,0.0005300603,0.00025617587,0.00013264753,0.0070728958,0.0013297715,0.007972777],"genre_scores_gemma":[0.9166179,0.0007718614,0.07597054,0.00006832081,0.00011479748,0.000073873605,0.004099061,0.00013991114,0.0021436273],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99989414,0.000013154035,0.00000611545,0.000018882363,0.00004430309,0.000023335426],"domain_scores_gemma":[0.9997471,0.000063208274,0.00004468894,0.000029667448,0.00008940239,0.000025905123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026500408,0.00029005908,0.00021786055,0.0023776758,0.00019262584,0.0004107714,0.00022175294,0.00025361008,0.0015613054],"category_scores_gemma":[0.0004704195,0.00014916043,0.0004222977,0.0018943046,0.00010083411,0.00036624479,0.00019453508,0.00026912687,0.00032514555],"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":[0.0010053094,0.0007741989,0.041906077,0.00053858955,0.0003840059,0.0006282576,0.00020352917,0.056081153,0.35781533,0.0012720344,0.010896423,0.52849513],"study_design_scores_gemma":[0.000058350022,0.00015133126,0.35582796,0.000041085274,0.0002396437,0.00053461717,0.00023541626,0.6020255,0.031801064,0.0011572002,0.007859871,0.000067909175],"about_ca_topic_score_codex":0.0051815757,"about_ca_topic_score_gemma":0.01347973,"teacher_disagreement_score":0.0051815757,"about_ca_system_score_codex":0.00017940135,"about_ca_system_score_gemma":0.0002521163,"threshold_uncertainty_score":0.010302842},"labels":[],"label_agreement":null},{"id":"W2071945470","doi":"10.1016/j.rse.2012.03.015","title":"Merging land-marine realms: Spatial patterns of seamless coastal habitats using a multispectral LiDAR","year":2012,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":58,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski; Institut National de la Recherche Scientifique","funders":"Fisheries and Oceans Canada","keywords":"Lidar; Shoal; Remote sensing; Littoral zone; Bathymetry; Habitat; Environmental science; Salt marsh; Multispectral image; Land cover; Geography; Ecology; Oceanography; Geology; Cartography; Land use","score_opus":0.014695556432641517,"score_gpt":0.23994930507252793,"score_spread":0.22525374863988643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071945470","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98924446,0.00011831363,0.008631692,0.000035679524,0.000004037893,0.0000150495225,0.0002163861,0.00009878867,0.001635601],"genre_scores_gemma":[0.99225074,0.000033504508,0.0074279574,0.0000060622297,0.0000022936365,0.000005040741,0.00014039331,0.000010966052,0.00012305964],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998672,0.000022994987,0.000005711233,0.000032337335,0.000030663246,0.000041051873],"domain_scores_gemma":[0.9997222,0.00006706058,0.000053233856,0.000036253277,0.00006882064,0.000052427487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023218464,0.000115824434,0.00016163886,0.0011051049,0.00032366443,0.00080582674,0.00026606026,0.00017572296,0.00081741705],"category_scores_gemma":[0.00075574254,0.00023025302,0.0002014956,0.0016675919,0.00020016228,0.00066068256,0.00079295156,0.00013915423,0.00015592822],"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":[0.0007202314,0.00022809947,0.620123,0.00012660548,0.00013897163,0.0006295831,0.0038042236,0.012072664,0.13815051,0.002380914,0.001381134,0.22024406],"study_design_scores_gemma":[0.000024888453,0.00008570731,0.919751,0.000028040087,0.000108304004,0.00057971594,0.002297283,0.06354392,0.009319025,0.0012143078,0.0030037046,0.000044283417],"about_ca_topic_score_codex":0.005560174,"about_ca_topic_score_gemma":0.016563458,"teacher_disagreement_score":0.005560174,"about_ca_system_score_codex":0.0001403497,"about_ca_system_score_gemma":0.00032679885,"threshold_uncertainty_score":0.011055589},"labels":[],"label_agreement":null},{"id":"W2072723788","doi":"10.1016/j.rse.2013.02.010","title":"Mapping eelgrass (Zostera marina) in the Gulf Islands National Park Reserve of Canada using high spatial resolution satellite and airborne imagery","year":2013,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Marine and coastal plant biology","field":"Earth and Planetary Sciences","cited_by":61,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Parks Canada","keywords":"Remote sensing; Hyperspectral imaging; Environmental science; Multispectral image; Image resolution; Zostera marina; Satellite imagery; Satellite; Geology; Computer science; Ecosystem; Ecology; Artificial intelligence","score_opus":0.012874452776783389,"score_gpt":0.17598411235429304,"score_spread":0.16310965957750964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072723788","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9955715,0.0003087622,0.00016544323,0.000094271556,0.000004341718,0.000027344862,0.0017070203,0.000028900997,0.002092312],"genre_scores_gemma":[0.9945821,0.00034976713,0.001155243,0.00004008423,0.0000019929564,0.000020715774,0.0016172444,0.000009494889,0.0022233475],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998159,0.000009621788,0.000008088862,0.000038404767,0.000059559778,0.000068473295],"domain_scores_gemma":[0.99968445,0.000019806712,0.00004120993,0.000011171993,0.00016744403,0.00007587954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019912046,0.00026305916,0.00022086628,0.0019371557,0.0012498486,0.0008084371,0.00070683326,0.0002853424,0.0007630137],"category_scores_gemma":[0.00045001434,0.00022448786,0.00017852527,0.0020103191,0.00055573834,0.00029482527,0.00063005125,0.0002280744,0.00017168037],"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":[0.00019973113,0.00009135449,0.9311084,0.00016482762,0.00019862922,0.00048826495,0.0041018235,0.0023280138,0.012961525,0.0005903734,0.003845757,0.043921422],"study_design_scores_gemma":[0.000008957454,0.000008308066,0.9914863,0.000028870325,0.00002791914,0.00004494319,0.0033291886,0.0014432973,0.0002808245,0.000027441769,0.0033028128,0.000011037491],"about_ca_topic_score_codex":0.9929101,"about_ca_topic_score_gemma":0.9979475,"teacher_disagreement_score":0.008042575,"about_ca_system_score_codex":0.008042575,"about_ca_system_score_gemma":0.00973735,"threshold_uncertainty_score":0.058353245},"labels":[],"label_agreement":null},{"id":"W2075085551","doi":"10.1016/j.rse.2003.10.019","title":"Spectral indices and fire behavior simulation for fire risk assessment in savanna ecosystems","year":2004,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":84,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Environmental science; Woodland; Vegetation (pathology); Firewood; National park; Remote sensing; Thematic Mapper; Disturbance (geology); Land cover; Shrub; Thematic map; Geography; Hydrology (agriculture); Satellite imagery; Land use; Ecology; Cartography; Geology","score_opus":0.009445021084852371,"score_gpt":0.24711562114005084,"score_spread":0.23767060005519847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075085551","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9481501,0.0001336351,0.049807414,0.00011763488,0.000019335406,0.00003186957,0.00016021125,0.00032269963,0.0012571492],"genre_scores_gemma":[0.9825953,0.00004230145,0.016736623,0.000013077362,0.000005361286,0.000029486459,0.0000977081,0.00002832625,0.00045176578],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998242,0.00010239188,0.000011024312,0.000023199022,0.000020492422,0.00001872927],"domain_scores_gemma":[0.99662066,0.002836032,0.00013497088,0.00008601506,0.00022385843,0.000098534576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012478705,0.0005287346,0.00044229583,0.000634552,0.00044420143,0.0004934253,0.00059661194,0.000739924,0.0012667364],"category_scores_gemma":[0.0050968966,0.00043093917,0.00044043871,0.00035380927,0.00026213133,0.00067809934,0.0003641543,0.00055313285,0.00013098757],"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":[0.000112054404,0.00009674305,0.0045393896,0.000008076693,0.000025272,0.000021000695,0.0000212237,0.989441,0.0003311714,0.00041864064,0.00009008488,0.004895341],"study_design_scores_gemma":[0.0000037453592,0.000006299993,0.0002689207,5.0369925e-7,0.0000025386514,0.0000019457832,0.000003597613,0.99950826,0.000081258884,0.00011087512,0.000010998715,0.0000010310026],"about_ca_topic_score_codex":0.030436771,"about_ca_topic_score_gemma":0.022403311,"teacher_disagreement_score":0.030436771,"about_ca_system_score_codex":0.00074881356,"about_ca_system_score_gemma":0.00057276053,"threshold_uncertainty_score":0.06051922},"labels":[],"label_agreement":null},{"id":"W2077202991","doi":"10.1016/j.rse.2009.04.008","title":"Evaluation of annual forest disturbance monitoring using a static decision tree approach and 250 m MODIS data","year":2009,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada","funders":"Natural Resources Canada","keywords":"Normalization (sociology); Change detection; Decision tree; Computer science; Cohen's kappa; Kappa; Remote sensing; Data mining; Artificial intelligence; Machine learning; Mathematics","score_opus":0.049057218976460794,"score_gpt":0.2822607351625055,"score_spread":0.2332035161860447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077202991","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99113864,0.000118309996,0.0075519574,0.000027594777,0.000013618728,0.00003208624,0.00022544774,0.00014222351,0.0007501006],"genre_scores_gemma":[0.9943323,0.000034993132,0.005232299,0.000007657103,0.0000051266557,0.000011600262,0.00019760328,0.0000071473546,0.0001712472],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.999196,0.00028874856,0.000071386625,0.00014389351,0.00022803388,0.00007206807],"domain_scores_gemma":[0.99402434,0.004330645,0.00029485207,0.00020649249,0.000895194,0.00024847273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003368573,0.0005170696,0.0005799234,0.0009566266,0.00034354554,0.0004619547,0.0006269451,0.000432292,0.0008933694],"category_scores_gemma":[0.005270241,0.00020908573,0.0003418975,0.00085560227,0.00017336964,0.0008283956,0.00027112826,0.00015608473,0.00011997665],"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":[0.008484134,0.0018438179,0.20186643,0.0003561766,0.00056229945,0.00043307125,0.00023784405,0.49843758,0.019036403,0.00064329273,0.0011900916,0.26690876],"study_design_scores_gemma":[0.00011895019,0.0012984944,0.06126394,0.000009442515,0.0001637402,0.00007520475,0.00009715572,0.9331711,0.003388669,0.00018952742,0.00020770996,0.000016105789],"about_ca_topic_score_codex":0.014670973,"about_ca_topic_score_gemma":0.015312738,"teacher_disagreement_score":0.014670973,"about_ca_system_score_codex":0.0007191398,"about_ca_system_score_gemma":0.0007329827,"threshold_uncertainty_score":0.029171169},"labels":[],"label_agreement":null},{"id":"W2077358591","doi":"10.1016/j.rse.2013.09.016","title":"The Ocean Colour Climate Change Initiative: III. A round-robin comparison on in-water bio-optical algorithms","year":2013,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":194,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bedford Institute of Oceanography; Université Laval","funders":"National Centre for Earth Observation; Natural Environment Research Council; Sight Research UK; European Space Agency; National Aeronautics and Space Administration","keywords":"Algorithm; Remote sensing; Computer science; Scale (ratio); Empirical modelling; Ranking (information retrieval); Hyperspectral imaging; Environmental science; Machine learning; Artificial intelligence; Geology; Simulation","score_opus":0.026057991369515282,"score_gpt":0.21620791212165824,"score_spread":0.19014992075214296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077358591","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6901407,0.016135992,0.17436619,0.008485021,0.004260842,0.002242661,0.04048578,0.006367667,0.05751519],"genre_scores_gemma":[0.75081456,0.002055588,0.16873515,0.0017127327,0.0003974794,0.0009407606,0.058167264,0.0062647285,0.0109116975],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9879171,0.005836759,0.0006821654,0.0010246239,0.0037218425,0.0008174579],"domain_scores_gemma":[0.97853845,0.0069207875,0.0008296304,0.00462845,0.00764708,0.0014356343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02910874,0.0013631987,0.0012017012,0.003471883,0.0011307369,0.0025701504,0.0024729972,0.0020503649,0.004151159],"category_scores_gemma":[0.04228371,0.00061636086,0.0017026123,0.0032783602,0.00077836745,0.003486269,0.0039442508,0.0016930592,0.0022382343],"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":[0.01920304,0.0019110705,0.11392229,0.0015924221,0.0054191914,0.00023292663,0.00079089723,0.12116341,0.028215045,0.01862674,0.15272394,0.53619903],"study_design_scores_gemma":[0.0057300534,0.0052140974,0.40696624,0.0010542283,0.002987346,0.0005150091,0.0028637592,0.36174834,0.08051974,0.021513907,0.110082075,0.00080523605],"about_ca_topic_score_codex":0.021911602,"about_ca_topic_score_gemma":0.030802181,"teacher_disagreement_score":0.02910874,"about_ca_system_score_codex":0.0015485089,"about_ca_system_score_gemma":0.0017149305,"threshold_uncertainty_score":0.1539436},"labels":[],"label_agreement":null},{"id":"W2078180904","doi":"10.1016/j.rse.2007.02.014","title":"Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification","year":2007,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":232,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Parks Canada; Clayoquot Biosphere Trust; Ministry of Forests, Lands and Natural Resource Operations; University of Queensland","keywords":"Remote sensing; Riparian zone; Vegetation (pathology); Image resolution; Spatial analysis; Pixel; Contextual image classification; Environmental science; Satellite imagery; Image texture; Vegetation classification; Image segmentation; Geology; Segmentation; Computer science; Artificial intelligence; Ecology; Image (mathematics)","score_opus":0.009260019439390978,"score_gpt":0.2081799651654975,"score_spread":0.19891994572610652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078180904","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7023584,0.0015761679,0.2702121,0.000591606,0.00017813568,0.00034852998,0.003472452,0.0024258713,0.018836666],"genre_scores_gemma":[0.7576129,0.000613868,0.23587929,0.00010940442,0.000046785062,0.000058347407,0.0023095729,0.00008254224,0.0032873938],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9998734,0.000027721555,0.000007612421,0.000024176494,0.000049158603,0.000017949733],"domain_scores_gemma":[0.9997341,0.0000802357,0.000022555869,0.000039073337,0.000100209356,0.000023813433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004067336,0.00024706862,0.00019055155,0.00149154,0.00021462605,0.00044343824,0.00022997745,0.00024423955,0.0015497849],"category_scores_gemma":[0.0007986766,0.00016504466,0.0002748519,0.0010703334,0.00010969075,0.00044008263,0.0002386022,0.00018280656,0.00032348305],"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":[0.0002714224,0.00031073837,0.027335458,0.0001754577,0.00013735422,0.00021446905,0.00013342251,0.033489708,0.08109006,0.0010145097,0.005366638,0.85046077],"study_design_scores_gemma":[0.00010747997,0.00019443371,0.14721398,0.000038301096,0.00020192243,0.00037944314,0.0003483813,0.80308634,0.034151673,0.0021983543,0.012022387,0.00005735568],"about_ca_topic_score_codex":0.009497624,"about_ca_topic_score_gemma":0.016112335,"teacher_disagreement_score":0.009497624,"about_ca_system_score_codex":0.0002168209,"about_ca_system_score_gemma":0.00035232792,"threshold_uncertainty_score":0.018884659},"labels":[],"label_agreement":null},{"id":"W2080409199","doi":"10.1016/j.rse.2015.04.014","title":"An approach for evaluating the impact of gaps and measurement errors on satellite land surface phenology algorithms: Application to 20year NOAA AVHRR data over Canada","year":2015,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Canadian Space Agency","keywords":"Satellite; Remote sensing; Normalized Difference Vegetation Index; Environmental science; Algorithm; Phenology; Computer science; Meteorology; Vegetation (pathology); Noise (video); Statistics; Climate change; Climatology; Mathematics; Geography; Geology","score_opus":0.06902062055121302,"score_gpt":0.3089509974612764,"score_spread":0.2399303769100634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080409199","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96902543,0.0005112112,0.027286796,0.00013983324,0.000023419701,0.0001907346,0.0007775536,0.00030888885,0.0017360483],"genre_scores_gemma":[0.91131926,0.00021147942,0.08651642,0.000057265686,0.000008820041,0.000083123385,0.0008583221,0.000074520416,0.0008708037],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988544,0.00029639108,0.00008594975,0.00018029238,0.000470269,0.0001127489],"domain_scores_gemma":[0.9945452,0.0029896835,0.00034746734,0.00029019316,0.0017025373,0.00012494795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004640408,0.00066511193,0.00040424385,0.0013109397,0.0010556545,0.0013151547,0.00095018884,0.0007313504,0.00061374326],"category_scores_gemma":[0.011494271,0.00032654658,0.0005035501,0.0018665325,0.0005455384,0.0008166314,0.00075049454,0.00056435406,0.00007126825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012228605,0.00092594,0.18637669,0.00029235973,0.00070863595,0.00015807524,0.0006662271,0.61428577,0.014758995,0.0025032826,0.0014937482,0.17660733],"study_design_scores_gemma":[0.00014237066,0.00041870328,0.09441256,0.00002756267,0.00017588645,0.00008840163,0.0004677845,0.8954049,0.0069985627,0.0005377385,0.0012644281,0.000061007217],"about_ca_topic_score_codex":0.64110845,"about_ca_topic_score_gemma":0.7205341,"teacher_disagreement_score":0.35889155,"about_ca_system_score_codex":0.0056847213,"about_ca_system_score_gemma":0.007439632,"threshold_uncertainty_score":0.72201025},"labels":[],"label_agreement":null},{"id":"W2081090308","doi":"10.1016/j.rse.2012.05.008","title":"Biases in long-term NO2 averages inferred from satellite observations due to cloud selection criteria","year":2012,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":52,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Environmental science; Ozone Monitoring Instrument; Troposphere; Satellite; Trace gas; Atmospheric sciences; Meteorology; Cloud fraction; Cloud cover; Cloud computing; Geography; Physics","score_opus":0.03735090834614484,"score_gpt":0.24154055240183006,"score_spread":0.20418964405568524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081090308","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9961515,0.00025011113,0.0022204851,0.00005257084,0.000041951833,0.000006299383,0.00051278475,0.000052988362,0.0007113719],"genre_scores_gemma":[0.9982936,0.000058743026,0.0008036114,0.000024223551,0.000026313306,0.000003026389,0.00065496215,0.000018809864,0.000116801624],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994987,0.0000924802,0.00006794123,0.00012804058,0.000101662474,0.00011122641],"domain_scores_gemma":[0.9967361,0.0017494028,0.00045630697,0.00032386277,0.00059122074,0.00014311199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001524382,0.0002784464,0.00028184915,0.0006326377,0.00038262134,0.0007746034,0.00038672698,0.00040431667,0.0004815198],"category_scores_gemma":[0.0054061767,0.00021094621,0.00034356964,0.00082422374,0.00022730524,0.00074779673,0.0003435827,0.00021642723,0.00011652589],"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":[0.001051723,0.000101647434,0.8921982,0.00014973372,0.0005111737,0.00018012621,0.00018387061,0.032406937,0.047364995,0.00081651635,0.00092281576,0.024112264],"study_design_scores_gemma":[0.000049240683,0.000043645137,0.9202251,0.00002389755,0.00018657911,0.00007950663,0.00006532918,0.06517708,0.012701381,0.00076064875,0.00065827725,0.000029327453],"about_ca_topic_score_codex":0.015721193,"about_ca_topic_score_gemma":0.026445184,"teacher_disagreement_score":0.015721193,"about_ca_system_score_codex":0.00069684867,"about_ca_system_score_gemma":0.00054335647,"threshold_uncertainty_score":0.031259418},"labels":[],"label_agreement":null},{"id":"W2082081125","doi":"10.1016/j.rse.2011.11.020","title":"A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery","year":2011,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":973,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Total (Canada); Trent University; University of Saskatchewan","funders":"","keywords":"Support vector machine; Artificial intelligence; Land cover; Pixel; Computer science; Random forest; Decision tree; Statistical classification; Pattern recognition (psychology); Object (grammar); Contextual image classification; Machine learning; Algorithm; Image (mathematics); Land use","score_opus":0.024976375501351233,"score_gpt":0.2432161822838509,"score_spread":0.21823980678249966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082081125","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81899965,0.0018185059,0.17258717,0.0002626894,0.00012897333,0.00012664605,0.0006122728,0.0012302796,0.0042338506],"genre_scores_gemma":[0.87809855,0.00057506614,0.11832037,0.000107712935,0.000048555903,0.000058839727,0.0008778384,0.0001882602,0.0017248769],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99930465,0.00022344806,0.000051517647,0.00010013889,0.00027455974,0.000045762445],"domain_scores_gemma":[0.99755895,0.0013982746,0.00009858391,0.000107902146,0.00079072727,0.000045580713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023653666,0.0004943994,0.0005439674,0.0018696194,0.0002927082,0.0011407624,0.00063097756,0.0007543593,0.001398298],"category_scores_gemma":[0.0031284913,0.00018008414,0.00052561966,0.0012164162,0.00029102634,0.0012597764,0.00032732487,0.00030208763,0.00047788073],"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":[0.005871685,0.00077105337,0.053870555,0.00063055597,0.0009868931,0.00018206312,0.00028739133,0.049170606,0.10202434,0.0013990623,0.0031743455,0.78163147],"study_design_scores_gemma":[0.00015726079,0.0008081697,0.1255817,0.000032368956,0.00050524506,0.0002449967,0.00029902553,0.83480537,0.034438178,0.00095615047,0.002103461,0.00006808214],"about_ca_topic_score_codex":0.0046950197,"about_ca_topic_score_gemma":0.0066440483,"teacher_disagreement_score":0.0046950197,"about_ca_system_score_codex":0.00042523854,"about_ca_system_score_gemma":0.00038666895,"threshold_uncertainty_score":0.012509406},"labels":[],"label_agreement":null},{"id":"W2082263501","doi":"10.1016/j.rse.2009.03.007","title":"A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS","year":2009,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":702,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; Natural Resources Canada; Canadian Forest Service; University of British Columbia","funders":"","keywords":"Remote sensing; Vegetation (pathology); Temporal resolution; Environmental science; Disturbance (geology); Land cover; Image resolution; Range (aeronautics); Sensor fusion; Geography; Computer science; Land use; Ecology; Geology","score_opus":0.019169217681720495,"score_gpt":0.21160731609075703,"score_spread":0.19243809840903653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082263501","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031524427,0.000188186,0.9657774,0.00016408398,0.00012918081,0.0000446741,0.00017510295,0.0012691591,0.00072774995],"genre_scores_gemma":[0.6288517,0.00027740598,0.36628884,0.00018277418,0.000116685,0.0002492829,0.0008779949,0.00016971497,0.0029855738],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996221,0.000045015,0.000029805355,0.00011267211,0.00015048336,0.00003994396],"domain_scores_gemma":[0.9995765,0.000102887985,0.00004492049,0.000041414358,0.00020856854,0.000025751031],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013157399,0.0006591069,0.0011396485,0.00062403805,0.0005187451,0.0008502589,0.0013299495,0.0007840781,0.0009846911],"category_scores_gemma":[0.0014286769,0.00055029604,0.00082593295,0.0008244468,0.0003209922,0.002010565,0.00088771404,0.00086779415,0.0003413982],"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":[0.00019904498,0.00016090619,0.002790157,0.00006466262,0.00020075655,0.00009103233,0.00008144412,0.79836273,0.012701725,0.0042528813,0.0026941039,0.17840052],"study_design_scores_gemma":[0.0000045138627,0.0000069818393,0.0002621993,0.0000011042533,0.000011917922,0.000007635417,0.000002366445,0.998243,0.00066612154,0.0005210068,0.00026771188,0.000005449547],"about_ca_topic_score_codex":0.012651922,"about_ca_topic_score_gemma":0.013875095,"teacher_disagreement_score":0.012651922,"about_ca_system_score_codex":0.00091512885,"about_ca_system_score_gemma":0.0010794398,"threshold_uncertainty_score":0.025156558},"labels":[],"label_agreement":null},{"id":"W2082472350","doi":"10.1016/j.rse.2009.07.003","title":"Mapping forest background reflectivity over North America with Multi-angle Imaging SpectroRadiometer (MISR) data","year":2009,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":78,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Remote sensing; Spectroradiometer; Environmental science; Moderate-resolution imaging spectroradiometer; Nadir; Reflectivity; Deciduous; Bidirectional reflectance distribution function; Taiga; Vegetation (pathology); Land cover; Normalized Difference Vegetation Index; Leaf area index; Atmospheric correction; Canopy; Geography; Land use; Satellite; Forestry; Optics","score_opus":0.025139240927646505,"score_gpt":0.24470315054420108,"score_spread":0.2195639096165546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082472350","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9974477,0.00010653466,0.0008221291,0.000055975644,0.000003345984,0.000008510601,0.00050911104,0.00008660645,0.00096006325],"genre_scores_gemma":[0.9956703,0.00011679939,0.0031209702,0.000015668682,0.0000041344265,0.000009107712,0.00070891884,0.000009298751,0.00034476252],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998926,0.000012332083,0.000004255019,0.00003275722,0.000032076467,0.000025951054],"domain_scores_gemma":[0.99981505,0.000022815982,0.00002392577,0.000022034264,0.00009029792,0.000025789252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023577361,0.00024352038,0.00019630109,0.0006313397,0.00037602926,0.00031340003,0.00024158864,0.000250562,0.00033779937],"category_scores_gemma":[0.00031727852,0.00018253832,0.00014827441,0.00086842815,0.00015062199,0.00035349422,0.00018648324,0.00018465001,0.00012159383],"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":[0.0006114631,0.00043009536,0.69669217,0.00012969528,0.00022579754,0.00044775673,0.0008911537,0.02648326,0.12834293,0.0005135016,0.0042296546,0.14100246],"study_design_scores_gemma":[0.000040073075,0.000034509063,0.95447034,0.000011769549,0.00007369561,0.000070417154,0.00031956437,0.037954394,0.0049649426,0.00010139248,0.0019444145,0.000014379633],"about_ca_topic_score_codex":0.14847472,"about_ca_topic_score_gemma":0.279698,"teacher_disagreement_score":0.14847472,"about_ca_system_score_codex":0.00045764694,"about_ca_system_score_gemma":0.0006977332,"threshold_uncertainty_score":0.29522103},"labels":[],"label_agreement":null},{"id":"W2082627840","doi":"10.1016/s0034-4257(01)00299-1","title":"Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance","year":2002,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":286,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; McGill University","funders":"Agriculture and Agri-Food Canada","keywords":"Hyperspectral imaging; Spectroradiometer; Environmental science; Canopy; Growing season; Crop; Reflectivity; Agronomy; Remote sensing; Ecology; Biology; Geography","score_opus":0.008283903442224051,"score_gpt":0.21933396226509524,"score_spread":0.2110500588228712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082627840","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9990258,0.000113436676,0.00018913714,0.000018050421,0.0000042203183,0.000001960567,0.000049121525,0.000005386017,0.00059286633],"genre_scores_gemma":[0.99862003,0.00013489825,0.00021107179,0.000026620046,0.0000026811194,0.0000032690034,0.00015837068,0.000012058843,0.0008308807],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998178,0.000031296186,0.0000068270283,0.00004163871,0.000058963535,0.000043567714],"domain_scores_gemma":[0.99950206,0.0002858899,0.00006508139,0.000020025622,0.00006581803,0.00006120645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033381078,0.0002992475,0.00022223702,0.00018980498,0.00028090214,0.0004258241,0.00026485225,0.00037617912,0.0009691839],"category_scores_gemma":[0.0006509738,0.00020299296,0.00017228368,0.0002098999,0.0004286342,0.00054228836,0.00031155243,0.00028822396,0.00013162312],"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":[0.0045912717,0.00018551669,0.029953286,0.00006764315,0.00006895611,0.00024175986,0.00008345202,0.0042550955,0.95440745,0.00020444237,0.000112064714,0.0058290246],"study_design_scores_gemma":[0.00010159941,0.0007185967,0.56964076,0.000010029548,0.000085668435,0.0002439978,0.00029915542,0.020706167,0.40670204,0.0004239421,0.0010272568,0.000040833173],"about_ca_topic_score_codex":0.011999014,"about_ca_topic_score_gemma":0.015904242,"teacher_disagreement_score":0.011999014,"about_ca_system_score_codex":0.00057355815,"about_ca_system_score_gemma":0.00031744645,"threshold_uncertainty_score":0.023858309},"labels":[],"label_agreement":null},{"id":"W2082742619","doi":"10.1016/j.rse.2014.06.022","title":"The seasonal cycle of satellite chlorophyll fluorescence observations and its relationship to vegetation phenology and ecosystem atmosphere carbon exchange","year":2014,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":375,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Aeronautics and Space Administration; Natural Resources Canada; U.S. Department of Agriculture; Natural Sciences and Engineering Research Council of Canada; National Oceanic and Atmospheric Administration; Oak Ridge National Laboratory; Biological and Environmental Research; Canadian Foundation for Climate and Atmospheric Sciences; U.S. Department of Energy; Università degli Studi della Tuscia; Environment Canada; Australian Research Council; European Organization for the Exploitation of Meteorological Satellites; National Science Foundation","keywords":"Environmental science; Photosynthetically active radiation; Satellite; Remote sensing; Carbon cycle; Vegetation (pathology); Primary production; Atmospheric sciences; Chlorophyll fluorescence; Eddy covariance; FluxNet; Seasonality; Ecosystem; Chlorophyll; Geography; Photosynthesis; Ecology; Geology","score_opus":0.01134867586554522,"score_gpt":0.19343222682785344,"score_spread":0.18208355096230822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082742619","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99821776,0.0001828294,0.00038004247,0.00006538995,0.000008319847,0.0000040155146,0.00052364497,0.000011027591,0.00060704094],"genre_scores_gemma":[0.9988092,0.000084039355,0.00020638625,0.000027987124,0.000009037675,0.0000045273764,0.00052634685,0.00000783739,0.00032461502],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99993575,0.000015322727,0.000006657421,0.000018414925,0.000010931585,0.000012823928],"domain_scores_gemma":[0.9987779,0.0006425891,0.0002560006,0.00006525541,0.00015281218,0.000105470215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036815068,0.00012017611,0.0001410729,0.0005473016,0.00016895455,0.00035303654,0.00016427832,0.00032762397,0.0010445521],"category_scores_gemma":[0.0018917845,0.00020026727,0.00016261046,0.00065288495,0.00020389029,0.00034123892,0.00020378748,0.00021205148,0.00022171362],"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":[0.00022655362,0.000056583285,0.9760991,0.000022312855,0.000082021455,0.000079544334,0.00008698128,0.0013203179,0.0140502555,0.00019385792,0.00028843398,0.007493945],"study_design_scores_gemma":[0.000003213681,0.000013596523,0.9976755,0.0000016395556,0.0000071491863,0.000039961684,0.000017024386,0.0017863577,0.00023759989,0.000048318874,0.00016736111,0.000002358343],"about_ca_topic_score_codex":0.006305533,"about_ca_topic_score_gemma":0.0098329885,"teacher_disagreement_score":0.006305533,"about_ca_system_score_codex":0.00026886066,"about_ca_system_score_gemma":0.00023331953,"threshold_uncertainty_score":0.012537658},"labels":[],"label_agreement":null},{"id":"W2086708373","doi":"10.1016/j.rse.2010.11.001","title":"Spectroscopic determination of leaf water content using continuous wavelet analysis","year":2010,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":272,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Wavelet; Wavelet transform; Remote sensing; Hyperspectral imaging; Mathematics; Computer science; Artificial intelligence; Geology","score_opus":0.013532032168775607,"score_gpt":0.21733304408731835,"score_spread":0.20380101191854275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086708373","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72628003,0.000617342,0.26943552,0.00010435694,0.000053556454,0.000040139825,0.00026997723,0.00022988838,0.0029690377],"genre_scores_gemma":[0.927193,0.00039856034,0.071189195,0.000031305546,0.00002187932,0.0000210121,0.00015151987,0.000035787943,0.00095777755],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999161,0.0000137575325,0.0000029870967,0.00002256178,0.000036527625,0.000008155524],"domain_scores_gemma":[0.9998418,0.00006414224,0.000016199334,0.000023321656,0.000043415752,0.000011086838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021430604,0.00017386083,0.00017743424,0.0003155882,0.00012329649,0.00030087068,0.00021307396,0.00026575403,0.00066504726],"category_scores_gemma":[0.00049694505,0.000119874196,0.00012564409,0.00058582996,0.00018533846,0.00039912137,0.00020200998,0.00033855345,0.00018157176],"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":[0.00012013769,0.000055775516,0.0016646722,0.000060942653,0.000012939207,0.000029025587,0.000046847403,0.0010151055,0.9420207,0.00048200754,0.00017128994,0.05432061],"study_design_scores_gemma":[0.000063917825,0.0003320293,0.050057966,0.00002011937,0.000079867496,0.0005687657,0.000118799246,0.20544842,0.7385425,0.0019977142,0.0027184633,0.000051509127],"about_ca_topic_score_codex":0.00049666327,"about_ca_topic_score_gemma":0.00060727895,"teacher_disagreement_score":0.00066504726,"about_ca_system_score_codex":0.00009284752,"about_ca_system_score_gemma":0.00010829506,"threshold_uncertainty_score":0.002224803},"labels":[],"label_agreement":null},{"id":"W2086975319","doi":"10.1016/j.rse.2006.01.017","title":"Spatial scaling of evapotranspiration as affected by heterogeneities in vegetation, topography, and soil texture","year":2006,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":85,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Evapotranspiration; Remote sensing; Pixel; Environmental science; Land cover; Spatial variability; Watershed; Vegetation (pathology); Soil texture; Scaling; Image resolution; Boreal; Spatial heterogeneity; Soil science; Land use; Soil water; Geology; Computer science; Mathematics; Geometry","score_opus":0.0036610101450264823,"score_gpt":0.18312699145474196,"score_spread":0.17946598130971547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086975319","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9992054,0.00003937703,0.00032529363,0.000023858971,0.0000024203064,0.0000010716952,0.000056068613,0.000008932399,0.0003375371],"genre_scores_gemma":[0.99974185,0.000013476808,0.00008689717,0.0000031627187,0.0000030745318,0.000001066662,0.000053589327,0.000004744128,0.000092137234],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998913,0.000034628218,0.00000740677,0.00003326697,0.000015838976,0.00001760703],"domain_scores_gemma":[0.9976674,0.0013417739,0.00036557345,0.00024063839,0.000258638,0.00012592395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041024247,0.00010900725,0.0001746021,0.00040911365,0.00014491819,0.0003863598,0.00010610086,0.00016639222,0.0008613183],"category_scores_gemma":[0.0026413237,0.0001833129,0.0002119973,0.0003622596,0.00035593403,0.0003771436,0.00023466752,0.00019337203,0.00013231927],"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":[0.0010523476,0.00012085233,0.8651652,0.000048039332,0.00022122069,0.00043736643,0.00086622883,0.024898972,0.086168684,0.001770503,0.0006327247,0.018617759],"study_design_scores_gemma":[0.0000052199725,0.000023660103,0.9880215,0.0000015449982,0.000011467321,0.000088886685,0.00007981273,0.010871744,0.00043905462,0.00036249167,0.0000872364,0.000007361263],"about_ca_topic_score_codex":0.0028413178,"about_ca_topic_score_gemma":0.0030587993,"teacher_disagreement_score":0.0028413178,"about_ca_system_score_codex":0.00016089354,"about_ca_system_score_gemma":0.00012559044,"threshold_uncertainty_score":0.005649507},"labels":[],"label_agreement":null},{"id":"W2088548373","doi":"10.1016/j.rse.2005.01.006","title":"Evaluation of spring snow covered area depletion in the Canadian Arctic from NOAA snow charts","year":2005,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":71,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"National Oceanic and Atmospheric Administration","keywords":"Snowmelt; Snow; Northern Hemisphere; Environmental science; Arctic; Advanced very-high-resolution radiometer; Climatology; Latitude; Snow cover; Spring (device); Cloud cover; Snow line; Snow field; Physical geography; Meteorology; Geology; Satellite; Oceanography; Geography","score_opus":0.041294471679548035,"score_gpt":0.21628497613493758,"score_spread":0.17499050445538955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088548373","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97651553,0.0003186323,0.0005905502,0.00010614722,0.000015574109,0.00006411392,0.013558666,0.00011457042,0.00871618],"genre_scores_gemma":[0.9884066,0.0001935599,0.0010649568,0.000014187564,0.0000057531056,0.000021016629,0.0090724025,0.000018162202,0.0012032014],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992539,0.000060130988,0.000046723086,0.00008301907,0.00039911945,0.00015707445],"domain_scores_gemma":[0.9957997,0.0003663078,0.00023930604,0.00010531751,0.003192528,0.00029678966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012118667,0.0004330422,0.00031244598,0.003335068,0.0012847356,0.0015579439,0.0008572723,0.00029870364,0.0010653051],"category_scores_gemma":[0.0049979123,0.00020774598,0.00030737714,0.0045461114,0.0002313348,0.0005040867,0.0005549243,0.00022281475,0.00025613487],"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":[0.00041843174,0.000072358074,0.94916564,0.000082283215,0.00011044396,0.000105967265,0.00045880023,0.01210769,0.0012478906,0.00034243747,0.0031695096,0.032718554],"study_design_scores_gemma":[0.00000885728,0.00002137714,0.981065,0.000023164514,0.000041052113,0.000020412961,0.0005674635,0.014537253,0.0006584573,0.000040440915,0.003002492,0.000014015113],"about_ca_topic_score_codex":0.97071326,"about_ca_topic_score_gemma":0.98711944,"teacher_disagreement_score":0.029286742,"about_ca_system_score_codex":0.009761122,"about_ca_system_score_gemma":0.0103539,"threshold_uncertainty_score":0.07082224},"labels":[],"label_agreement":null},{"id":"W2088595526","doi":"10.1016/j.rse.2004.03.002","title":"Simplified atmospheric radiative transfer modelling for estimating incident PAR using MODIS atmosphere products","year":2004,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":107,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"University of Alberta","keywords":"Atmosphere (unit); Environmental science; Moderate-resolution imaging spectroradiometer; Radiative transfer; Aerosol; Remote sensing; Spectroradiometer; Photosynthetically active radiation; Rayleigh scattering; Atmospheric radiative transfer codes; Water vapor; Opacity; Atmospheric sciences; Vegetation (pathology); Attenuation; Atmospheric optics; Scattering; Meteorology; Optics; Physics; Geology; Reflectivity; Chemistry; Satellite","score_opus":0.01844661409295274,"score_gpt":0.21850633114429163,"score_spread":0.2000597170513389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088595526","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17300987,0.0005008546,0.8122316,0.0002551018,0.00015426679,0.000093860675,0.0016882456,0.005265931,0.006800212],"genre_scores_gemma":[0.85938615,0.00033278187,0.13314888,0.00007808167,0.00007463015,0.00015465246,0.0014612926,0.0006358944,0.004727703],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982363,0.00004342747,0.000012782419,0.00003942823,0.000056933786,0.000023825582],"domain_scores_gemma":[0.9997725,0.0000838014,0.000018942086,0.000053103176,0.00006204886,0.00000960703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003205625,0.00067782897,0.00077706866,0.00037096578,0.0004330055,0.0007046124,0.0016873202,0.00071348494,0.0023777788],"category_scores_gemma":[0.0011083373,0.00070552394,0.00097506377,0.0007298479,0.00021031259,0.0009997126,0.00048692233,0.0006070385,0.0006779449],"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":[0.000040084597,0.000022490374,0.0004966386,0.000029853238,0.000034532644,0.000039586776,0.00002442986,0.98125976,0.0037509904,0.0010814483,0.0006694286,0.012550766],"study_design_scores_gemma":[0.0000067109827,0.0000025582542,0.00025505686,8.770908e-7,0.0000062728573,0.0000046662217,0.000001782281,0.99896204,0.00035130506,0.00022226648,0.0001823336,0.00000415915],"about_ca_topic_score_codex":0.052024104,"about_ca_topic_score_gemma":0.041913852,"teacher_disagreement_score":0.052024104,"about_ca_system_score_codex":0.0008012036,"about_ca_system_score_gemma":0.00079049356,"threshold_uncertainty_score":0.10344255},"labels":[],"label_agreement":null},{"id":"W2089377323","doi":"10.1016/j.rse.2011.10.017","title":"Burned area mapping time series in Canada (1984–1999) from NOAA-AVHRR LTDR: A comparison with other remote sensing products and fire perimeters","year":2011,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":52,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Remote sensing; Environmental science; Boreal; Advanced very-high-resolution radiometer; Taiga; Meteorology; Physical geography; Geography; Forestry; Satellite","score_opus":0.014352410799540768,"score_gpt":0.17210414247033778,"score_spread":0.157751731670797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089377323","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9384136,0.0010820584,0.00053230696,0.00017829132,0.000033754866,0.000033366032,0.055504397,0.00019149567,0.0040306193],"genre_scores_gemma":[0.95211715,0.0007800344,0.002058844,0.00005590565,0.00001800954,0.000028410908,0.041050922,0.00004653595,0.0038442288],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997031,0.000012286328,0.000018804114,0.00005490891,0.00014615686,0.00006470919],"domain_scores_gemma":[0.998659,0.00008060297,0.00016368626,0.00004422229,0.0009111951,0.00014122714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004027871,0.00041079315,0.00035791713,0.0027023635,0.00074385887,0.0007801162,0.0007356482,0.00032906173,0.0015459233],"category_scores_gemma":[0.001098402,0.0002168784,0.00031738714,0.0055821594,0.0002773093,0.0003659359,0.00028387472,0.00030286366,0.0004119777],"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":[0.0011265292,0.00014997573,0.8708429,0.00036623207,0.00042728346,0.00047495347,0.0010271085,0.015614364,0.0069403425,0.00064771756,0.02359925,0.078783296],"study_design_scores_gemma":[0.000008878631,0.0000069314815,0.9930165,0.000015413387,0.000029942215,0.00005266441,0.00016955721,0.0024911806,0.00042282132,0.000018451034,0.0037566018,0.000011087424],"about_ca_topic_score_codex":0.9881859,"about_ca_topic_score_gemma":0.9923343,"teacher_disagreement_score":0.011814117,"about_ca_system_score_codex":0.009075114,"about_ca_system_score_gemma":0.0070055504,"threshold_uncertainty_score":0.06584489},"labels":[],"label_agreement":null},{"id":"W2089652552","doi":"10.1016/j.rse.2006.09.015","title":"Analysis of climate change impacts on lake ice phenology in Canada using the historical satellite data record","year":2006,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":226,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Natural Resources Canada","funders":"Natural Resources Canada","keywords":"Phenology; Advanced very-high-resolution radiometer; Climatology; Climate change; Environmental science; Satellite; Physical geography; Geography; Geology; Oceanography; Ecology","score_opus":0.03288282895089386,"score_gpt":0.2137517673915014,"score_spread":0.18086893844060753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089652552","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99471325,0.0003382645,0.00009998813,0.0002440139,0.0000080281625,0.000008059484,0.00345,0.000017526643,0.0011208546],"genre_scores_gemma":[0.99574536,0.00031704918,0.00017378663,0.000034271856,0.0000048357874,0.0000046941677,0.0024970584,0.00000777756,0.0012151919],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99975115,0.000022028024,0.000013902589,0.000036657206,0.0000840115,0.00009222566],"domain_scores_gemma":[0.9987367,0.00016662627,0.00011542381,0.000030649026,0.0007522588,0.00019843123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048143836,0.00024749656,0.00024075418,0.001345442,0.001060111,0.001057702,0.00055819895,0.00032588324,0.0014143472],"category_scores_gemma":[0.0019998814,0.00021495413,0.00048989354,0.0028228061,0.00041915936,0.00034982493,0.00040524083,0.00034213116,0.0001349063],"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":[0.00027386006,0.000051264382,0.9716034,0.000066507644,0.0002737632,0.00018997287,0.00034366213,0.0089113675,0.000981642,0.00048638615,0.0021635497,0.014654683],"study_design_scores_gemma":[0.000009815528,0.000009311128,0.99209857,0.000010805724,0.000059244117,0.000020917,0.0004327419,0.005650122,0.00022188501,0.000039391805,0.0014380576,0.00000922694],"about_ca_topic_score_codex":0.99381566,"about_ca_topic_score_gemma":0.99616194,"teacher_disagreement_score":0.017665867,"about_ca_system_score_codex":0.017665867,"about_ca_system_score_gemma":0.015728213,"threshold_uncertainty_score":0.12817538},"labels":[],"label_agreement":null},{"id":"W2089882657","doi":"10.1016/j.rse.2007.01.012","title":"Hyperspectral discrimination of tropical dry forest lianas and trees: Comparative data reduction approaches at the leaf and canopy levels","year":2007,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":119,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Hyperspectral imaging; Liana; Principal component analysis; Pattern recognition (psychology); Artificial intelligence; Remote sensing; Crown (dentistry); Dimensionality reduction; Feature selection; Canopy; Mathematics; Computer science; Botany; Biology; Geography","score_opus":0.0762875960998628,"score_gpt":0.2600414305683219,"score_spread":0.1837538344684591,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089882657","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.979914,0.00021049805,0.016102733,0.00006834765,0.0000062935414,0.000017850338,0.00018129444,0.00012715784,0.003371832],"genre_scores_gemma":[0.9866454,0.00023045045,0.011145625,0.000037172464,0.000007989787,0.000019094003,0.00055960973,0.000025330108,0.0013293581],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9999305,0.000013689543,0.0000025958684,0.000011944685,0.000022071637,0.000019240508],"domain_scores_gemma":[0.9998939,0.000032040003,0.00000988409,0.000008603816,0.00003971912,0.000015869922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021938284,0.00023389395,0.00017956342,0.0005159636,0.00017508506,0.00039501596,0.000167446,0.00015292325,0.00085393013],"category_scores_gemma":[0.0002588365,0.00010474355,0.00015588362,0.0002913959,0.00018261351,0.00032006108,0.00019436154,0.00020171415,0.00020463292],"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":[0.0008453786,0.00018119036,0.036055826,0.00017334938,0.00008785732,0.000089560155,0.00061330287,0.009241589,0.72172016,0.00092399545,0.0006654712,0.2294023],"study_design_scores_gemma":[0.00008571335,0.0002074108,0.66734546,0.00003681598,0.00021109698,0.00039295896,0.0011746387,0.15270083,0.17194581,0.0015173443,0.0043010036,0.00008095204],"about_ca_topic_score_codex":0.0050745932,"about_ca_topic_score_gemma":0.011831292,"teacher_disagreement_score":0.0050745932,"about_ca_system_score_codex":0.0001394216,"about_ca_system_score_gemma":0.0002006082,"threshold_uncertainty_score":0.010090113},"labels":[],"label_agreement":null},{"id":"W2090626095","doi":"10.1016/j.rse.2005.05.003","title":"Global mapping of foliage clumping index using multi-angular satellite data","year":2005,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":498,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Remote sensing; Leaf area index; Hotspot (geology); Shrub; Environmental science; Canopy; Vegetation Index; Vegetation (pathology); Enhanced vegetation index; Normalized Difference Vegetation Index; Geography; Geology; Ecology","score_opus":0.0411165642735542,"score_gpt":0.2634772275268146,"score_spread":0.22236066325326037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090626095","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.986299,0.00028778074,0.0063992487,0.000049867016,0.000018050234,0.000033115793,0.003638094,0.0004829835,0.0027918068],"genre_scores_gemma":[0.98091745,0.00013072854,0.012674096,0.000015093579,0.000020281597,0.000024476365,0.0053747296,0.00004955786,0.00079359487],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99987936,0.000010624404,0.0000040472555,0.000047822887,0.00003467751,0.00002346259],"domain_scores_gemma":[0.99969447,0.0000356788,0.00006369345,0.000048390117,0.000096910604,0.00006093904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025220067,0.00033719462,0.00035070913,0.002678027,0.00024095144,0.00043299023,0.00028434602,0.00029472072,0.00086129573],"category_scores_gemma":[0.00032573787,0.00017638486,0.0002833615,0.0024171902,0.00017233248,0.00043980856,0.00033696357,0.00021771438,0.00031310527],"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":[0.0010148932,0.0003785813,0.4341643,0.00025036014,0.00025787274,0.00033152878,0.0006047174,0.058007438,0.24600366,0.0008324973,0.004542783,0.25361145],"study_design_scores_gemma":[0.000065667315,0.00007136465,0.9276072,0.000013321607,0.00006445015,0.00008731338,0.00010965421,0.062912166,0.0060839322,0.00019313676,0.002758671,0.000033174103],"about_ca_topic_score_codex":0.02290732,"about_ca_topic_score_gemma":0.034515835,"teacher_disagreement_score":0.02290732,"about_ca_system_score_codex":0.00037803952,"about_ca_system_score_gemma":0.0003586552,"threshold_uncertainty_score":0.045547962},"labels":[],"label_agreement":null},{"id":"W2090977430","doi":"10.1016/j.rse.2011.09.021","title":"Evaluation of passive microwave brightness temperature simulations and snow water equivalent retrievals through a winter season","year":2011,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":82,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; Université de Sherbrooke; Environment and Climate Change Canada","funders":"Canadian Space Agency; Natural Environment Research Council; Sight Research UK","keywords":"Snowpack; Environmental science; Snow; Brightness temperature; Radiometer; Remote sensing; Microwave; Microwave radiometer; Mean squared error; Atmospheric sciences; Meteorology; Geology; Geography; Physics","score_opus":0.053136355178866036,"score_gpt":0.2397723778910106,"score_spread":0.18663602271214458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090977430","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99347866,0.00007896189,0.003777618,0.0001142413,0.000035688015,0.00003662385,0.0005455696,0.0004352943,0.001497347],"genre_scores_gemma":[0.9948619,0.00003201458,0.0036832062,0.000029359548,0.0000100230445,0.000017280603,0.0006911681,0.0000748288,0.0006002382],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961984,0.00013576518,0.000029238501,0.00010290029,0.000056841356,0.000055433462],"domain_scores_gemma":[0.997677,0.0014551159,0.00013751944,0.00016754598,0.00043454266,0.00012828235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001514526,0.0010877288,0.000661555,0.00039486046,0.0006392167,0.0007710331,0.00091538613,0.001137267,0.0011838417],"category_scores_gemma":[0.0030802302,0.00065116584,0.0006963295,0.00048234005,0.00039152562,0.0009687491,0.0003145618,0.00044826177,0.00028181903],"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":[0.0021774468,0.00072086375,0.021239879,0.000083056206,0.00021688077,0.00014956263,0.00012765774,0.9424688,0.014703417,0.00023272527,0.00081073586,0.017068865],"study_design_scores_gemma":[0.0001979833,0.00030058168,0.007869005,0.0000051693232,0.00005894835,0.00001706779,0.00005449892,0.98616517,0.0050183167,0.00006669144,0.00023005485,0.000016577127],"about_ca_topic_score_codex":0.04555806,"about_ca_topic_score_gemma":0.02745421,"teacher_disagreement_score":0.04555806,"about_ca_system_score_codex":0.00092868035,"about_ca_system_score_gemma":0.00082471315,"threshold_uncertainty_score":0.09058577},"labels":[],"label_agreement":null},{"id":"W2093608156","doi":"10.1016/j.rse.2010.07.011","title":"Soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data","year":2010,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":212,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada; University of Guelph; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Water content; Environmental science; Synthetic aperture radar; Surface roughness; Soil science; Remote sensing; Vegetation (pathology); Geology; Materials science","score_opus":0.016136708281322708,"score_gpt":0.23081060170591167,"score_spread":0.21467389342458895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093608156","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9905409,0.0002022685,0.0072296453,0.00007766454,0.000015328478,0.000009944303,0.00080732355,0.00027173542,0.0008451251],"genre_scores_gemma":[0.99149305,0.000114854054,0.006706819,0.000022551407,0.00001737089,0.000008293793,0.0010711355,0.000025591795,0.000540408],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9999136,0.000011937321,0.0000043722134,0.000025037702,0.000023561177,0.000021459407],"domain_scores_gemma":[0.99989176,0.000029346504,0.000017484535,0.000013278809,0.000031188767,0.000016827373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020498324,0.00045030724,0.0003237132,0.00070251443,0.00016851115,0.00036738196,0.00026388915,0.0003562586,0.0007130811],"category_scores_gemma":[0.00033250693,0.00026912687,0.00021517971,0.0006988758,0.0001480687,0.00063814415,0.00021289282,0.00023082759,0.00022028835],"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":[0.0018273212,0.00037601174,0.04802023,0.00024887724,0.00020412174,0.00031011458,0.00013413289,0.13085343,0.67973566,0.000419021,0.0021027885,0.13576826],"study_design_scores_gemma":[0.00041301074,0.00018465566,0.26970232,0.00001845598,0.0001806732,0.00020044987,0.00016162347,0.6407497,0.08623492,0.00048374134,0.0016052276,0.000065149194],"about_ca_topic_score_codex":0.0064157587,"about_ca_topic_score_gemma":0.010943096,"teacher_disagreement_score":0.0064157587,"about_ca_system_score_codex":0.00032541476,"about_ca_system_score_gemma":0.0003182385,"threshold_uncertainty_score":0.0127568245},"labels":[],"label_agreement":null},{"id":"W2094451874","doi":"10.1016/j.rse.2006.09.010","title":"Comparison of MODIS, eddy covariance determined and physiologically modelled gross primary production (GPP) in a Douglas-fir forest stand","year":2006,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":122,"is_retracted":false,"has_abstract":false,"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":"Eddy covariance; Primary production; Environmental science; Photosynthetically active radiation; Moderate-resolution imaging spectroradiometer; Atmospheric sciences; Ecosystem respiration; Canopy; Evergreen; Terrestrial ecosystem; Temperate forest; Vegetation (pathology); Carbon cycle; Remote sensing; Climatology; Ecosystem; Satellite; Ecology; Geology; Photosynthesis","score_opus":0.011271517875681453,"score_gpt":0.21455090280821948,"score_spread":0.20327938493253803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094451874","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99954164,0.000016773693,0.000085302214,0.000009871952,0.0000022508755,0.0000011851095,0.00013958875,0.000007484317,0.00019604161],"genre_scores_gemma":[0.99947757,0.000009899187,0.00014598014,0.000005864056,0.0000013617516,0.0000014810884,0.0002181615,0.0000026477085,0.00013709604],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99991834,0.000012997208,0.00000697858,0.00003041782,0.000017024266,0.000014163527],"domain_scores_gemma":[0.9995559,0.00019053645,0.000031606305,0.000025182408,0.00013827204,0.00005855666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037250825,0.00019069131,0.00018406271,0.00020294535,0.00031407695,0.0004579823,0.00028858279,0.00047269015,0.0005271806],"category_scores_gemma":[0.0008290507,0.00016279928,0.00020939657,0.00019385274,0.00023416216,0.00032294873,0.0001534636,0.00016696309,0.00013083304],"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":[0.004535538,0.00089591293,0.74647146,0.00016755801,0.00028249578,0.0008857683,0.0006839898,0.04852571,0.17600729,0.001046454,0.0012747694,0.01922322],"study_design_scores_gemma":[0.000041928542,0.00014130979,0.9551248,0.0000040464374,0.000042464475,0.00012560024,0.00015721143,0.039907012,0.0040885876,0.00011791312,0.00023761751,0.00001150049],"about_ca_topic_score_codex":0.03427655,"about_ca_topic_score_gemma":0.060202107,"teacher_disagreement_score":0.03427655,"about_ca_system_score_codex":0.0008309797,"about_ca_system_score_gemma":0.00041397798,"threshold_uncertainty_score":0.06815404},"labels":[],"label_agreement":null},{"id":"W2096410503","doi":"10.1016/j.rse.2005.01.009","title":"Satellite mapping of CO emission from forest fires in Northwest America using MOPITT measurements","year":2005,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":54,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Canadian Forest Service; U.S. Forest Service; Goddard Space Flight Center; University of Toronto; York University","keywords":"Environmental science; Advanced very-high-resolution radiometer; Remote sensing; Hotspot (geology); Troposphere; Satellite; Radiometer; Atmospheric sciences; Meteorology; Geology; Geography","score_opus":0.022239430499297705,"score_gpt":0.22385144413611935,"score_spread":0.20161201363682163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096410503","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9967237,0.00010737326,0.00034784572,0.00003114642,0.000007443769,0.000011863002,0.0016220217,0.00005500767,0.0010935211],"genre_scores_gemma":[0.9947836,0.0001256758,0.0022528195,0.000013749083,0.000011664769,0.000020334603,0.0022182462,0.0000111697855,0.0005628186],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99992,0.000006384195,0.0000039508427,0.00002950938,0.000025794445,0.000014394321],"domain_scores_gemma":[0.9997967,0.000022546268,0.000041810126,0.000018688663,0.00008727419,0.00003296872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000155223,0.00024944314,0.000218746,0.0008507781,0.00040942305,0.00034777753,0.00026133106,0.00029521482,0.00052246655],"category_scores_gemma":[0.00026484494,0.00016985701,0.00020235911,0.0010622339,0.00016956445,0.00033294276,0.0002269425,0.00019455641,0.00010470161],"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":[0.0009057236,0.00042562498,0.8478764,0.00013754609,0.00037269574,0.0003560565,0.0004399903,0.02645203,0.068389714,0.00035518812,0.0038931903,0.050395854],"study_design_scores_gemma":[0.0000334277,0.000025808666,0.980146,0.000009069531,0.00004841295,0.000055754,0.00011262962,0.015988009,0.002238594,0.000046921643,0.0012827341,0.0000126033065],"about_ca_topic_score_codex":0.1548825,"about_ca_topic_score_gemma":0.3385536,"teacher_disagreement_score":0.1548825,"about_ca_system_score_codex":0.00055798114,"about_ca_system_score_gemma":0.0004954632,"threshold_uncertainty_score":0.30796194},"labels":[],"label_agreement":null},{"id":"W2096688679","doi":"10.1016/j.rse.2003.11.010","title":"Mapping deciduous forest ice storm damage using Landsat and environmental data","year":2003,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":63,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada; Ministry of Natural Resources","keywords":"Remote sensing; Deciduous; Environmental science; Linear discriminant analysis; Computer science; Reference data; Multispectral image; Artificial intelligence; Data mining; Geography","score_opus":0.02732239899868468,"score_gpt":0.23439449104143342,"score_spread":0.20707209204274873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096688679","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9969465,0.00005950552,0.0011159234,0.000028569691,0.000005194667,0.000016185517,0.0006461596,0.000064962805,0.0011169487],"genre_scores_gemma":[0.99415624,0.00008542507,0.0039953194,0.000011174948,0.000008816688,0.000015445507,0.0010979535,0.000007918597,0.00062176125],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998596,0.000013359597,0.00001058661,0.000029709237,0.000046836372,0.00003986918],"domain_scores_gemma":[0.99971884,0.00005200176,0.00006485112,0.00003090428,0.000081806895,0.000051609924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028591428,0.00028039323,0.00027837072,0.00159685,0.0003838033,0.00041884978,0.00027964413,0.00022648965,0.00073984684],"category_scores_gemma":[0.00050334085,0.00020121549,0.00019236832,0.0011334508,0.00015503573,0.00058145035,0.00025514554,0.00013276844,0.00015230566],"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":[0.0005143241,0.0003996117,0.7647824,0.00012172333,0.00018247725,0.0006411931,0.00057108904,0.033289384,0.033086695,0.00045610088,0.0022541904,0.16370077],"study_design_scores_gemma":[0.000038322156,0.00007381254,0.9258239,0.000014167034,0.000077002755,0.0001884436,0.00058613275,0.06633791,0.0044695744,0.00037482704,0.0019963693,0.00001956875],"about_ca_topic_score_codex":0.036552798,"about_ca_topic_score_gemma":0.106726974,"teacher_disagreement_score":0.036552798,"about_ca_system_score_codex":0.00063489197,"about_ca_system_score_gemma":0.0004995108,"threshold_uncertainty_score":0.072680056},"labels":[],"label_agreement":null},{"id":"W2097179086","doi":"10.1016/j.rse.2014.07.016","title":"Evaluation of WorldView-2 and acoustic remote sensing for mapping benthic habitats in temperate coastal Pacific waters","year":2014,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Coral and Marine Ecosystems Studies","field":"Environmental Science","cited_by":86,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Remote sensing; Satellite imagery; Environmental science; Ground truth; Habitat; Echo sounding; Benthic habitat; Coral reef; Marine protected area; Underwater; Oceanography; Geology; Benthic zone; Ecology; Computer science","score_opus":0.02572166376480286,"score_gpt":0.2343831827102793,"score_spread":0.20866151894547644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097179086","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99641055,0.000105714484,0.0017378028,0.00003768302,0.0000089423875,0.000062701954,0.00038563795,0.000044623164,0.0012063427],"genre_scores_gemma":[0.98629355,0.000153303,0.011615527,0.000034804827,0.000008804874,0.000058072612,0.0012653206,0.000027209417,0.00054347265],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982699,0.0006028932,0.00014985773,0.0003035857,0.0005433457,0.00013041888],"domain_scores_gemma":[0.9940221,0.0033314493,0.0003548104,0.00027353922,0.0016552549,0.00036291027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0068487767,0.00077347335,0.00040624128,0.0013794223,0.00046174802,0.0013329916,0.0009526659,0.00070458156,0.0013571111],"category_scores_gemma":[0.008700232,0.00039080926,0.00058933854,0.0010368263,0.00032720133,0.0016958388,0.00083282863,0.00026893322,0.00021219064],"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":[0.007697176,0.0023886582,0.72956455,0.00041876786,0.0010441855,0.00036258568,0.0010781117,0.045456182,0.036558706,0.0003967305,0.0011656254,0.17386879],"study_design_scores_gemma":[0.00043632055,0.002046595,0.7592463,0.000052754196,0.0006883611,0.00019454893,0.0014707391,0.22420979,0.009940815,0.00017250632,0.0014538686,0.00008739586],"about_ca_topic_score_codex":0.05808278,"about_ca_topic_score_gemma":0.09685047,"teacher_disagreement_score":0.05808278,"about_ca_system_score_codex":0.0010173547,"about_ca_system_score_gemma":0.0011968907,"threshold_uncertainty_score":0.11548942},"labels":[],"label_agreement":null},{"id":"W2097819357","doi":"10.1016/j.rse.2005.07.006","title":"A MODIS-derived photochemical reflectance index to detect inter-annual variations in the photosynthetic light-use efficiency of a boreal deciduous forest","year":2005,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":199,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Université Laval","funders":"Canadian Forest Service; Natural Sciences and Engineering Research Council of Canada; U.S. Forest Service; Canadian Foundation for Climate and Atmospheric Sciences; Goddard Space Flight Center; University of Maryland, Baltimore County; National Aeronautics and Space Administration","keywords":"Photochemical Reflectance Index; Remote sensing; Environmental science; Taiga; Normalized Difference Vegetation Index; Eddy covariance; Atmospheric sciences; Canopy; Enhanced vegetation index; Vegetation (pathology); Boreal; Nadir; Atmosphere (unit); Deciduous; Flux (metallurgy); Leaf area index; Boreal ecosystem; Satellite; Meteorology; Geology; Vegetation Index; Ecosystem; Chemistry; Physics; Botany; Geography","score_opus":0.008013090051860778,"score_gpt":0.22047775504558492,"score_spread":0.21246466499372416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097819357","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9968406,0.00009346694,0.002000587,0.000019662151,0.000014411087,0.000013510596,0.00041600686,0.00011380529,0.00048810305],"genre_scores_gemma":[0.9931766,0.0000352237,0.0059771636,0.00001744741,0.000008348015,0.000011883064,0.00060053146,0.000014963464,0.00015773994],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99987936,0.000012607867,0.00001024343,0.000046386645,0.000035681074,0.000015717957],"domain_scores_gemma":[0.9997582,0.000044994813,0.000044839366,0.00002303519,0.00008042433,0.00004847593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005313987,0.00036606396,0.00038132083,0.00056642987,0.00030479603,0.00032987617,0.00030483207,0.00024813428,0.00024797439],"category_scores_gemma":[0.00046937633,0.00016754327,0.00023731035,0.0005223027,0.00011698269,0.00040410535,0.00014376511,0.0002065268,0.0000862458],"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":[0.001435375,0.0012538519,0.61674696,0.00012902239,0.00035276086,0.00017401576,0.00019927086,0.016339773,0.25566167,0.0002790514,0.0020948504,0.1053334],"study_design_scores_gemma":[0.000069182846,0.00024530536,0.887836,0.000005144187,0.00011705526,0.00019497001,0.00008311844,0.09709303,0.013594801,0.00010836047,0.0006205181,0.00003266287],"about_ca_topic_score_codex":0.007890062,"about_ca_topic_score_gemma":0.01484665,"teacher_disagreement_score":0.007890062,"about_ca_system_score_codex":0.00039361796,"about_ca_system_score_gemma":0.0003145765,"threshold_uncertainty_score":0.0156883},"labels":[],"label_agreement":null},{"id":"W2098653553","doi":"10.1016/j.rse.2004.07.008","title":"Spatial scaling of net primary productivity using subpixel information","year":2004,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":59,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Subpixel rendering; Primary production; Remote sensing; Scaling; Image resolution; Environmental science; Computer science; Leaf area index; Scale (ratio); Land cover; Ecosystem; Pixel; Mathematics; Land use; Geology; Cartography; Artificial intelligence; Geography; Ecology; Geometry","score_opus":0.007644653131580654,"score_gpt":0.18827787270248686,"score_spread":0.1806332195709062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098653553","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9261644,0.0006500679,0.066634215,0.00015924932,0.00009744069,0.00001806819,0.0015150681,0.00081166317,0.003949859],"genre_scores_gemma":[0.9672239,0.00026833088,0.030440288,0.00003265194,0.00005570552,0.000019986246,0.0011770729,0.00011769893,0.00066431693],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998369,0.000022960721,0.000008163321,0.000072405455,0.000043659642,0.00001600088],"domain_scores_gemma":[0.99933004,0.00028287125,0.000066294444,0.00013806293,0.00014975817,0.00003302727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003500404,0.0003119864,0.00034085705,0.0009643381,0.00014927269,0.00056117354,0.00019906086,0.00020314088,0.001316628],"category_scores_gemma":[0.0016310767,0.00025061457,0.00032727246,0.0012126404,0.00016677995,0.00064353534,0.00032254535,0.00024695016,0.00036397154],"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":[0.0007927109,0.00018541691,0.16214524,0.00033493945,0.000364149,0.00018304767,0.00037438745,0.20407821,0.22049874,0.0038432085,0.00435557,0.40284437],"study_design_scores_gemma":[0.000026806907,0.00006867406,0.5361201,0.000018275425,0.00008628489,0.00013717316,0.00007296212,0.44494942,0.012509805,0.003141378,0.0028237978,0.00004520076],"about_ca_topic_score_codex":0.005598891,"about_ca_topic_score_gemma":0.0077726184,"teacher_disagreement_score":0.005598891,"about_ca_system_score_codex":0.00033938995,"about_ca_system_score_gemma":0.00021528928,"threshold_uncertainty_score":0.011132598},"labels":[],"label_agreement":null},{"id":"W2099915029","doi":"10.1016/j.rse.2005.06.016","title":"A multi-angle spectrometer for automatic measurement of plant canopy reflectance spectra","year":2006,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":44,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Spectrometer; Remote sensing; Zenith; Radiance; Solar zenith angle; Imaging spectrometer; Canopy; Optics; Environmental science; Irradiance; Azimuth; Wavelength; Bidirectional reflectance distribution function; Near-infrared spectroscopy; Spectroradiometer; Materials science; Reflectivity; Physics; Geology; Geography","score_opus":0.01706455216650416,"score_gpt":0.21310316302426988,"score_spread":0.19603861085776572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099915029","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22729053,0.0022417943,0.740782,0.00032037153,0.0004288296,0.0005344184,0.003638581,0.012481207,0.0122822905],"genre_scores_gemma":[0.28049126,0.0005874396,0.7095325,0.0003356486,0.000089295405,0.0002307922,0.0017008521,0.000343771,0.006688494],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993838,0.00005850662,0.000023575592,0.00016218038,0.0003363598,0.00003552862],"domain_scores_gemma":[0.9994955,0.00014146464,0.000045980527,0.00008574595,0.0001708674,0.000060366587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005708982,0.0004953501,0.00058728794,0.00080851914,0.0004946643,0.00048654957,0.0008217022,0.0007719197,0.003889214],"category_scores_gemma":[0.00052511116,0.0005043713,0.00031497685,0.00064752414,0.00013737612,0.0010936267,0.0005666021,0.00063761405,0.0014591778],"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":[0.0003861737,0.00015323704,0.0035339587,0.00013172372,0.000049117636,0.000039977207,0.000053442887,0.00074515655,0.8605951,0.00055790675,0.0025237296,0.13123041],"study_design_scores_gemma":[0.00023839425,0.0006891137,0.037520736,0.000045165543,0.00017178903,0.0015506982,0.000094717674,0.12188082,0.80007744,0.00069898175,0.03685502,0.00017705162],"about_ca_topic_score_codex":0.0014204126,"about_ca_topic_score_gemma":0.0036368645,"teacher_disagreement_score":0.003889214,"about_ca_system_score_codex":0.000302255,"about_ca_system_score_gemma":0.00044704796,"threshold_uncertainty_score":0.013010681},"labels":[],"label_agreement":null},{"id":"W2107250927","doi":"10.1016/j.rse.2004.10.011","title":"Automated tree recognition in old growth conifer stands with high resolution digital imagery","year":2004,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":180,"is_retracted":false,"has_abstract":false,"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":"","keywords":"Tree (set theory); Remote sensing; Computer science; Artificial intelligence; Geography; Mathematics","score_opus":0.008687496788263901,"score_gpt":0.19661972457435534,"score_spread":0.18793222778609142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107250927","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96301776,0.00022370231,0.034909334,0.00003904432,0.000015983205,0.000030337445,0.00031629295,0.00037622324,0.0010713035],"genre_scores_gemma":[0.9595032,0.00012947258,0.038267225,0.000015929674,0.000014000477,0.0000189873,0.00075280084,0.000020616495,0.0012779149],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999125,0.000013443407,0.000005402676,0.000018404406,0.000028115252,0.000022154625],"domain_scores_gemma":[0.9996904,0.00012409635,0.000036093716,0.000025339845,0.0000954295,0.000028559396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031943925,0.00017900323,0.0002550998,0.0009678782,0.00017106238,0.00037743707,0.0002855117,0.00019383871,0.00057599327],"category_scores_gemma":[0.000489452,0.00014470085,0.00013871164,0.00045957745,0.00012731104,0.0003349517,0.00017222652,0.00013782406,0.00019065331],"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":[0.00088416564,0.00034838726,0.10474898,0.00014616661,0.00009484739,0.00041507446,0.00023873904,0.048047148,0.19267687,0.00067453407,0.0032555745,0.64846957],"study_design_scores_gemma":[0.000039256178,0.0001418853,0.2608284,0.000014137307,0.00006900557,0.00035635606,0.0002181859,0.7031179,0.032905243,0.0006127876,0.0016749392,0.00002191701],"about_ca_topic_score_codex":0.008461871,"about_ca_topic_score_gemma":0.021373935,"teacher_disagreement_score":0.008461871,"about_ca_system_score_codex":0.0001773336,"about_ca_system_score_gemma":0.00029246075,"threshold_uncertainty_score":0.0168252},"labels":[],"label_agreement":null},{"id":"W2107450192","doi":"10.1016/s0034-4257(02)00104-9","title":"Optimal conditions for wet snow detection using RADARSAT SAR data","year":2002,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Institut national de la recherche scientifique","keywords":"Snow; Remote sensing; Environmental science; Synthetic aperture radar; Surface roughness; Liquid water content; Mode (computer interface); Radar; Snow cover; Meteorology; Geology; Materials science; Computer science; Geography","score_opus":0.07390375731365009,"score_gpt":0.24041080052660485,"score_spread":0.16650704321295476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107450192","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93447226,0.00068148423,0.059257224,0.00030320615,0.00007501458,0.00007104684,0.000711987,0.00029818586,0.004129599],"genre_scores_gemma":[0.9786214,0.00025701363,0.019910341,0.000045685883,0.00006558277,0.000037413967,0.0005732956,0.000054183834,0.00043498195],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9995566,0.000098072815,0.000026970512,0.00007851131,0.00009326562,0.00014655427],"domain_scores_gemma":[0.9969921,0.001890921,0.00023920699,0.000103089464,0.00061260466,0.00016200781],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007764994,0.0003527142,0.00056015095,0.0008495775,0.0006326392,0.00094574253,0.00023247085,0.000594676,0.0022517059],"category_scores_gemma":[0.005647069,0.00042796912,0.00023307963,0.00041212124,0.00041858116,0.0014936356,0.000605619,0.0004385823,0.000657586],"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":[0.007738574,0.0006002953,0.06492867,0.0007006824,0.00019569651,0.0009665942,0.0005503455,0.11438777,0.7038521,0.0057200044,0.0046635317,0.09569581],"study_design_scores_gemma":[0.0009389066,0.0012838572,0.17675112,0.00017060532,0.0003574123,0.0006196072,0.0014266203,0.41710073,0.38367718,0.011622689,0.0058348714,0.0002164933],"about_ca_topic_score_codex":0.0027111226,"about_ca_topic_score_gemma":0.0047557754,"teacher_disagreement_score":0.0027111226,"about_ca_system_score_codex":0.00028013138,"about_ca_system_score_gemma":0.0008997524,"threshold_uncertainty_score":0.007532716},"labels":[],"label_agreement":null},{"id":"W2109404357","doi":"10.1016/j.rse.2004.01.017","title":"Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops","year":2004,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":421,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministerio de Ciencia y Tecnología; Deutsches Zentrum für Luft- und Raumfahrt","keywords":"Hyperspectral imaging; Remote sensing; Canopy; Atmospheric radiative transfer codes; Environmental science; Shadow (psychology); Crown (dentistry); Leaf area index; Pixel; Tree (set theory); Image resolution; Radiative transfer; Mathematics; Computer science; Geography; Botany; Artificial intelligence; Biology","score_opus":0.015434297999523486,"score_gpt":0.25206694948249275,"score_spread":0.23663265148296928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109404357","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9350978,0.00018435223,0.059723895,0.00028357323,0.00003756493,0.000040590774,0.00041970462,0.00055062666,0.0036619806],"genre_scores_gemma":[0.9876913,0.000053133666,0.010970605,0.000025961624,0.000007432138,0.000036505244,0.00024168438,0.000066419176,0.00090692396],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998548,0.000057026748,0.0000091287,0.000028441063,0.000029187726,0.00002129721],"domain_scores_gemma":[0.99742115,0.0021264316,0.00011144961,0.00006886001,0.00021443953,0.000057653615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067590084,0.00055576005,0.0006765979,0.0004326199,0.0005663138,0.00079606293,0.0008159773,0.0014167486,0.0017994171],"category_scores_gemma":[0.0036790823,0.0005333369,0.00056304253,0.00060470705,0.0005032403,0.0010763648,0.0004773312,0.00092891266,0.00019246353],"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":[0.000031620242,0.000040020284,0.00087350176,0.000007259551,0.0000064481146,0.000014017782,0.000011916517,0.99702555,0.00024965874,0.00036534976,0.000079982994,0.0012946296],"study_design_scores_gemma":[0.000002397162,0.0000022454724,0.00008984661,3.078092e-7,7.842676e-7,7.639726e-7,0.0000025310967,0.99975795,0.00006211107,0.00007173677,0.000008275586,0.0000011143386],"about_ca_topic_score_codex":0.04811196,"about_ca_topic_score_gemma":0.025748761,"teacher_disagreement_score":0.04811196,"about_ca_system_score_codex":0.0011781208,"about_ca_system_score_gemma":0.0007510322,"threshold_uncertainty_score":0.095663846},"labels":[],"label_agreement":null},{"id":"W2109421115","doi":"10.1016/j.rse.2006.01.011","title":"Using satellite time-series data sets to analyze fire disturbance and forest recovery across Canada","year":2006,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":247,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Oceanic and Atmospheric Administration; National Aeronautics and Space Administration","keywords":"Normalized Difference Vegetation Index; Environmental science; Advanced very-high-resolution radiometer; Biome; Disturbance (geology); Boreal; Vegetation (pathology); Land cover; Ecoregion; Fire regime; Physical geography; Satellite imagery; Taiga; Remote sensing; Ecological succession; Biogeochemical cycle; Satellite; Climatology; Ecosystem; Land use; Climate change; Geography; Forestry; Ecology; Geology","score_opus":0.010505074569091225,"score_gpt":0.21851090841553786,"score_spread":0.20800583384644664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109421115","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99624133,0.00021253142,0.0002780137,0.0001000918,0.0000057577427,0.000017306676,0.002255966,0.000028535766,0.0008605964],"genre_scores_gemma":[0.99402267,0.00026263235,0.0010313183,0.00003383075,0.000004287288,0.000014147194,0.0037597434,0.000011662641,0.0008597163],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999627,0.000032425196,0.000024653442,0.0000749138,0.00013751413,0.00010342867],"domain_scores_gemma":[0.99782073,0.00036771625,0.00029556494,0.000100298384,0.0011821887,0.00023348001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007164061,0.00033181565,0.00027654073,0.0024606485,0.0012362795,0.0012135005,0.0006356451,0.00032508202,0.00064738],"category_scores_gemma":[0.0032638467,0.00027632408,0.0003724692,0.00515528,0.0004812415,0.0004271163,0.0004221331,0.00041389195,0.000109085755],"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":[0.00019663888,0.00012559685,0.953891,0.000045710076,0.00034557868,0.00016731504,0.00066220533,0.010774686,0.0015965208,0.00028958288,0.0022290733,0.029676124],"study_design_scores_gemma":[0.000018682806,0.000012440092,0.9858007,0.000015646972,0.000070330345,0.000025510833,0.00096381066,0.011072169,0.0003279665,0.00007162521,0.0016066169,0.000014611548],"about_ca_topic_score_codex":0.9962333,"about_ca_topic_score_gemma":0.997707,"teacher_disagreement_score":0.016177444,"about_ca_system_score_codex":0.016177444,"about_ca_system_score_gemma":0.012287643,"threshold_uncertainty_score":0.11737615},"labels":[],"label_agreement":null},{"id":"W2110551686","doi":"10.1016/s0034-4257(02)00027-5","title":"Estimating fire-related parameters in boreal forest using SPOT VEGETATION","year":2002,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":126,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada","funders":"Canadian Forest Service","keywords":"Taiga; Environmental science; Remote sensing; Boreal; Vegetation (pathology); Biomass (ecology); Satellite; Satellite imagery; Physical geography; Forest inventory; Leaf area index; Forest management; Forestry; Geography; Geology; Ecology; Agroforestry","score_opus":0.014241702732414143,"score_gpt":0.20956379050740112,"score_spread":0.19532208777498697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110551686","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9984242,0.000042856296,0.0013376842,0.0000053285726,0.0000016954923,0.0000034645354,0.00007343727,0.00002700102,0.00008439826],"genre_scores_gemma":[0.9977729,0.000016156197,0.0020098765,0.0000021568028,0.000002262419,0.0000027848885,0.000159298,0.000005218326,0.000029381472],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998461,0.000048648268,0.000012830362,0.00004044098,0.000029578849,0.000022308726],"domain_scores_gemma":[0.99849653,0.00094768626,0.00018155317,0.000106307016,0.00016374112,0.00010422779],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010042067,0.00051122723,0.00038356628,0.00085957034,0.0003991079,0.0004091712,0.00044687072,0.00038616554,0.0002809975],"category_scores_gemma":[0.002617166,0.00032229576,0.0003449451,0.0006142096,0.00023662375,0.0007572421,0.0001840692,0.0002102729,0.00005931541],"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":[0.0012130729,0.0002577898,0.8397569,0.000052799493,0.00021426217,0.00013251176,0.00017677463,0.10687801,0.014929264,0.00017592599,0.0002757824,0.03593689],"study_design_scores_gemma":[0.000047485708,0.00011413596,0.6662543,0.000004559421,0.00007207718,0.00014196419,0.00010553294,0.33063006,0.0022438671,0.00027872858,0.00008365933,0.00002368201],"about_ca_topic_score_codex":0.03221813,"about_ca_topic_score_gemma":0.05880728,"teacher_disagreement_score":0.03221813,"about_ca_system_score_codex":0.00042307333,"about_ca_system_score_gemma":0.0003366916,"threshold_uncertainty_score":0.064061224},"labels":[],"label_agreement":null},{"id":"W2111759081","doi":"10.1016/j.rse.2005.12.010","title":"Estimating the probability of mountain pine beetle red-attack damage","year":2006,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":161,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Canadian Forest Service","funders":"Canadian Forest Service; Natural Resources Canada; U.S. Forest Service; “la Caixa” Foundation","keywords":"Mountain pine beetle; Environmental science; Terrain; Remote sensing; Logistic regression; Random forest; Topographic Wetness Index; Computer science; Statistics; Geography; Digital elevation model; Cartography; Mathematics; Forestry; Artificial intelligence","score_opus":0.01080278898968624,"score_gpt":0.21524580597446455,"score_spread":0.20444301698477832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111759081","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9863014,0.00024782016,0.01218856,0.00006245061,0.0000056195895,0.000016000738,0.00026689091,0.00011711544,0.000794134],"genre_scores_gemma":[0.9969663,0.00008432374,0.0023327374,0.0000064267606,0.000008963525,0.0000028719946,0.00026560164,0.000003598381,0.00032926167],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955374,0.0001163834,0.000029795186,0.00013958444,0.0000938597,0.00006664612],"domain_scores_gemma":[0.9926812,0.005650815,0.0009231664,0.00024927454,0.00027400072,0.00022157577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015203274,0.0003929834,0.00037364743,0.001176757,0.00029125146,0.0005779686,0.0004987217,0.00077729044,0.0016237678],"category_scores_gemma":[0.005234942,0.00037464828,0.00037834726,0.0003845638,0.00022133048,0.0007728063,0.0003464849,0.00036380193,0.00028721968],"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":[0.0006394221,0.00013252147,0.80029565,0.00006083544,0.00031518377,0.0003818018,0.00009042507,0.15912245,0.0038794875,0.0007501683,0.0006054198,0.03372662],"study_design_scores_gemma":[0.000023174081,0.0003106293,0.38251612,0.000010927363,0.00012703176,0.0007288162,0.00014075985,0.61216384,0.0026506123,0.0008296955,0.00046950302,0.00002892142],"about_ca_topic_score_codex":0.0064065857,"about_ca_topic_score_gemma":0.009931039,"teacher_disagreement_score":0.0064065857,"about_ca_system_score_codex":0.00033861573,"about_ca_system_score_gemma":0.00023253424,"threshold_uncertainty_score":0.0127385855},"labels":[],"label_agreement":null},{"id":"W2111979722","doi":"10.1016/j.rse.2012.06.007","title":"Characterizing spatial representativeness of flux tower eddy-covariance measurements across the Canadian Carbon Program Network using remote sensing and footprint analysis","year":2012,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":108,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Trent University; University of New Brunswick; Queen's University; McMaster University; University of Lethbridge; University of Manitoba; Université Laval; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Science and Technology of the People's Republic of China; Chinese Academy of Sciences; National Science Foundation","keywords":"Environmental science; Eddy covariance; Variogram; Spatial variability; Land cover; Normalized Difference Vegetation Index; Remote sensing; Enhanced vegetation index; Wetland; Vegetation (pathology); Geostatistics; Spatial ecology; Flux (metallurgy); Physical geography; Leaf area index; Land use; Kriging; Geography; Ecosystem; Ecology","score_opus":0.029705354823429585,"score_gpt":0.2613836191494275,"score_spread":0.2316782643259979,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111979722","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9963521,0.000108708475,0.0014861798,0.00004201526,0.00000457032,0.000013416992,0.0010756763,0.000030745618,0.00088666566],"genre_scores_gemma":[0.99719906,0.00005906698,0.0011023179,0.000017530678,0.0000036048239,0.000009947344,0.0014781477,0.0000074618,0.0001228779],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992618,0.00007925296,0.000044542776,0.000212478,0.00022991555,0.00017202825],"domain_scores_gemma":[0.99748564,0.00076371484,0.00027773064,0.00023353765,0.0011310792,0.0001082372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013870504,0.00027773052,0.00034894457,0.0015091856,0.0008461765,0.00093586673,0.0007415886,0.0005309193,0.00028708717],"category_scores_gemma":[0.00544657,0.00025963358,0.00034208057,0.0023355125,0.00045314681,0.00059922563,0.0005184186,0.00026254554,0.00007929078],"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":[0.0001479223,0.00008401787,0.9412582,0.000050126655,0.00023541943,0.00009957796,0.00053737935,0.018077748,0.007358574,0.000495356,0.0010316881,0.030623995],"study_design_scores_gemma":[0.000009101153,0.000009216641,0.9670264,0.000012981072,0.00004659637,0.00005538746,0.00029433865,0.03088625,0.0008781452,0.0001073066,0.0006571866,0.000017114487],"about_ca_topic_score_codex":0.8413297,"about_ca_topic_score_gemma":0.8914851,"teacher_disagreement_score":0.1586703,"about_ca_system_score_codex":0.002703964,"about_ca_system_score_gemma":0.0032790736,"threshold_uncertainty_score":0.31920946},"labels":[],"label_agreement":null},{"id":"W2114080556","doi":"10.1016/j.rse.2006.02.008","title":"Automated assessment of hardwood and shrub competition in regenerating forests using leaf-off airborne imagery","year":2006,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Forest Service; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Pittsburgh","keywords":"Remote sensing; Shrub; Hardwood; Environmental science; Competition (biology); Forestry; Geography; Ecology","score_opus":0.010572001660873883,"score_gpt":0.24672497640376287,"score_spread":0.236152974742889,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114080556","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9952508,0.000056456644,0.0037448183,0.000010688866,0.0000027037954,0.00001499463,0.00022821155,0.00014463889,0.0005467519],"genre_scores_gemma":[0.9877559,0.000033181255,0.011049867,0.000010726237,0.000004770772,0.000014521568,0.00057571504,0.000013621938,0.0005416669],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998659,0.000021931355,0.000005442304,0.000042242726,0.00003900697,0.000025497558],"domain_scores_gemma":[0.9994703,0.0002132343,0.0000632723,0.000037459475,0.00014212394,0.000073590534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033099166,0.0002294098,0.000386537,0.0009900413,0.0002508357,0.0003826237,0.0004399878,0.0002939409,0.0007900135],"category_scores_gemma":[0.00046523547,0.00014964992,0.00015477902,0.0003803172,0.00013132124,0.00041496934,0.00023668716,0.00013495235,0.00019352607],"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":[0.001490015,0.0006675701,0.47204468,0.00017978865,0.00018407556,0.00017510055,0.00035069257,0.030815259,0.18412785,0.0003656087,0.001661517,0.30793786],"study_design_scores_gemma":[0.000043600267,0.000156374,0.7153155,0.000006271236,0.000060234557,0.00015914948,0.00018650448,0.27310592,0.0100977,0.00031875205,0.00052419055,0.000025815503],"about_ca_topic_score_codex":0.011640809,"about_ca_topic_score_gemma":0.03751462,"teacher_disagreement_score":0.011640809,"about_ca_system_score_codex":0.00039576265,"about_ca_system_score_gemma":0.0003406532,"threshold_uncertainty_score":0.023146093},"labels":[],"label_agreement":null},{"id":"W2118439236","doi":"10.1016/j.rse.2009.07.012","title":"An assessment of photosynthetic light use efficiency from space: Modeling the atmospheric and directional impacts on PRI reflectance","year":2009,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":87,"is_retracted":false,"has_abstract":false,"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; National Aeronautics and Space Administration","keywords":"Remote sensing; Photochemical Reflectance Index; Spectroradiometer; Environmental science; Atmospheric correction; Bidirectional reflectance distribution function; Satellite; Eddy covariance; Pixel; Reflectivity; Leaf area index; Sampling (signal processing); Meteorology; Computer science; Normalized Difference Vegetation Index; Geography; Optics; Physics; Artificial intelligence","score_opus":0.009585814039192926,"score_gpt":0.25066088485492893,"score_spread":0.241075070815736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118439236","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9795544,0.00016141158,0.0165391,0.00018024269,0.000014372222,0.00001915884,0.00014641069,0.00009131007,0.0032935843],"genre_scores_gemma":[0.99597615,0.00008463086,0.0031261267,0.000017644617,0.000005150011,0.000014087171,0.000051857845,0.000019021192,0.0007054218],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999113,0.000029958052,0.000004306423,0.0000223095,0.000016983022,0.0000152562125],"domain_scores_gemma":[0.99961853,0.0002641678,0.000029921986,0.000023828989,0.000039964543,0.000023604573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006000481,0.00059196755,0.0003784057,0.00021143572,0.00036425758,0.0007237555,0.00067802955,0.0011402387,0.00081610715],"category_scores_gemma":[0.0010689624,0.00041943556,0.00079105457,0.0003651353,0.00041466622,0.00077476434,0.0003392897,0.0005672803,0.00011019208],"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":[0.00003237177,0.000038221264,0.0020354039,0.000010426158,0.000018218501,0.0000135552355,0.000014857699,0.994589,0.0013170074,0.00034825245,0.000046307312,0.0015363054],"study_design_scores_gemma":[0.000008330164,0.000016875123,0.000846597,9.123092e-7,0.000010215707,0.0000035840199,0.000006904684,0.99858177,0.00037076286,0.00010475978,0.000045278764,0.0000039089523],"about_ca_topic_score_codex":0.027351812,"about_ca_topic_score_gemma":0.01827194,"teacher_disagreement_score":0.027351812,"about_ca_system_score_codex":0.00075225957,"about_ca_system_score_gemma":0.00073564996,"threshold_uncertainty_score":0.054385185},"labels":[],"label_agreement":null},{"id":"W2120607258","doi":"10.1016/j.rse.2011.06.022","title":"Experimental characterization and modelling of the nighttime directional anisotropy of thermal infrared measurements over an urban area: Case study of Toulouse (France)","year":2011,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":69,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Institut national des sciences de l'Univers; Centre National de la Recherche Scientifique","keywords":"Anisotropy; Azimuth; Daytime; Sunset; Remote sensing; Environmental science; Radiative transfer; Thermal; Atmospheric sciences; Infrared; Characterization (materials science); Meteorology; Computational physics; Geology; Optics; Physics","score_opus":0.04609195252683962,"score_gpt":0.2130275703459229,"score_spread":0.16693561781908328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120607258","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99690515,0.000033637632,0.0020245784,0.000015759553,0.000004215165,0.000008467673,0.00018272226,0.0000637701,0.0007616147],"genre_scores_gemma":[0.9986712,0.00002210974,0.0010020171,0.0000030190686,0.0000016469712,0.0000073339065,0.0001159239,0.0000076113647,0.0001691483],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977714,0.000055138622,0.00000779896,0.00006109142,0.000045415785,0.000053486958],"domain_scores_gemma":[0.99940836,0.00027377973,0.0000581486,0.00011727289,0.000110241985,0.000032236298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005218044,0.00044085327,0.00030880407,0.00033375062,0.0004102302,0.00056634296,0.0005765395,0.00075948273,0.000489616],"category_scores_gemma":[0.0005607225,0.00023169615,0.0004589225,0.0005415053,0.000465526,0.00031414372,0.00018705738,0.0002942757,0.00013318638],"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":[0.0018298755,0.0017915363,0.13998213,0.00030155396,0.00020452817,0.0013090696,0.001548701,0.49346563,0.31638628,0.00096666045,0.0010552051,0.041158717],"study_design_scores_gemma":[0.00017248567,0.000761367,0.42149696,0.000014931625,0.00013463426,0.00025244843,0.0005867134,0.4844857,0.090373866,0.00020682653,0.0014089711,0.00010510497],"about_ca_topic_score_codex":0.04373281,"about_ca_topic_score_gemma":0.041617304,"teacher_disagreement_score":0.04373281,"about_ca_system_score_codex":0.0006722705,"about_ca_system_score_gemma":0.0004981143,"threshold_uncertainty_score":0.08695656},"labels":[],"label_agreement":null},{"id":"W2122119815","doi":"10.1016/j.rse.2010.11.002","title":"Continuity of Landsat observations: Short term considerations","year":2010,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Planetary Science and Exploration","field":"Physics and Astronomy","cited_by":123,"is_retracted":false,"has_abstract":false,"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":"U.S. Geological Survey; U.S. Department of Agriculture; National Aeronautics and Space Administration","keywords":"Remote sensing; Compositing; Term (time); Satellite imagery; Computer science; Thematic Mapper; Environmental science; Geography; Image (mathematics)","score_opus":0.025529743972658572,"score_gpt":0.22446425310743368,"score_spread":0.19893450913477512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122119815","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45156935,0.014315214,0.42008045,0.044046134,0.002405117,0.00034146215,0.008110244,0.00049736944,0.058634605],"genre_scores_gemma":[0.9658618,0.001869138,0.022750769,0.00123763,0.0010607626,0.00009454294,0.0015441359,0.00017604927,0.0054050703],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99616516,0.0014117646,0.00042261474,0.0008493978,0.00081129523,0.00033979362],"domain_scores_gemma":[0.90161616,0.070778474,0.0079545025,0.008157492,0.010129207,0.0013641984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01563313,0.00030042324,0.00094344077,0.0011819855,0.0014296337,0.004029721,0.0032375662,0.0028959394,0.003842416],"category_scores_gemma":[0.094649784,0.00053566566,0.0007311943,0.002646533,0.0016238411,0.008349132,0.0018661594,0.00333227,0.00035136173],"study_design_candidate":"not_applicable","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":[0.0022032438,0.00044915787,0.48093933,0.0011607654,0.001053113,0.00452409,0.0019221265,0.10784674,0.0074087065,0.21333967,0.013588835,0.16556433],"study_design_scores_gemma":[0.00013318409,0.0010514073,0.527934,0.0006296667,0.00072256115,0.0044596414,0.003550003,0.24899925,0.008824505,0.1593177,0.044133592,0.00024447436],"about_ca_topic_score_codex":0.014116148,"about_ca_topic_score_gemma":0.012322651,"teacher_disagreement_score":0.01563313,"about_ca_system_score_codex":0.001788328,"about_ca_system_score_gemma":0.0014330237,"threshold_uncertainty_score":0.08267689},"labels":[],"label_agreement":null},{"id":"W2124004030","doi":"10.1016/j.rse.2004.10.008","title":"Mapping lichen in a caribou habitat of Northern Quebec, Canada, using an enhancement_classification method and spectral mixture analysis","year":2004,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Rangeland and Wildlife Management","field":"Environmental Science","cited_by":52,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Lethbridge; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; Université du Québec à Rimouski; Université Laval","keywords":"Lichen; Remote sensing; Wildlife; Geography; Abundance (ecology); Ecology; Environmental science; Habitat; Population; Vegetation (pathology); Physical geography; Cartography; Biology","score_opus":0.01207785369522812,"score_gpt":0.22339543157581368,"score_spread":0.21131757788058556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124004030","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99795735,0.00015883548,0.0005310424,0.000032462267,0.0000024833525,0.000023828761,0.00046498343,0.000023741499,0.00080533285],"genre_scores_gemma":[0.9959772,0.00010406809,0.0016120655,0.00001574191,0.0000015045373,0.000017818029,0.00045873373,0.000006423741,0.0018064672],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999824,0.000018908933,0.0000059071144,0.000049428167,0.00005443802,0.000047219728],"domain_scores_gemma":[0.99963284,0.000037697955,0.000030298961,0.0000112514235,0.00023555203,0.00005239556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026245217,0.0003048434,0.00024088044,0.0015004606,0.0014992466,0.00086966314,0.00055166986,0.00029892987,0.00076685235],"category_scores_gemma":[0.00049831235,0.00014459204,0.00015776388,0.0016274103,0.00047243026,0.00022113536,0.00032092916,0.00020561219,0.00015102363],"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":[0.0004102212,0.00016376907,0.91079205,0.000103290426,0.00012781465,0.00041323577,0.0026013623,0.002716081,0.02322854,0.00024467864,0.0016563815,0.0575425],"study_design_scores_gemma":[0.000010305108,0.000018117895,0.9923781,0.000012767192,0.000026266522,0.0000726698,0.001045831,0.004961154,0.00059418986,0.000020544601,0.000848687,0.000011413189],"about_ca_topic_score_codex":0.9884355,"about_ca_topic_score_gemma":0.99635714,"teacher_disagreement_score":0.011564493,"about_ca_system_score_codex":0.005429621,"about_ca_system_score_gemma":0.00456666,"threshold_uncertainty_score":0.039394855},"labels":[],"label_agreement":null},{"id":"W2125721005","doi":"10.1016/j.rse.2008.01.016","title":"Continuous wavelets for the improved use of spectral libraries and hyperspectral data","year":2008,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":174,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Networks of Centres of Excellence of Canada","keywords":"Endmember; Hyperspectral imaging; Wavelet; Context (archaeology); Spectral signature; Computer science; Remote sensing; Spectral line; Sample (material); Pattern recognition (psychology); Artificial intelligence; Geology; Chemistry; Physics","score_opus":0.05406645263418813,"score_gpt":0.21271239953210838,"score_spread":0.15864594689792025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125721005","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017766269,0.00051047467,0.97979283,0.0002010312,0.00009319745,0.00001570938,0.00015637702,0.0006940536,0.0007700928],"genre_scores_gemma":[0.15715158,0.0010401902,0.83798057,0.0001006175,0.00017182897,0.0000672763,0.0005935683,0.00037165053,0.0025226735],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993185,0.00024149982,0.00004096429,0.00009271634,0.00025957054,0.000046775996],"domain_scores_gemma":[0.9979393,0.0008514565,0.00013540593,0.00052450487,0.00047484314,0.0000745935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001529457,0.00070633757,0.0006351495,0.0011300268,0.00028340067,0.0012242928,0.0008084005,0.0007300465,0.0027990097],"category_scores_gemma":[0.0056053563,0.00031033435,0.0005850912,0.002373238,0.00049374875,0.0013537356,0.0010426794,0.0015801919,0.0014518733],"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":[0.00059929845,0.0002239026,0.0011038618,0.00023516497,0.00009948494,0.000134684,0.00013880635,0.04917353,0.08518642,0.024051849,0.0064540585,0.8325989],"study_design_scores_gemma":[0.000047543992,0.00009552212,0.001210991,0.000032558397,0.00006204407,0.00013681162,0.000031217565,0.94385505,0.036811087,0.010559066,0.007116225,0.000041818967],"about_ca_topic_score_codex":0.0010383273,"about_ca_topic_score_gemma":0.001326839,"teacher_disagreement_score":0.0027990097,"about_ca_system_score_codex":0.00024115507,"about_ca_system_score_gemma":0.00047229946,"threshold_uncertainty_score":0.009363592},"labels":[],"label_agreement":null},{"id":"W2125781982","doi":"10.1016/j.rse.2006.09.022","title":"Modelling and mapping potential hooded warbler (Wilsonia citrina) habitat using remotely sensed imagery","year":2006,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Bird Studies Canada; Ministry of Natural Resources; Natural Sciences and Engineering Research Council of Canada; National Research Centre","keywords":"Nest (protein structural motif); Remote sensing; Habitat; Environmental science; Vegetation (pathology); Geography; Warbler; Cartography; Ecology; Biology","score_opus":0.012681814742957619,"score_gpt":0.1898647756994511,"score_spread":0.17718296095649347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125781982","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99537575,0.000046498495,0.0038851448,0.000031457326,0.0000055402547,0.000013763017,0.00009982598,0.000065443004,0.00047652182],"genre_scores_gemma":[0.9962901,0.00003559971,0.0032118151,0.0000039593688,0.0000019010455,0.000009282259,0.000099075565,0.000007749415,0.00034056805],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999918,0.000016441007,0.000004187488,0.000029097713,0.000009854002,0.000022387741],"domain_scores_gemma":[0.9996283,0.00023779177,0.000038696355,0.000019977275,0.000037950034,0.00003729738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026864055,0.0006347761,0.00037632618,0.00049506547,0.00033425406,0.00066156854,0.0007297797,0.0006758235,0.00085423124],"category_scores_gemma":[0.00076910714,0.00065615267,0.0004859646,0.00035117252,0.00030911865,0.00090197596,0.0002508142,0.00032070008,0.000104798106],"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":[0.000097748045,0.000092976894,0.01492965,0.000027268945,0.000041374326,0.00009760161,0.00005518274,0.97691965,0.002496835,0.00018115444,0.00013395825,0.0049266513],"study_design_scores_gemma":[0.000010592213,0.00003151106,0.0037861702,0.0000023456269,0.000012800626,0.000017617805,0.00004457975,0.99551976,0.0004265419,0.00008922928,0.000052802763,0.000005966928],"about_ca_topic_score_codex":0.07483241,"about_ca_topic_score_gemma":0.07468015,"teacher_disagreement_score":0.07483241,"about_ca_system_score_codex":0.0007807174,"about_ca_system_score_gemma":0.0005653848,"threshold_uncertainty_score":0.1487937},"labels":[],"label_agreement":null},{"id":"W2126250722","doi":"10.1016/j.rse.2008.09.003","title":"Noise reduction of NDVI time series: An empirical comparison of selected techniques","year":2008,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":561,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Normalized Difference Vegetation Index; Remote sensing; Noise reduction; Noise (video); Time series; Vegetation (pathology); Environmental science; Empirical orthogonal functions; Gaussian noise; Meteorology; Series (stratigraphy); Computer science; Phenology; Mathematics; Algorithm; Statistics; Climate change; Geography; Artificial intelligence; Geology; Ecology","score_opus":0.01565058111343126,"score_gpt":0.2526108635584379,"score_spread":0.23696028244500666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126250722","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8430725,0.0035297803,0.14812239,0.0001286989,0.00006361004,0.00012889456,0.00072177197,0.00046553483,0.0037668173],"genre_scores_gemma":[0.9105681,0.0019846559,0.08428466,0.00003921843,0.00005528117,0.000086234686,0.001782832,0.0001630726,0.0010359226],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986767,0.00040618103,0.00009338307,0.0002453278,0.00048829487,0.00009003209],"domain_scores_gemma":[0.9926347,0.005260111,0.000381767,0.00048340272,0.0011692585,0.000070842085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034256447,0.0007844578,0.00085628737,0.0025254546,0.00039967935,0.00080621184,0.0008105167,0.0008572719,0.00065154943],"category_scores_gemma":[0.010614195,0.00020683641,0.0010675519,0.0023484363,0.00032033765,0.0012518103,0.0005025975,0.00039724112,0.00024979014],"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":[0.004097818,0.000828452,0.07000244,0.0015484696,0.00092365174,0.0001481547,0.0006236564,0.0883769,0.04519769,0.0016183745,0.0011820446,0.7854523],"study_design_scores_gemma":[0.00020694995,0.0015961836,0.4027084,0.00023486579,0.0014413134,0.00065660354,0.0005868222,0.53877914,0.0457054,0.0024134957,0.0055270055,0.00014389462],"about_ca_topic_score_codex":0.0039478587,"about_ca_topic_score_gemma":0.0047190073,"teacher_disagreement_score":0.0039478587,"about_ca_system_score_codex":0.00041957072,"about_ca_system_score_gemma":0.0004019419,"threshold_uncertainty_score":0.018116772},"labels":[],"label_agreement":null},{"id":"W2128827136","doi":"10.1016/j.rse.2009.02.015","title":"Large area monitoring with a MODIS-based Disturbance Index (DI) sensitive to annual and seasonal variations","year":2009,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":78,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; University of British Columbia","funders":"Canadian Space Agency; British Columbia Ministry of Agriculture and Lands","keywords":"Environmental science; Disturbance (geology); Vegetation (pathology); Moderate-resolution imaging spectroradiometer; Enhanced vegetation index; Windthrow; Biosphere; Normalized Difference Vegetation Index; Agricultural land; Remote sensing; Satellite; Leaf area index; Physical geography; Hydrology (agriculture); Land use; Ecology; Geography; Vegetation Index; Forestry","score_opus":0.0056652157000021235,"score_gpt":0.2005238000243021,"score_spread":0.19485858432429998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128827136","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9553219,0.00025406873,0.029720584,0.00009296369,0.00007855189,0.0001939641,0.006185664,0.0011626537,0.0069895866],"genre_scores_gemma":[0.9736467,0.00013063841,0.020255309,0.000059318576,0.000035159643,0.00013057105,0.0039895508,0.000057707774,0.001695091],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99970967,0.000035160672,0.000026195336,0.0001000234,0.000101134894,0.000027726346],"domain_scores_gemma":[0.99933857,0.000092514165,0.00022521384,0.00009343809,0.00018188485,0.00006839598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051359605,0.00037606942,0.00041923765,0.0011097245,0.00034069427,0.00045603348,0.00033043642,0.00023466884,0.0007722821],"category_scores_gemma":[0.00073086715,0.00018336921,0.00018049535,0.0018075343,0.00016007104,0.00046665405,0.0003113051,0.00019301247,0.0002819569],"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":[0.0006102141,0.00050931156,0.7516629,0.00020617915,0.00025413817,0.00016741955,0.00026368172,0.019910492,0.087709285,0.00029904483,0.0049150246,0.13349219],"study_design_scores_gemma":[0.000052474017,0.00017880402,0.9215981,0.000011857291,0.00009995906,0.00018839615,0.000120931676,0.063895434,0.010081341,0.00020278645,0.0035409057,0.000028982911],"about_ca_topic_score_codex":0.009278466,"about_ca_topic_score_gemma":0.023953928,"teacher_disagreement_score":0.009278466,"about_ca_system_score_codex":0.00044057393,"about_ca_system_score_gemma":0.00034165624,"threshold_uncertainty_score":0.018448949},"labels":[],"label_agreement":null},{"id":"W2131982632","doi":"10.1016/j.rse.2003.08.017","title":"Remote sensing in BOREAS: Lessons learned","year":2003,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":103,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto; University of Lethbridge","funders":"Agriculture and Agri-Food Canada; Natural Resources Canada; Canadian Space Agency; Natural Sciences and Engineering Research Council of Canada; U.S. Forest Service; U.S. Geological Survey; National Aeronautics and Space Administration; Canadian Forest Service; Parks Canada; U.S. Environmental Protection Agency; National Science Foundation","keywords":"Biome; Taiga; Biosphere; Boreal; Remote sensing; Environmental science; Vegetation (pathology); Land cover; Climate change; Environmental resource management; Geography; Land use; Ecosystem; Ecology; Forestry","score_opus":0.021705623193472568,"score_gpt":0.24613988559231115,"score_spread":0.22443426239883857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131982632","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03832267,0.45954028,0.02028768,0.43364593,0.0067089195,0.000068221125,0.0003867951,0.00029074415,0.040748842],"genre_scores_gemma":[0.42340055,0.42706117,0.061224107,0.065350644,0.008406241,0.00008028454,0.00038223068,0.00024018444,0.013854593],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99874663,0.00038514452,0.0000716951,0.00032490154,0.00025818372,0.00021342971],"domain_scores_gemma":[0.9914145,0.004061774,0.00030760435,0.00075934635,0.0024509174,0.001005746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011929464,0.001100178,0.001066575,0.0009319955,0.0012919736,0.0034682967,0.0022685132,0.0044028,0.003607017],"category_scores_gemma":[0.010821379,0.00035817208,0.00076544075,0.0010851144,0.0071947384,0.0074253283,0.0026068774,0.0056865215,0.00065475213],"study_design_candidate":"not_applicable","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":[0.00047087314,0.000572104,0.015462927,0.0024934355,0.00029970417,0.0008167941,0.0034199806,0.009895107,0.0040908274,0.12247161,0.075404055,0.76460266],"study_design_scores_gemma":[0.0001656288,0.0004198675,0.024617152,0.003814066,0.00015687807,0.0007423428,0.008897178,0.003875398,0.0036444438,0.19929144,0.7541291,0.00024651262],"about_ca_topic_score_codex":0.08178882,"about_ca_topic_score_gemma":0.093657374,"teacher_disagreement_score":0.08178882,"about_ca_system_score_codex":0.0043709585,"about_ca_system_score_gemma":0.0075000776,"threshold_uncertainty_score":0.16262555},"labels":[],"label_agreement":null},{"id":"W2136258041","doi":"10.1016/j.rse.2009.01.011","title":"The effects of ecologically determined spatial complexity on the classification accuracy of simulated coral reef images","year":2009,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Coral and Marine Ecosystems Studies","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Environment Research Council; Sight Research UK; Royal Society; World Bank Group","keywords":"Remote sensing; Image resolution; Coral reef; Reef; Atoll; Pixel; Spatial ecology; Environmental science; Scale (ratio); Coral; Computer science; Cartography; Geography; Artificial intelligence; Geology; Ecology; Oceanography","score_opus":0.023573989995770338,"score_gpt":0.2375260377732434,"score_spread":0.21395204777747306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136258041","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99752873,0.000057422498,0.0017558855,0.00008661143,0.000010945697,0.0000075437238,0.00014399442,0.000033886998,0.00037493304],"genre_scores_gemma":[0.998828,0.00002207099,0.00076243776,0.000019279725,0.0000043504565,0.0000035977605,0.00021205767,0.000018689723,0.00012960576],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827456,0.00065303594,0.00017652808,0.0003236859,0.0003494936,0.00022270974],"domain_scores_gemma":[0.9475836,0.046166614,0.0017029912,0.0014725206,0.0025833498,0.0004910181],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003370855,0.0003869861,0.00032653412,0.0006169095,0.00043165335,0.0009965929,0.00038551615,0.0008662877,0.0005417523],"category_scores_gemma":[0.034612563,0.00033062015,0.00053061725,0.00046995367,0.00073819887,0.0009085165,0.0006365254,0.00060631026,0.0001396707],"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":[0.002845842,0.0003270305,0.26150498,0.00009422335,0.00035861155,0.00026813798,0.00024290699,0.6826668,0.020339308,0.00044437838,0.00067500776,0.03023287],"study_design_scores_gemma":[0.00007262701,0.00037769074,0.24410337,0.000021580623,0.00017922756,0.00029243433,0.00015906114,0.73980546,0.0142711345,0.00043231982,0.00022646548,0.000058618818],"about_ca_topic_score_codex":0.011903031,"about_ca_topic_score_gemma":0.01135044,"teacher_disagreement_score":0.011903031,"about_ca_system_score_codex":0.00095346814,"about_ca_system_score_gemma":0.0004951226,"threshold_uncertainty_score":0.023667455},"labels":[],"label_agreement":null},{"id":"W2136636747","doi":"10.1016/j.rse.2009.01.003","title":"Characterizing forest succession with lidar data: An evaluation for the Inland Northwest, USA","year":2009,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":335,"is_retracted":false,"has_abstract":false,"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":"Rocky Mountain Research Station; U.S. Geological Survey; International Institute of Tropical Forestry; U.S. Forest Service; Sustainable Forestry Initiative","keywords":"Lidar; Ecological succession; Basal area; Remote sensing; Forest inventory; Vegetation (pathology); Canopy; Forest ecology; Environmental science; Forest management; Random forest; Forest structure; Wildlife; Geography; Ecosystem; Ecology; Agroforestry; Forestry; Computer science","score_opus":0.03385452860308631,"score_gpt":0.27916816376361525,"score_spread":0.24531363516052895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136636747","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9965496,0.000075014024,0.0020326215,0.000029080671,0.0000043036416,0.00006800064,0.00061632076,0.00008858871,0.0005364346],"genre_scores_gemma":[0.98034555,0.00017378121,0.017021144,0.000036112764,0.0000066898547,0.000065285276,0.0018744626,0.000024522498,0.00045246704],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99928564,0.00023802399,0.00005087988,0.00011412884,0.00025893425,0.00005242694],"domain_scores_gemma":[0.9965675,0.001469035,0.00023658192,0.00024188298,0.001274158,0.00021093104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032314982,0.00058737025,0.00051386026,0.00094047555,0.00064349885,0.0008436916,0.0006957047,0.00056161225,0.00054807815],"category_scores_gemma":[0.004491678,0.00024971156,0.00047364412,0.0012695452,0.0002536311,0.00092302094,0.00045834325,0.00023556486,0.00015980417],"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":[0.002545462,0.0016650364,0.8241213,0.00017372594,0.00036907225,0.0002675268,0.00026218654,0.039646965,0.014037075,0.00016273496,0.0011683013,0.11558055],"study_design_scores_gemma":[0.00028751337,0.0013027202,0.627034,0.000036584086,0.00036905266,0.00029694883,0.00087246613,0.35428983,0.013697044,0.00019014694,0.0015829547,0.000040678475],"about_ca_topic_score_codex":0.07773931,"about_ca_topic_score_gemma":0.18619767,"teacher_disagreement_score":0.07773931,"about_ca_system_score_codex":0.0009952177,"about_ca_system_score_gemma":0.0010047959,"threshold_uncertainty_score":0.15457362},"labels":[],"label_agreement":null},{"id":"W2136660332","doi":"10.1016/s0034-4257(02)00182-7","title":"Spatial analysis of radiometric fractions from high-resolution multispectral imagery for modelling individual tree crown and forest canopy structure and health","year":2003,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":115,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Defence Research and Development Canada","funders":"Canadian Forest Service; Natural Sciences and Engineering Research Council of Canada; Ministry of Natural Resources; University of Pittsburgh","keywords":"Multispectral image; Remote sensing; Canopy; Tree canopy; Variogram; Environmental science; Microsite; Forest inventory; Image resolution; Mathematics; Semivariance; Scale (ratio); Forest management; Spatial variability; Statistics; Geography; Kriging; Computer science; Cartography; Artificial intelligence; Agroforestry","score_opus":0.017293036320994073,"score_gpt":0.22869368162109216,"score_spread":0.21140064530009808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136660332","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.907045,0.00026541366,0.09069928,0.000053225933,0.000008120293,0.000023146555,0.00046684008,0.00028130293,0.0011576],"genre_scores_gemma":[0.9871033,0.000056846966,0.012383297,0.0000044266026,0.0000039157208,0.0000109244165,0.00019191837,0.000020145073,0.00022525391],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99993694,0.000015149602,0.000004421923,0.000016661606,0.000016441365,0.000010417742],"domain_scores_gemma":[0.9997186,0.00014189823,0.00004063464,0.000029755885,0.000056395118,0.00001273438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027765986,0.00034120414,0.00021756536,0.000954017,0.00022210293,0.00037396562,0.0003299004,0.00028369424,0.00056618644],"category_scores_gemma":[0.0007678139,0.00021904446,0.0004623578,0.0006520368,0.00019113712,0.00036684197,0.00018539131,0.00015283872,0.00016102046],"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":[0.00038260574,0.00018573416,0.06742305,0.000121777,0.00016838209,0.00018653153,0.00022839285,0.76881486,0.03887789,0.0010920856,0.00045059275,0.12206812],"study_design_scores_gemma":[0.000006825595,0.000021402617,0.058429804,0.0000048558973,0.00003993029,0.00008421274,0.00005435975,0.9372973,0.0033360159,0.00049795734,0.00021553444,0.00001184359],"about_ca_topic_score_codex":0.013143724,"about_ca_topic_score_gemma":0.012675745,"teacher_disagreement_score":0.013143724,"about_ca_system_score_codex":0.00030633507,"about_ca_system_score_gemma":0.0002530584,"threshold_uncertainty_score":0.026134431},"labels":[],"label_agreement":null},{"id":"W2139056631","doi":"10.1016/j.rse.2012.02.019","title":"Intercomparison of MODIS albedo retrievals and in situ measurements across the global FLUXNET network","year":2012,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":311,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Water Center, United Arab Emirates University; Canadian Foundation for Climate and Atmospheric Sciences; Université Laval; National Aeronautics and Space Administration; U.S. Department of Energy","keywords":"Environmental science; Albedo (alchemy); FluxNet; Remote sensing; Land cover; Satellite; Atmospheric sciences; Land use; Eddy covariance; Ecosystem; Geology","score_opus":0.0205141160021099,"score_gpt":0.24889120016803748,"score_spread":0.22837708416592759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139056631","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.982991,0.00033452344,0.0033514693,0.00028420083,0.0002744707,0.000037505546,0.009350634,0.00049155677,0.0028846196],"genre_scores_gemma":[0.9705156,0.00019457542,0.0079172,0.00010598553,0.00006741528,0.000051189414,0.019626418,0.00016626895,0.0013554159],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99945503,0.00012284901,0.000046854402,0.0002342048,0.00008395011,0.000057082118],"domain_scores_gemma":[0.9987447,0.0003006743,0.00019901732,0.00023529743,0.00044880263,0.00007135507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019419472,0.00068273867,0.00046673973,0.0010971794,0.0004876944,0.0010710286,0.0006334547,0.0007448196,0.0010550744],"category_scores_gemma":[0.00218722,0.00037661023,0.00046661473,0.0015093869,0.00024528388,0.0013917881,0.0005967737,0.00036313423,0.00038533245],"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":[0.004045943,0.0015897151,0.52354926,0.00077333464,0.0024748137,0.0006048835,0.0018066373,0.1401137,0.13379572,0.0027530752,0.035342738,0.15315008],"study_design_scores_gemma":[0.00043938504,0.00019543612,0.8570426,0.00008436828,0.0007262178,0.00013146538,0.00063010864,0.098583385,0.023345865,0.0010697023,0.017636511,0.00011493842],"about_ca_topic_score_codex":0.036255542,"about_ca_topic_score_gemma":0.05282014,"teacher_disagreement_score":0.036255542,"about_ca_system_score_codex":0.0009868836,"about_ca_system_score_gemma":0.0009069437,"threshold_uncertainty_score":0.07208902},"labels":[],"label_agreement":null},{"id":"W2139709933","doi":"10.1016/j.rse.2014.02.001","title":"Landsat-8: Science and product vision for terrestrial global change research","year":2014,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Calibration and Measurement Techniques","field":"Engineering","cited_by":2557,"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 Forest Service; Natural Resources Canada","funders":"U.S. Geological Survey","keywords":"Remote sensing; Environmental science; Earth observation; Land cover; Spectral bands; Atmospheric correction; Global change; Cirrus; Radiometry; Climate change; Satellite; Geography; Land use; Geology","score_opus":0.07596300017510862,"score_gpt":0.31580442519175983,"score_spread":0.2398414250166512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139709933","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029849255,0.012744973,0.40656438,0.006247601,0.0020027454,0.0022152634,0.34736952,0.03774896,0.15525733],"genre_scores_gemma":[0.0981696,0.006687944,0.37780887,0.002223535,0.0008668658,0.001837626,0.459063,0.0040976964,0.049244836],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99921787,0.00015616245,0.000042987784,0.00010794253,0.00040922605,0.00006578394],"domain_scores_gemma":[0.9991823,0.00005414408,0.00009018864,0.0001072333,0.00051681127,0.000049327995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011756226,0.00083735725,0.0004970834,0.001915445,0.00048347536,0.0013151265,0.0008222253,0.0006958048,0.008105334],"category_scores_gemma":[0.0012592304,0.00020402487,0.00033098157,0.003568686,0.0003824049,0.0013187382,0.0009432494,0.0010656761,0.0100366175],"study_design_candidate":"theoretical_or_conceptual","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":[0.0001713594,0.00010705077,0.0044256723,0.00046357888,0.000068475056,0.000114009854,0.00012242208,0.0043239887,0.011389608,0.0126729,0.6364854,0.3296556],"study_design_scores_gemma":[0.00006682391,0.000077877936,0.012765394,0.00015174835,0.00003204601,0.00017255268,0.00013592376,0.011194891,0.0041135103,0.0072556348,0.9639728,0.000060682003],"about_ca_topic_score_codex":0.0089440225,"about_ca_topic_score_gemma":0.006165692,"teacher_disagreement_score":0.0089440225,"about_ca_system_score_codex":0.00092855026,"about_ca_system_score_gemma":0.0015212976,"threshold_uncertainty_score":0.027115047},"labels":[],"label_agreement":null},{"id":"W2140135187","doi":"10.1016/j.rse.2011.01.015","title":"Spectral variations in the near-infrared ocean reflectance","year":2011,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":73,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"SeaWiFS; Remote sensing; Environmental science; Ocean color; Turbidity; Reflectivity; Atmospheric correction; Near-infrared spectroscopy; Seawater; Absorption (acoustics); In situ; Geology; Oceanography; Satellite; Phytoplankton; Chemistry; Optics; Meteorology; Physics; Nutrient","score_opus":0.019760610063104957,"score_gpt":0.1883782641873409,"score_spread":0.16861765412423596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140135187","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9952194,0.000093342074,0.0014763088,0.00005040482,0.000012869314,0.000003425341,0.00030516824,0.00007487547,0.0027641815],"genre_scores_gemma":[0.9983119,0.00005856376,0.00050695677,0.000022157543,0.00000625998,0.0000027772642,0.00032717775,0.00002235643,0.0007418901],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9999429,0.000008078441,0.000002106863,0.00001965012,0.000014072936,0.0000132009245],"domain_scores_gemma":[0.9998671,0.00004155618,0.000018297429,0.000015110769,0.000041484192,0.000016349422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000102367376,0.00014521713,0.00011875807,0.00035832013,0.00016143892,0.00018241009,0.00014091305,0.00022997394,0.0013818725],"category_scores_gemma":[0.0003337344,0.000103090715,0.00019309235,0.0003733595,0.00017362078,0.00032097477,0.00013181668,0.0002647364,0.00037153257],"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":[0.0014130032,0.00035423995,0.17219006,0.00014346479,0.00017957485,0.00045399327,0.00048096658,0.01336068,0.73763204,0.0012470387,0.0026684878,0.06987653],"study_design_scores_gemma":[0.000025306643,0.00008724212,0.94971687,0.000007210508,0.000058694608,0.00036107303,0.00015903988,0.023352237,0.024791464,0.00032639617,0.0010872436,0.000027183381],"about_ca_topic_score_codex":0.0020984306,"about_ca_topic_score_gemma":0.0033757254,"teacher_disagreement_score":0.0020984306,"about_ca_system_score_codex":0.00012302425,"about_ca_system_score_gemma":0.00007765724,"threshold_uncertainty_score":0.004622817},"labels":[],"label_agreement":null},{"id":"W2140161421","doi":"10.1016/j.rse.2009.10.011","title":"Salt-marsh characterization, zonation assessment and mapping through a dual-wavelength LiDAR","year":2009,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":72,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski; Institut National de la Recherche Scientifique","funders":"Fisheries and Oceans Canada","keywords":"Lidar; Intertidal zone; Salt marsh; Remote sensing; Digital elevation model; Shoal; Bathymetry; Vegetation (pathology); Environmental science; Marsh; Wetland; Geology; Oceanography; Ecology","score_opus":0.01381214960368691,"score_gpt":0.24290277081847594,"score_spread":0.22909062121478904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140161421","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9761604,0.000059375383,0.021685002,0.000061308085,0.0000066315865,0.000028830678,0.00017150029,0.00015436888,0.0016726888],"genre_scores_gemma":[0.97441465,0.00004770532,0.023628186,0.000013811729,0.0000029953997,0.000013396737,0.00014239138,0.000013763578,0.0017231542],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9999069,0.000010128502,0.0000052024116,0.00002593181,0.00004069469,0.0000111578875],"domain_scores_gemma":[0.9998739,0.000015361604,0.000019189629,0.000011360234,0.00006117645,0.000019078738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022120876,0.00016337246,0.00022474334,0.00069702964,0.00032704507,0.00046783686,0.00025841952,0.000265334,0.0005159825],"category_scores_gemma":[0.0003919786,0.00019764392,0.0001262654,0.00044449305,0.00015588006,0.0006455963,0.0004215819,0.0001667938,0.00015965765],"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":[0.0007045336,0.0003721344,0.27171883,0.00009348301,0.000056031873,0.00019507589,0.0006735407,0.017400077,0.52559036,0.0012608212,0.0007719221,0.18116315],"study_design_scores_gemma":[0.0001659316,0.00033321563,0.5704782,0.000017121443,0.00013781834,0.00028690926,0.00071387296,0.32911602,0.09266236,0.0014107348,0.0045854812,0.00009234631],"about_ca_topic_score_codex":0.011134475,"about_ca_topic_score_gemma":0.031226674,"teacher_disagreement_score":0.011134475,"about_ca_system_score_codex":0.000225118,"about_ca_system_score_gemma":0.00047624047,"threshold_uncertainty_score":0.02213931},"labels":[],"label_agreement":null},{"id":"W2142996145","doi":"10.1016/j.rse.2010.02.001","title":"Modeling fire severity in black spruce stands in the Alaskan boreal forest using spectral and non-spectral geospatial data","year":2010,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":62,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"U.S. Bureau of Land Management; U.S. Geological Survey; U.S. Fish and Wildlife Service; National Science Foundation","keywords":"Environmental science; Black spruce; Taiga; Boreal; Remote sensing; Vegetation (pathology); Atmospheric sciences; Forestry; Geography; Geology","score_opus":0.014721983326812235,"score_gpt":0.2286485452742916,"score_spread":0.21392656194747936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142996145","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9993728,0.0000274979,0.00032762915,0.000021992908,0.0000040500304,0.0000028121483,0.00009840289,0.000015833451,0.00012897051],"genre_scores_gemma":[0.9990916,0.000021074606,0.0005615467,0.0000039276892,0.0000030214057,0.000003117357,0.00018385288,0.0000031998834,0.00012864385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9998534,0.000039176437,0.000012448436,0.000046794026,0.000019864807,0.00002837281],"domain_scores_gemma":[0.99926955,0.0004053629,0.0000967159,0.000033734847,0.000093385934,0.00010119571],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088611763,0.0006478877,0.0003681591,0.0005525826,0.00047999056,0.0007000723,0.0005468073,0.0004913543,0.00037767662],"category_scores_gemma":[0.0014096939,0.00044420248,0.0005960881,0.00043653615,0.0002955691,0.0005937659,0.00028252255,0.00038146263,0.000054005523],"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":[0.00032652495,0.00020412306,0.22699752,0.00001772805,0.00018204788,0.0000927392,0.00008057874,0.7658187,0.0013545937,0.00023760734,0.00022209922,0.0044657704],"study_design_scores_gemma":[0.000021348149,0.000043668417,0.06754947,0.0000028859688,0.00004565726,0.00002171173,0.00012624913,0.93147844,0.00044749177,0.00018628527,0.000065734544,0.000011131119],"about_ca_topic_score_codex":0.17476784,"about_ca_topic_score_gemma":0.21306968,"teacher_disagreement_score":0.17476784,"about_ca_system_score_codex":0.0013317202,"about_ca_system_score_gemma":0.00077276904,"threshold_uncertainty_score":0.34750116},"labels":[],"label_agreement":null},{"id":"W2144158466","doi":"10.1016/s0034-4257(02)00050-0","title":"Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration","year":2002,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":389,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Forest Service; Ontario Forest Research Institute; Carleton University","funders":"Canadian Forest Service; Natural Sciences and Engineering Research Council of Canada; Ministry of Natural Resources; National Geographic Society","keywords":"Computer science; Tree (set theory); Remote sensing; Change detection; Crown (dentistry); Boundary (topology); Artificial intelligence; Data mining; Mathematics; Geology","score_opus":0.009299320227389865,"score_gpt":0.19731405862666673,"score_spread":0.18801473839927688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144158466","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9700024,0.00023131765,0.027594542,0.00004065565,0.000015285525,0.00005489808,0.00031530924,0.00035875727,0.0013869394],"genre_scores_gemma":[0.95504844,0.00011727727,0.043074235,0.000022559643,0.000011228792,0.000023692659,0.00055660453,0.000022856544,0.0011229772],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986935,0.000025158712,0.0000081782355,0.000027950831,0.000041940406,0.000027359678],"domain_scores_gemma":[0.9994822,0.00022619982,0.00005869032,0.000036580717,0.00016045247,0.000035840592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047171925,0.00016314969,0.00026084192,0.0010334088,0.00024837308,0.0005171862,0.00031742163,0.00023331441,0.00058767264],"category_scores_gemma":[0.0008730732,0.00018757838,0.00012717406,0.0004169092,0.00015390746,0.00032439287,0.00016518023,0.00015043395,0.00016584764],"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":[0.0009953694,0.00037141953,0.11236892,0.00019820945,0.000092729846,0.00016596436,0.00036917144,0.039615683,0.1922791,0.00085833785,0.0029375902,0.64974743],"study_design_scores_gemma":[0.000059313556,0.00010144697,0.46712583,0.000013207132,0.00007351153,0.00023593067,0.000141648,0.5007891,0.02953754,0.00033071623,0.0015695349,0.000022156692],"about_ca_topic_score_codex":0.018525064,"about_ca_topic_score_gemma":0.052696325,"teacher_disagreement_score":0.018525064,"about_ca_system_score_codex":0.00034095487,"about_ca_system_score_gemma":0.0006482623,"threshold_uncertainty_score":0.03683448},"labels":[],"label_agreement":null},{"id":"W2145022968","doi":"10.1016/j.rse.2011.02.025","title":"Characterizing the state and processes of change in a dynamic forest environment using hierarchical spatio-temporal segmentation","year":2011,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":119,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"Canadian Space Agency; U.S. Geological Survey","keywords":"Remote sensing; Change detection; Environmental science; Vegetation (pathology); Segmentation; Computer science; Brightness; Environmental change; Climate change; Geography; Artificial intelligence; Geology","score_opus":0.03226072711099083,"score_gpt":0.2326573325377596,"score_spread":0.2003966054267688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145022968","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8444369,0.0003622933,0.15291052,0.00013585946,0.000011990647,0.000029604518,0.0005005692,0.00028727239,0.0013250901],"genre_scores_gemma":[0.98102564,0.00007194427,0.018252414,0.000010484749,0.000008630139,0.000010301919,0.00032853862,0.0000195552,0.00027254084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99983776,0.000021624488,0.000011615315,0.000052347663,0.000032191336,0.00004450975],"domain_scores_gemma":[0.9995427,0.00022980214,0.00009176745,0.000037688304,0.000058311027,0.000039867187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041533637,0.00023394838,0.00034360174,0.0015346725,0.00036676682,0.0009419848,0.00047525662,0.0004521072,0.0004526499],"category_scores_gemma":[0.000984149,0.00030323578,0.00047929518,0.0015624565,0.0005243906,0.00083478517,0.00039542568,0.00030166772,0.00007571853],"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":[0.0006459683,0.00020499846,0.12061759,0.0001462541,0.00026444896,0.00045191904,0.00072312634,0.67259926,0.06995678,0.008489437,0.0009938219,0.12490637],"study_design_scores_gemma":[0.000004716847,0.00001526924,0.03441488,0.0000051538236,0.00002405265,0.000044929366,0.000101028534,0.9597648,0.0018889966,0.003478269,0.0002452949,0.000012732743],"about_ca_topic_score_codex":0.023236206,"about_ca_topic_score_gemma":0.03632825,"teacher_disagreement_score":0.023236206,"about_ca_system_score_codex":0.00064031547,"about_ca_system_score_gemma":0.0005336054,"threshold_uncertainty_score":0.046201944},"labels":[],"label_agreement":null},{"id":"W2154103236","doi":"10.1016/j.rse.2014.04.018","title":"Potentials and limitations for estimating daytime ecosystem respiration by combining tower-based remote sensing and carbon flux measurements","year":2014,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Directorate for Biological Sciences; Canadian Foundation for Climate and Atmospheric Sciences","keywords":"Eddy covariance; Ecosystem respiration; Environmental science; Daytime; Photosynthetically active radiation; Ecosystem; Primary production; Atmospheric sciences; Carbon cycle; Respiration; Flux (metallurgy); Soil respiration; Diurnal cycle; Photosynthesis; Soil science; Ecology; Soil water; Chemistry; Physics; Botany; Biology","score_opus":0.025347849761474778,"score_gpt":0.21612766531443514,"score_spread":0.19077981555296036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154103236","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67046213,0.013910856,0.3013926,0.0011695233,0.00033004413,0.0002225271,0.0027064458,0.0012094621,0.008596489],"genre_scores_gemma":[0.85665953,0.002647981,0.13748506,0.0002405252,0.0002477259,0.00021131264,0.0014492625,0.00011712987,0.00094149495],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9978339,0.0008609878,0.00015741063,0.00053642323,0.0005238509,0.000087436834],"domain_scores_gemma":[0.9868217,0.00988524,0.0005521355,0.0011458498,0.0014388236,0.00015614729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0066941543,0.0016006351,0.0010128696,0.0013558342,0.00053788204,0.0026950263,0.0018716537,0.002065875,0.000856657],"category_scores_gemma":[0.015929114,0.00076412415,0.0009001177,0.0023467722,0.00057763344,0.0036918954,0.0008831094,0.0007913988,0.0005038946],"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":[0.0010942344,0.00039197365,0.39262596,0.0023017903,0.0020379387,0.0003193605,0.0007074691,0.063641295,0.11574377,0.0032480622,0.0019415106,0.41594672],"study_design_scores_gemma":[0.00015023546,0.0006435465,0.2922546,0.00063085277,0.002282578,0.0014819457,0.00074484263,0.6131197,0.063489996,0.011531138,0.013255006,0.00041558393],"about_ca_topic_score_codex":0.009616607,"about_ca_topic_score_gemma":0.015417036,"teacher_disagreement_score":0.009616607,"about_ca_system_score_codex":0.00069570175,"about_ca_system_score_gemma":0.00084488833,"threshold_uncertainty_score":0.035402477},"labels":[],"label_agreement":null},{"id":"W2154572745","doi":"10.1016/j.rse.2003.11.002","title":"Land cover mapping of North and Central America—Global Land Cover 2000","year":2003,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":171,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada","funders":"Canadian Space Agency; Centre National d’Etudes Spatiales; European Commission; U.S. Geological Survey; National Aeronautics and Space Administration","keywords":"Land cover; Remote sensing; Moderate-resolution imaging spectroradiometer; Land use; Environmental science; Vegetation (pathology); Geography; Satellite; Physical geography","score_opus":0.006000058320809887,"score_gpt":0.17991265862933373,"score_spread":0.17391260030852385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154572745","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89683336,0.003100944,0.0056369323,0.0007130182,0.00016738808,0.00034803525,0.053120624,0.0013043365,0.038775288],"genre_scores_gemma":[0.91022146,0.00172742,0.016829913,0.00028420458,0.000062849635,0.00025447406,0.050241686,0.00011457329,0.020263515],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99988914,0.000008889731,0.00000798995,0.000027906237,0.000047027712,0.00001901331],"domain_scores_gemma":[0.99955887,0.000017794766,0.000050086936,0.000037553473,0.00028723487,0.000048487716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002748611,0.00030574508,0.00017001564,0.0017850108,0.00032865567,0.0004857935,0.0003588481,0.00013656115,0.0011372615],"category_scores_gemma":[0.00046048628,0.00017138613,0.00011645701,0.0028142224,0.00011114448,0.00039956052,0.00033880386,0.000186227,0.00032940917],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006403641,0.00042287534,0.46233183,0.00040126324,0.00034341705,0.00044571565,0.0018159624,0.007473496,0.018150777,0.0018382432,0.12630375,0.3798322],"study_design_scores_gemma":[0.00002733944,0.000015754524,0.925088,0.000038112517,0.000063430576,0.000088666755,0.00046279258,0.0032921713,0.0021749518,0.00018997777,0.068547316,0.0000115527455],"about_ca_topic_score_codex":0.28079516,"about_ca_topic_score_gemma":0.4706948,"teacher_disagreement_score":0.71920484,"about_ca_system_score_codex":0.0011613496,"about_ca_system_score_gemma":0.0016207093,"threshold_uncertainty_score":0.5583215},"labels":[],"label_agreement":null},{"id":"W2155162346","doi":"10.1016/j.rse.2007.06.013","title":"Cross-sensor change detection over a forested landscape: Options to enable continuity of medium spatial resolution measures","year":2007,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":42,"is_retracted":false,"has_abstract":false,"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","funders":"Ministry of Forests, Lands and Natural Resource Operations","keywords":"Remote sensing; Change detection; Normalization (sociology); Pixel; Computer science; Image resolution; Environmental science; Advanced Spaceborne Thermal Emission and Reflection Radiometer; Thematic Mapper; Geography; Satellite imagery; Computer vision; Digital elevation model","score_opus":0.017699612953548573,"score_gpt":0.24328294256284802,"score_spread":0.22558332960929944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155162346","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.289024,0.0010808231,0.6923486,0.0010598802,0.00019892692,0.0002803447,0.0014682752,0.0026043341,0.011934728],"genre_scores_gemma":[0.7057567,0.00020922207,0.29092273,0.0002850069,0.00007492863,0.00018859522,0.00087866554,0.00010592217,0.0015782603],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9989735,0.00022891053,0.000076343946,0.00032457983,0.00023695957,0.00015977802],"domain_scores_gemma":[0.9905565,0.0032841188,0.00066232955,0.0034391119,0.0017923996,0.00026552356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003436624,0.00052034017,0.0009132208,0.0014353087,0.000660607,0.0016749518,0.0017508185,0.0016178988,0.002969914],"category_scores_gemma":[0.013507162,0.00069861277,0.0005027185,0.0023479303,0.0006634725,0.0045021395,0.0025796192,0.0011570663,0.0005714001],"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":[0.0028890837,0.001304404,0.085683584,0.000452673,0.0003856201,0.0003781954,0.0010931692,0.04157419,0.1347302,0.0162931,0.0046195947,0.7105961],"study_design_scores_gemma":[0.0006942191,0.0016268597,0.17228875,0.00028104358,0.0006760196,0.0019624366,0.0010357378,0.5906313,0.16895059,0.02765951,0.03376158,0.00043202005],"about_ca_topic_score_codex":0.0043212646,"about_ca_topic_score_gemma":0.008537462,"teacher_disagreement_score":0.0043212646,"about_ca_system_score_codex":0.00042513193,"about_ca_system_score_gemma":0.00059215433,"threshold_uncertainty_score":0.018174827},"labels":[],"label_agreement":null},{"id":"W2156220628","doi":"10.1016/j.rse.2007.02.019","title":"Integration of spatial–spectral information for the improved extraction of endmembers","year":2007,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":243,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Geological Survey of Canada; University of Alberta","funders":"","keywords":"Endmember; Hyperspectral imaging; Pattern recognition (psychology); Contrast (vision); Pixel; Artificial intelligence; Spectral signature; Projection (relational algebra); Mathematics; Computer science; Basis (linear algebra); Singular value decomposition; Set (abstract data type); Remote sensing; Computer vision; Algorithm; Geography","score_opus":0.011494760901427896,"score_gpt":0.22508498633424928,"score_spread":0.21359022543282138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156220628","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054218084,0.00051682803,0.9413011,0.00013340628,0.00009376025,0.00003538672,0.00015583647,0.001319807,0.0022257783],"genre_scores_gemma":[0.2704495,0.0005672049,0.72475743,0.000099171564,0.000070648915,0.00006802636,0.0007410647,0.00029060658,0.0029563217],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999729,0.00004645886,0.000016568514,0.000041873212,0.00013562095,0.000030525556],"domain_scores_gemma":[0.999566,0.00011328152,0.000026502381,0.000060045342,0.00022011883,0.000014064171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058187393,0.0007074609,0.00053271983,0.0010230697,0.00032069642,0.00067774305,0.00049578445,0.0004938137,0.0022248705],"category_scores_gemma":[0.0012614573,0.00040771766,0.0005587749,0.0012124879,0.00021087864,0.0012697829,0.0006048284,0.000624215,0.0012590709],"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":[0.00023196799,0.00021164397,0.001655475,0.00020901744,0.00012107879,0.0000737924,0.000088165354,0.019031186,0.49967167,0.002115925,0.0016640358,0.474926],"study_design_scores_gemma":[0.000024019097,0.00010631939,0.011141987,0.000034372744,0.00021407702,0.00023526126,0.000044791534,0.6272762,0.34872916,0.0022786916,0.009847298,0.00006780001],"about_ca_topic_score_codex":0.0013060594,"about_ca_topic_score_gemma":0.004056734,"teacher_disagreement_score":0.0022248705,"about_ca_system_score_codex":0.0001461325,"about_ca_system_score_gemma":0.00040825424,"threshold_uncertainty_score":0.0074428916},"labels":[],"label_agreement":null},{"id":"W2158711600","doi":"10.1016/j.rse.2013.10.022","title":"A Surface Temperature Initiated Closure (STIC) for surface energy balance fluxes","year":2013,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":124,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Environment Research Council; Sight Research UK","keywords":"Sensible heat; Latent heat; FluxNet; Eddy covariance; Energy balance; Environmental science; Atmosphere (unit); Meteorology; Atmospheric sciences; Thermodynamics; Physics","score_opus":0.009227238080025867,"score_gpt":0.19373493537140518,"score_spread":0.1845076972913793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158711600","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08577303,0.0005345621,0.90072757,0.00044253212,0.0007011184,0.00025506993,0.00047118645,0.0010696673,0.010025392],"genre_scores_gemma":[0.8755413,0.0003079017,0.1158593,0.00015614819,0.0004506833,0.00028315044,0.00044855673,0.0005895574,0.0063633285],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.999405,0.00011436486,0.00003585058,0.00014990973,0.00020200474,0.00009280266],"domain_scores_gemma":[0.9977889,0.00088435865,0.00019414065,0.00036489067,0.0005693261,0.0001984196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015953743,0.0007635345,0.0008866041,0.0010354833,0.0010353951,0.0014135971,0.0016130413,0.0015898622,0.002883072],"category_scores_gemma":[0.00551069,0.0003402332,0.0017346274,0.00047531075,0.001362633,0.0018624284,0.0015155489,0.0021717069,0.00038144388],"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":[0.00043551,0.00043365164,0.008495643,0.00039196483,0.00012551877,0.0008342545,0.00043325315,0.70672506,0.040918082,0.16234082,0.0068998835,0.07196639],"study_design_scores_gemma":[0.000008914467,0.00002407269,0.0003758183,0.000009149832,0.0000083794075,0.000028024799,0.000010158722,0.9951208,0.0010893325,0.0025565783,0.00075625407,0.000012592363],"about_ca_topic_score_codex":0.0062456676,"about_ca_topic_score_gemma":0.0047407025,"teacher_disagreement_score":0.0062456676,"about_ca_system_score_codex":0.001112487,"about_ca_system_score_gemma":0.0016039846,"threshold_uncertainty_score":0.012418628},"labels":[],"label_agreement":null},{"id":"W2161815745","doi":"10.1016/j.rse.2003.12.013","title":"Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture","year":2004,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":2562,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Leaf area index; Hyperspectral imaging; Remote sensing; Enhanced vegetation index; Vegetation (pathology); Normalized Difference Vegetation Index; Environmental science; Context (archaeology); Canopy; Atmospheric radiative transfer codes; Radiative transfer; Vegetation Index; Agronomy; Geography","score_opus":0.015579578932879406,"score_gpt":0.22510290012801984,"score_spread":0.20952332119514044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161815745","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8509953,0.0004040396,0.14698583,0.0001648821,0.000041786443,0.00003408959,0.00012720558,0.00040209762,0.00084473466],"genre_scores_gemma":[0.93775976,0.00020828056,0.061342984,0.000025504454,0.000021953612,0.00003168129,0.00014704344,0.000028709303,0.00043406722],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978584,0.000053402662,0.000011487149,0.00005144193,0.00007650227,0.000021388982],"domain_scores_gemma":[0.9989988,0.00056751055,0.00011486556,0.00008228446,0.0002084623,0.000028099663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013316358,0.0006020654,0.00037746207,0.00041912138,0.000297142,0.0007936637,0.00062916015,0.00070682005,0.0003257956],"category_scores_gemma":[0.0024882427,0.00033812688,0.00040302525,0.00049184373,0.00042574576,0.001295673,0.0003021729,0.00083653897,0.000091553695],"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":[0.00023879805,0.00028417262,0.012229272,0.000063398475,0.0001294843,0.00003249669,0.00007585922,0.8760847,0.012579725,0.0011461849,0.00037985775,0.09675603],"study_design_scores_gemma":[0.000008279095,0.000025715148,0.0028757497,0.000001890314,0.00001127445,0.000008219023,0.000007436643,0.9946629,0.0020879707,0.00025587316,0.000049247777,0.0000054908046],"about_ca_topic_score_codex":0.013852753,"about_ca_topic_score_gemma":0.014591489,"teacher_disagreement_score":0.013852753,"about_ca_system_score_codex":0.00074269756,"about_ca_system_score_gemma":0.00052987406,"threshold_uncertainty_score":0.0275442},"labels":[],"label_agreement":null},{"id":"W2163410149","doi":"10.1016/s0034-4257(02)00018-4","title":"Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture","year":2002,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":1991,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; York University","funders":"Agricultural Research Service; Université Laval","keywords":"Leaf area index; Remote sensing; Environmental science; Hyperspectral imaging; Precision agriculture; Vegetation (pathology); Canopy; Enhanced vegetation index; Chlorophyll; Normalized Difference Vegetation Index; Solar zenith angle; Soil science; Vegetation Index; Agronomy; Agriculture; Geography; Chemistry","score_opus":0.01979938282610478,"score_gpt":0.2079077706472343,"score_spread":0.18810838782112954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163410149","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73668873,0.0015851103,0.25447255,0.00008798646,0.00008479395,0.00006800869,0.0013507233,0.002479047,0.0031831504],"genre_scores_gemma":[0.86413735,0.00044310724,0.13224836,0.00003636549,0.000033457523,0.0000875014,0.0012283898,0.000113146925,0.001672291],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99987733,0.000023634588,0.000005664209,0.00002695525,0.00005303293,0.000013333919],"domain_scores_gemma":[0.999708,0.00008990684,0.000028468367,0.000025858966,0.0001314006,0.000016255473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032315755,0.00051253947,0.0006678435,0.0008042162,0.00027239992,0.00046760944,0.00057139195,0.00040158036,0.0006759871],"category_scores_gemma":[0.0007932229,0.00043297827,0.00038294666,0.00087215396,0.000107142376,0.00059745804,0.00034427602,0.00044637092,0.00028301185],"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":[0.0014804989,0.0008979488,0.03966364,0.00018323648,0.00043586758,0.00008566551,0.00009739712,0.25971586,0.2000496,0.0011194374,0.0034279113,0.49284294],"study_design_scores_gemma":[0.00006689257,0.00012899155,0.029495914,0.0000067002284,0.00016993031,0.000024239269,0.000017661592,0.94322145,0.025590295,0.0004775882,0.00077535235,0.000024959338],"about_ca_topic_score_codex":0.009101626,"about_ca_topic_score_gemma":0.016934438,"teacher_disagreement_score":0.009101626,"about_ca_system_score_codex":0.00038316153,"about_ca_system_score_gemma":0.0004348255,"threshold_uncertainty_score":0.018097281},"labels":[],"label_agreement":null},{"id":"W2164149840","doi":"10.1016/j.rse.2010.04.009","title":"Dark-spot detection from SAR intensity imagery with spatial density thresholding for oil-spill monitoring","year":2010,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Oil Spill Detection and Mitigation","field":"Environmental Science","cited_by":110,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Pixel; Thresholding; Remote sensing; Synthetic aperture radar; Computer science; Artificial intelligence; Intensity (physics); Kernel density estimation; Segmentation; Feature (linguistics); Window (computing); Computer vision; Image (mathematics); Mathematics; Geology; Optics; Physics; Statistics; Estimator","score_opus":0.009066907845279457,"score_gpt":0.20394114977581843,"score_spread":0.19487424193053898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164149840","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6875292,0.00057774794,0.30824187,0.00020326162,0.00005294614,0.000042462914,0.00043789513,0.0007927177,0.0021218758],"genre_scores_gemma":[0.86527413,0.00022339569,0.13263562,0.0000391963,0.00003209575,0.00002361218,0.0005387259,0.000051338095,0.0011819276],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998851,0.00002919131,0.00000763411,0.000018505874,0.000041500178,0.000018044855],"domain_scores_gemma":[0.9996991,0.000120979494,0.00004386215,0.00003806698,0.000075847594,0.000022266788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040136394,0.00029420218,0.00032156528,0.0009354901,0.00014563465,0.00031463438,0.00035437976,0.00024517818,0.0006466641],"category_scores_gemma":[0.0010908538,0.0002608827,0.00019052283,0.00054392853,0.00023588717,0.00036054174,0.00035102273,0.00024083498,0.00015666788],"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":[0.0012938707,0.00035173868,0.024348741,0.0003205294,0.00016847404,0.00019556399,0.00017570112,0.08317923,0.34431836,0.0022968375,0.0033877227,0.5399632],"study_design_scores_gemma":[0.000044046043,0.0001336178,0.047134496,0.000014834095,0.000102296115,0.00024706166,0.00007018081,0.8622269,0.08626843,0.002054062,0.0016671822,0.000036903202],"about_ca_topic_score_codex":0.0014429779,"about_ca_topic_score_gemma":0.0031082428,"teacher_disagreement_score":0.0014429779,"about_ca_system_score_codex":0.0001230266,"about_ca_system_score_gemma":0.00021424351,"threshold_uncertainty_score":0.0028691292},"labels":[],"label_agreement":null},{"id":"W2166897060","doi":"10.1016/j.rse.2011.12.008","title":"Global clumping index map derived from the MODIS BRDF product","year":2012,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":269,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Bidirectional reflectance distribution function; Zenith; Remote sensing; Hotspot (geology); Moderate-resolution imaging spectroradiometer; Environmental science; Solar zenith angle; Leaf area index; Spectroradiometer; Meteorology; Geography; Reflectivity; Geology; Optics; Satellite; Physics","score_opus":0.010621615623825064,"score_gpt":0.20670428653150597,"score_spread":0.1960826709076809,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166897060","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8418917,0.00057945884,0.025922827,0.00052812905,0.0002738995,0.00018045028,0.08620962,0.0056978543,0.03871608],"genre_scores_gemma":[0.9163927,0.00030881356,0.022586336,0.00011732256,0.000051168718,0.000063049025,0.056609105,0.00046448596,0.0034070574],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999367,0.0000032804292,0.0000028092547,0.000018689747,0.000021419972,0.000017018181],"domain_scores_gemma":[0.99986804,0.000014042859,0.000017640468,0.000014271336,0.0000655927,0.000020408694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011504741,0.00029332613,0.00020693387,0.0017778964,0.00016539851,0.00047759656,0.00026744686,0.00031679432,0.00390474],"category_scores_gemma":[0.0002771042,0.000119345095,0.0001965693,0.0018142004,0.00013163291,0.00036342235,0.0002604714,0.00032387124,0.0010201678],"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":[0.0018480585,0.00042533077,0.21868946,0.00062148843,0.00022960371,0.0010698992,0.0005298514,0.073230565,0.17690472,0.004587087,0.12135817,0.40050578],"study_design_scores_gemma":[0.00030860488,0.00013840335,0.7933139,0.00011170469,0.0001317797,0.00034197775,0.00046744556,0.109831214,0.022592522,0.0017713788,0.070868656,0.00012227491],"about_ca_topic_score_codex":0.018623557,"about_ca_topic_score_gemma":0.022440756,"teacher_disagreement_score":0.018623557,"about_ca_system_score_codex":0.0003099662,"about_ca_system_score_gemma":0.000435858,"threshold_uncertainty_score":0.03703034},"labels":[],"label_agreement":null},{"id":"W2169131344","doi":"10.1016/j.rse.2014.08.034","title":"A hybrid method combining neighborhood information from satellite data with modeled diurnal temperature cycles over consecutive days","year":2014,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"State Key Laboratory of Earth Surface Processes and Resource Ecology; National Natural Science Foundation of China","keywords":"Diurnal cycle; Interpolation (computer graphics); Environmental science; Pixel; Linear interpolation; Remote sensing; Satellite; Diurnal temperature variation; Atmospheric model; Meteorology; Mathematics; Computer science; Physics; Geology; Mathematical analysis; Artificial intelligence","score_opus":0.010291612233629165,"score_gpt":0.2181850308363816,"score_spread":0.20789341860275243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169131344","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08184375,0.00056898006,0.91326046,0.00011556676,0.00029191986,0.00013144076,0.0005156322,0.0015060549,0.0017662005],"genre_scores_gemma":[0.4328523,0.0002973767,0.5580126,0.00014532304,0.00023638092,0.0002329993,0.0016915123,0.0002681678,0.0062633073],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995309,0.000090674075,0.000032824384,0.0001772629,0.00012666863,0.000041751082],"domain_scores_gemma":[0.99929047,0.00027563673,0.00004578707,0.00009699078,0.00025444428,0.00003674263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093696057,0.0008136918,0.0013497324,0.0014086993,0.0005790361,0.0009089819,0.0015382929,0.0012776246,0.0020969643],"category_scores_gemma":[0.0016713986,0.00057156594,0.0012245622,0.0015901861,0.0002656299,0.0013153151,0.0007173981,0.00059726107,0.00080220896],"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":[0.00044740262,0.00061948906,0.011570791,0.00020334261,0.00077359105,0.00016675699,0.00010304314,0.3474015,0.023976339,0.001422396,0.0037333812,0.60958195],"study_design_scores_gemma":[0.000020400577,0.000033993103,0.0011741301,0.000005638383,0.000054604727,0.00002907778,0.000014280947,0.996576,0.001226922,0.00030312463,0.0005452157,0.000016578198],"about_ca_topic_score_codex":0.017777326,"about_ca_topic_score_gemma":0.030745475,"teacher_disagreement_score":0.017777326,"about_ca_system_score_codex":0.0003600291,"about_ca_system_score_gemma":0.001137053,"threshold_uncertainty_score":0.0353477},"labels":[],"label_agreement":null},{"id":"W2169765847","doi":"10.1016/j.rse.2008.04.018","title":"Model-based mean square error estimators for k-nearest neighbour predictions and applications using remotely sensed data for forest inventories","year":2008,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":75,"is_retracted":false,"has_abstract":false,"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":"","keywords":"Estimator; Forest inventory; Mean squared error; Variance (accounting); Remote sensing; Computer science; Parametric statistics; k-nearest neighbors algorithm; Random forest; Statistics; Data mining; Environmental science; Mathematics; Geography; Forest management; Artificial intelligence","score_opus":0.07243775103388896,"score_gpt":0.28404791819662895,"score_spread":0.21161016716274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169765847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03563494,0.00058805995,0.96261364,0.00011414149,0.000068734415,0.000027885251,0.00015212705,0.00049496006,0.00030547476],"genre_scores_gemma":[0.49246287,0.00061753416,0.5035844,0.00008052235,0.000105400126,0.00022372934,0.0010014507,0.00033803715,0.0015860883],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979572,0.0010403941,0.00017244797,0.00032944002,0.0004097868,0.000090555404],"domain_scores_gemma":[0.9792615,0.0162602,0.0007921211,0.0010951586,0.002490753,0.00010023997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00883657,0.0008601681,0.0014176138,0.0012786521,0.00076425524,0.0014105614,0.0021961923,0.001713408,0.0010000094],"category_scores_gemma":[0.03553103,0.0011738713,0.0012141597,0.0014064675,0.00073226955,0.003003185,0.0009940804,0.0019124427,0.00047363233],"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":[0.00010892145,0.00008020618,0.002157129,0.000078561134,0.000113525435,0.000016697386,0.00006519031,0.93553483,0.0007785865,0.0041966317,0.00092801754,0.05594158],"study_design_scores_gemma":[0.000005632515,0.00000812732,0.00040997294,0.0000061873816,0.000008954303,0.000008186808,0.0000059598988,0.9969145,0.000374395,0.0021740447,0.00007460885,0.000009434513],"about_ca_topic_score_codex":0.014405159,"about_ca_topic_score_gemma":0.015549829,"teacher_disagreement_score":0.014405159,"about_ca_system_score_codex":0.0013505445,"about_ca_system_score_gemma":0.0014471525,"threshold_uncertainty_score":0.046732783},"labels":[],"label_agreement":null},{"id":"W2198568516","doi":"10.1016/j.rse.2015.12.017","title":"Matching the phenology of Net Ecosystem Exchange and vegetation indices estimated with MODIS and FLUXNET in-situ observations","year":2015,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":109,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Lawrence Berkeley National Laboratory; Natural Sciences and Engineering Research Council of Canada; Environment Canada; U.S. Geological Survey; European Research Council; BIOCAP Canada; Université Laval; Vlaamse regering; Natural Resources Canada; Oak Ridge National Laboratory; Biological and Environmental Research; Canadian Foundation for Climate and Atmospheric Sciences; Microsoft Research; U.S. Department of Energy; National Science Foundation","keywords":"FluxNet; Phenology; Remote sensing; Environmental science; Vegetation (pathology); Vegetation Index; Ecosystem; In situ; Normalized Difference Vegetation Index; Matching (statistics); Leaf area index; Geography; Meteorology; Eddy covariance; Ecology","score_opus":0.026495651192137766,"score_gpt":0.21508263476156517,"score_spread":0.1885869835694274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2198568516","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9887893,0.00009546172,0.004392146,0.00004711474,0.00004343042,0.000017980348,0.0043250876,0.00012088561,0.0021684924],"genre_scores_gemma":[0.9896131,0.0000645828,0.004424563,0.000024116538,0.000013659139,0.0000151118775,0.0046678404,0.00003316372,0.0011439046],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99990165,0.000014492791,0.000006499812,0.00004261931,0.000015216947,0.000019462106],"domain_scores_gemma":[0.9996909,0.00005255936,0.00006782901,0.000042023956,0.00010921214,0.000037529968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029819147,0.00017197167,0.00016161789,0.00071686605,0.00018031542,0.00039785923,0.00015144069,0.00020970446,0.0014885361],"category_scores_gemma":[0.0008359995,0.000083271574,0.00016278142,0.0008654148,0.00006346715,0.00036499245,0.00012932316,0.00012324547,0.00031928305],"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":[0.00025803037,0.00014677164,0.92881924,0.00009339619,0.00014271497,0.00011772454,0.00018597164,0.012753937,0.021865537,0.00040732455,0.003009424,0.03219982],"study_design_scores_gemma":[0.000010286304,0.000015502777,0.97842765,0.000005513302,0.000020243744,0.000056774763,0.00011153179,0.016450193,0.0020572129,0.00014890126,0.0026904217,0.0000058722944],"about_ca_topic_score_codex":0.024757907,"about_ca_topic_score_gemma":0.0758203,"teacher_disagreement_score":0.024757907,"about_ca_system_score_codex":0.00042005593,"about_ca_system_score_gemma":0.00037218185,"threshold_uncertainty_score":0.049227655},"labels":[],"label_agreement":null},{"id":"W2208503665","doi":"10.1016/j.rse.2015.07.013","title":"Application of the photosynthetic light-use efficiency model in a northern Great Plains grassland","year":2015,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Lethbridge","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Photosynthetically active radiation; Normalized Difference Vegetation Index; Leaf area index; Environmental science; Primary production; Canopy; Eddy covariance; Atmospheric sciences; Grassland; Grassland ecosystem; Ecosystem; Remote sensing; FluxNet; Growing season; Photosynthesis; Geography; Ecology; Physics; Botany","score_opus":0.010225714142220326,"score_gpt":0.18467641456341208,"score_spread":0.17445070042119176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2208503665","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99702615,0.00003868864,0.0011963113,0.00009554877,0.000006855606,0.0000126600335,0.00009484753,0.000093864095,0.0014350631],"genre_scores_gemma":[0.9982027,0.000022454406,0.0012934144,0.000013031268,0.000004458423,0.000008325357,0.00007172026,0.0000101742,0.00037380564],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985015,0.00006453171,0.0000081311055,0.000035658566,0.000016310943,0.000025236423],"domain_scores_gemma":[0.9994361,0.0003788216,0.000033444427,0.000038955408,0.00006770265,0.000045012806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006477821,0.00044466215,0.00053175923,0.00026062786,0.0007382537,0.0007466168,0.0008394511,0.0012450876,0.0013071308],"category_scores_gemma":[0.0011851749,0.00034989344,0.00045511805,0.00045911418,0.00041508127,0.00049334485,0.00038595966,0.00036796997,0.00011037475],"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":[0.00020844257,0.00027509173,0.013711445,0.000049217855,0.000054139124,0.0003128076,0.00009062525,0.97608566,0.0031709806,0.00043160614,0.00029176567,0.0053182035],"study_design_scores_gemma":[0.000070212554,0.000052822354,0.0081714345,0.0000030521082,0.000014336308,0.00001703627,0.000062601706,0.99105525,0.0003180438,0.000113300615,0.00011122326,0.000010632665],"about_ca_topic_score_codex":0.15008803,"about_ca_topic_score_gemma":0.08503946,"teacher_disagreement_score":0.15008803,"about_ca_system_score_codex":0.0012046237,"about_ca_system_score_gemma":0.0010523505,"threshold_uncertainty_score":0.29842883},"labels":[],"label_agreement":null},{"id":"W2282410336","doi":"10.1016/j.rse.2016.01.001","title":"The vegetation greenness trend in Canada and US Alaska from 1984–2012 Landsat data","year":2016,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":428,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Aeronautics and Space Administration","keywords":"Tundra; Normalized Difference Vegetation Index; Greening; Vegetation (pathology); Physical geography; Environmental science; Taiga; Boreal; Climate change; Geography; Remote sensing; Arctic; Forestry; Geology; Ecology; Oceanography","score_opus":0.028358329012385133,"score_gpt":0.1979870181501147,"score_spread":0.16962868913772958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2282410336","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8963354,0.0013955808,0.00017715985,0.00027356774,0.000050743838,0.000016752398,0.09712023,0.00007888163,0.004551649],"genre_scores_gemma":[0.9188037,0.0010294926,0.00049882685,0.00009116202,0.000017556655,0.000021849693,0.07390561,0.00002723653,0.0056045],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997451,0.000012587914,0.000026673988,0.000059802107,0.000085298336,0.00007045416],"domain_scores_gemma":[0.99867576,0.000078075456,0.00018563613,0.0000383107,0.0008453833,0.0001768655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037140012,0.00025745746,0.00021840922,0.0038308147,0.00088101625,0.0006883144,0.00046211368,0.0003030124,0.0016603345],"category_scores_gemma":[0.0012928649,0.00018567524,0.00044861724,0.0060221283,0.0002919913,0.0004697409,0.00040353712,0.00035027758,0.0003316528],"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":[0.00017925212,0.00003431867,0.9693215,0.0001425306,0.00022816632,0.00017445005,0.00050582644,0.0028056016,0.00055535627,0.00030194424,0.015110085,0.010641076],"study_design_scores_gemma":[0.000003771157,0.0000036673234,0.99285126,0.000032480788,0.00004319608,0.00003360337,0.0004568136,0.0005378939,0.00011877115,0.000027596863,0.005881949,0.000009017001],"about_ca_topic_score_codex":0.9832223,"about_ca_topic_score_gemma":0.99418443,"teacher_disagreement_score":0.016777694,"about_ca_system_score_codex":0.007997957,"about_ca_system_score_gemma":0.009762026,"threshold_uncertainty_score":0.058029532},"labels":[],"label_agreement":null},{"id":"W2372903769","doi":"10.1016/j.rse.2016.04.022","title":"Circumpolar vegetation dynamics product for global change study","year":2016,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":68,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Goddard Space Flight Center; National Weather Service; U.S. Forest Service; National Oceanic and Atmospheric Administration; National Science Foundation","keywords":"Normalized Difference Vegetation Index; Circumpolar star; Environmental science; Climatology; Vegetation (pathology); Enhanced vegetation index; Growing season; Phenology; Physical geography; Climate change; Atmospheric sciences; Geography; Vegetation Index; Geology; Ecology; Oceanography","score_opus":0.047769054919455496,"score_gpt":0.24754456111836715,"score_spread":0.19977550619891166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2372903769","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0128582,0.00047830623,0.0018077439,0.000355889,0.00024017744,0.000116299045,0.9748171,0.0009804338,0.008345932],"genre_scores_gemma":[0.05031772,0.0009069588,0.009590268,0.00021030438,0.00010173923,0.0005951624,0.926859,0.000536657,0.010882292],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99967396,0.00005354053,0.00003767496,0.00007170489,0.00011102132,0.000052061998],"domain_scores_gemma":[0.9981183,0.00019312135,0.00032672798,0.0002769089,0.0008240005,0.00026101872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009434597,0.00082439417,0.0006533587,0.003281417,0.00040382307,0.0010018661,0.0007438687,0.0003018999,0.012438912],"category_scores_gemma":[0.0018691906,0.00033081943,0.00040121013,0.0054860734,0.000114573144,0.0008748952,0.0009675011,0.0008768164,0.005473774],"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":[0.0004841323,0.00018244817,0.17573859,0.000473724,0.00040462322,0.00018922947,0.00030459504,0.0026526225,0.0014929486,0.0036106398,0.7191211,0.09534542],"study_design_scores_gemma":[0.00022648915,0.000054511318,0.42140105,0.00013516858,0.00022331357,0.00012215086,0.0003379474,0.0037532544,0.0013149104,0.0013519753,0.5710461,0.000033148342],"about_ca_topic_score_codex":0.12051725,"about_ca_topic_score_gemma":0.123797886,"teacher_disagreement_score":0.12051725,"about_ca_system_score_codex":0.0008594749,"about_ca_system_score_gemma":0.002487314,"threshold_uncertainty_score":0.23963153},"labels":[],"label_agreement":null},{"id":"W2412410070","doi":"10.1016/j.rse.2016.05.004","title":"Light use efficiency of peatlands: Variability and suitability for modeling ecosystem production","year":2016,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Peatlands and Wetlands Ecology","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Université Laval; Center for Northern Studies; Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada; Goddard Space Flight Center; Fonds Québécois de la Recherche sur la Nature et les Technologies; Canadian Foundation for Climate and Atmospheric Sciences","keywords":"Peat; Eddy covariance; Environmental science; Primary production; Photosynthetically active radiation; Ecosystem respiration; Atmospheric sciences; Bog; Ecosystem; Carbon cycle; Biomass (ecology); Sandhill; Flux (metallurgy); Vegetation (pathology); Ecology; Photosynthesis; Botany; Chemistry; Biology","score_opus":0.014694693560001549,"score_gpt":0.21093982479156068,"score_spread":0.19624513123155912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2412410070","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99625957,0.000019870251,0.0033072114,0.000013599814,0.0000012977583,0.000004270887,0.000075886885,0.000019664732,0.0002987489],"genre_scores_gemma":[0.99938273,0.000010259043,0.00046889906,0.0000015777873,8.3062554e-7,0.0000057515535,0.000049833183,0.000005244801,0.000074786854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9998977,0.000036345256,0.000006627958,0.000025897245,0.000010427381,0.000022978964],"domain_scores_gemma":[0.99919873,0.00058754673,0.00006894118,0.00006140908,0.000049671817,0.000033737408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063444406,0.00025611802,0.00022074206,0.00034662217,0.00021811122,0.000527352,0.0004326414,0.00037306207,0.0003573309],"category_scores_gemma":[0.0020425646,0.00017031922,0.00042429205,0.00039696327,0.00025622034,0.0005345805,0.00023541556,0.0002295078,0.00006725117],"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":[0.00015769934,0.00013222988,0.119722,0.000020363763,0.00007829864,0.000056414578,0.00006401247,0.8680255,0.0043634465,0.00053924863,0.0001153501,0.0067255218],"study_design_scores_gemma":[0.000006934412,0.000014613702,0.03418002,0.0000025708516,0.000013084694,0.000016846403,0.000028131277,0.96442366,0.00093099516,0.00031785254,0.000059698694,0.0000056385993],"about_ca_topic_score_codex":0.025705447,"about_ca_topic_score_gemma":0.021196513,"teacher_disagreement_score":0.025705447,"about_ca_system_score_codex":0.0005757235,"about_ca_system_score_gemma":0.00038374955,"threshold_uncertainty_score":0.0511117},"labels":[],"label_agreement":null},{"id":"W2429241456","doi":"10.1016/j.rse.2016.06.003","title":"Monitoring snow wetness in an Alpine Basin using combined C-band SAR and MODIS data","year":2016,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency; U.S. Geological Survey","keywords":"Snow; Snowpack; Remote sensing; Environmental science; Structural basin; Snow cover; Snowmelt; Drainage basin; Synthetic aperture radar; Geology; Geography; Geomorphology; Cartography","score_opus":0.0653704274780495,"score_gpt":0.24889550771995045,"score_spread":0.18352508024190095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2429241456","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9993917,0.000040098355,0.00012862778,0.000010583604,0.0000024892422,0.0000040238883,0.00015576924,0.000019859006,0.0002468159],"genre_scores_gemma":[0.998544,0.0000391016,0.0008981552,0.000009176126,0.0000092845385,0.0000057679695,0.00036175226,0.0000031762206,0.00012961168],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998708,0.000019454936,0.000011763721,0.000038832557,0.000035032208,0.000024121411],"domain_scores_gemma":[0.9996939,0.000055443277,0.0000633196,0.000025462361,0.0000979371,0.000064011765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003939316,0.00036539717,0.00032692036,0.0010115803,0.00039139052,0.00052005256,0.00031552173,0.0003820198,0.00038938108],"category_scores_gemma":[0.00041017347,0.00024508563,0.00024898455,0.00085069536,0.00018762292,0.0004665511,0.00027243525,0.00016590285,0.00009318164],"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":[0.0009920789,0.000587371,0.87302506,0.000090764464,0.00043055593,0.00045209663,0.00034621163,0.017372675,0.06871344,0.00008030573,0.00069878664,0.037210718],"study_design_scores_gemma":[0.00007985916,0.00013559648,0.9314072,0.0000080192785,0.00013364737,0.000082999526,0.00017410851,0.06476487,0.0028210771,0.000040254974,0.00033890735,0.000013429212],"about_ca_topic_score_codex":0.023806836,"about_ca_topic_score_gemma":0.05422199,"teacher_disagreement_score":0.023806836,"about_ca_system_score_codex":0.00040668162,"about_ca_system_score_gemma":0.0004346098,"threshold_uncertainty_score":0.04733652},"labels":[],"label_agreement":null},{"id":"W2465659380","doi":"10.1016/j.rse.2016.05.006","title":"A new algorithm for discriminating water sources from space: A case study for the southern Beaufort Sea using MODIS ocean color and SMOS salinity data","year":2016,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":41,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institut national des sciences de l'Univers; Japan Aerospace Exploration Agency; Centre National d’Etudes Spatiales; Networks of Centres of Excellence of Canada; Canada Research Chairs; Agence Nationale de la Recherche; European Space Agency; National Aeronautics and Space Administration","keywords":"Colored dissolved organic matter; Seawater; Biogeochemical cycle; Ocean color; Environmental science; Arctic; Surface water; Satellite; Water mass; Salinity; Sea ice; Remote sensing; Precipitation; Geology; Oceanography; Meteorology","score_opus":0.04223300098795808,"score_gpt":0.24599823278268124,"score_spread":0.20376523179472317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2465659380","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12551479,0.0002739797,0.8674945,0.00029698652,0.00012300524,0.00023993851,0.0002798779,0.0031330881,0.0026438269],"genre_scores_gemma":[0.12870163,0.00010761078,0.86619157,0.00008749737,0.000031842555,0.00006475489,0.00042533255,0.00016583195,0.0042239246],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967194,0.00003937513,0.000028641867,0.00010076515,0.000118345895,0.00004102261],"domain_scores_gemma":[0.9993419,0.0001500513,0.00003437187,0.00006743042,0.00037834834,0.000027827571],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010276258,0.00071549456,0.0006533521,0.0010922232,0.00077579974,0.0012737529,0.0010539388,0.0010634004,0.0014788932],"category_scores_gemma":[0.0014901763,0.0003150645,0.0005383126,0.001157407,0.00030736014,0.0009001276,0.00069703336,0.0005805289,0.00063372613],"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":[0.00021602327,0.00021336385,0.010394439,0.00007299848,0.00010700977,0.00026952382,0.0002230186,0.06714775,0.036670256,0.0016084712,0.004002891,0.87907416],"study_design_scores_gemma":[0.00006114735,0.00010948516,0.005899431,0.00001190672,0.00006206352,0.00029272825,0.00012564776,0.9715385,0.015122021,0.00089590135,0.0058457977,0.000035416724],"about_ca_topic_score_codex":0.023580385,"about_ca_topic_score_gemma":0.042471446,"teacher_disagreement_score":0.023580385,"about_ca_system_score_codex":0.0004233075,"about_ca_system_score_gemma":0.0011670903,"threshold_uncertainty_score":0.046886265},"labels":[],"label_agreement":null},{"id":"W2466624252","doi":"10.1016/j.rse.2016.06.014","title":"Evaluation of satellite-based algorithms to estimate photosynthetically available radiation (PAR) reaching the ocean surface at high northern latitudes","year":2016,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"National Aeronautics and Space Administration","keywords":"Albedo (alchemy); Environmental science; Remote sensing; Satellite; Atmospheric radiative transfer codes; Cloud fraction; Solar zenith angle; Latitude; Lookup table; Radiative transfer; Atmosphere (unit); Irradiance; Radiance; Cloud cover; Arctic; Mean squared error; Moderate-resolution imaging spectroradiometer; Atmospheric sciences; Meteorology; Geology; Geography; Cloud computing; Mathematics; Geodesy; Physics; Computer science","score_opus":0.01759836786220091,"score_gpt":0.22429870135091892,"score_spread":0.20670033348871802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2466624252","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9676848,0.00040244666,0.02838834,0.00011937714,0.00008627441,0.00011709722,0.00048870564,0.00060790876,0.00210502],"genre_scores_gemma":[0.95498854,0.000160994,0.04212676,0.000050806186,0.000028797269,0.00007119078,0.0015970542,0.00007400594,0.00090186205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991702,0.00027021955,0.0000881013,0.00017349655,0.00022744072,0.000070603404],"domain_scores_gemma":[0.9962851,0.002101707,0.00017513998,0.00018501116,0.0011575788,0.00009536346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003401772,0.0009939768,0.0005932296,0.0008622047,0.00050377834,0.0010797525,0.0009379659,0.0010371216,0.0010319351],"category_scores_gemma":[0.0064409426,0.00030200498,0.0004990261,0.0007911556,0.00035666293,0.000733769,0.00040456827,0.0003468005,0.0002716232],"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":[0.0036572376,0.0014043632,0.055149727,0.00027843448,0.0006724159,0.00011710104,0.00016088285,0.7305877,0.01629326,0.0008766081,0.0016943173,0.18910794],"study_design_scores_gemma":[0.0001872527,0.00039956614,0.016488316,0.0000097286575,0.00007516982,0.000026479884,0.000054559318,0.97721636,0.0051211356,0.000112232374,0.00029399485,0.000015185204],"about_ca_topic_score_codex":0.036529075,"about_ca_topic_score_gemma":0.017797612,"teacher_disagreement_score":0.036529075,"about_ca_system_score_codex":0.0010465891,"about_ca_system_score_gemma":0.0011648074,"threshold_uncertainty_score":0.07263291},"labels":[],"label_agreement":null},{"id":"W2474976943","doi":"10.1016/j.rse.2016.06.002","title":"Remote sensing reflectance anomalies in the ocean","year":2016,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Remote sensing; Ocean color; Colored dissolved organic matter; Satellite; Lookup table; Environmental science; Spectral bands; Reflectivity; Inversion (geology); SeaWiFS; Geology; Scale (ratio); Computer science; Optics; Geography; Physics; Cartography","score_opus":0.013156430122038482,"score_gpt":0.19384268985371111,"score_spread":0.18068625973167263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2474976943","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.994464,0.00026690424,0.0021971918,0.00014485627,0.00002153205,0.0000036051645,0.00052300637,0.00013443743,0.0022443428],"genre_scores_gemma":[0.9972908,0.00017410416,0.001474016,0.000020880316,0.000015368396,0.000001594692,0.0004828726,0.000021484162,0.00051899464],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9999254,0.000011910625,0.0000047789367,0.000022852122,0.000019094046,0.000016068698],"domain_scores_gemma":[0.99991786,0.000015306357,0.000018386012,0.000010791018,0.00002758215,0.000010084556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002077848,0.000205352,0.00016238594,0.00054555375,0.0001264196,0.0003492038,0.00015396692,0.00027664515,0.00063263474],"category_scores_gemma":[0.0004152004,0.0001760525,0.0002059373,0.0006747822,0.00016967734,0.00037027485,0.0002167624,0.00021693752,0.0002010333],"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":[0.0008327919,0.00024220423,0.34999096,0.00020005522,0.00021592813,0.000581956,0.00026297566,0.09244705,0.38066435,0.003462139,0.004137482,0.16696216],"study_design_scores_gemma":[0.000067001136,0.000055025503,0.8452729,0.000022723145,0.00007345916,0.00017020287,0.00019292986,0.1337361,0.016565088,0.0013612594,0.002448355,0.00003495893],"about_ca_topic_score_codex":0.009727456,"about_ca_topic_score_gemma":0.009520885,"teacher_disagreement_score":0.009727456,"about_ca_system_score_codex":0.00021877083,"about_ca_system_score_gemma":0.00020008441,"threshold_uncertainty_score":0.019341648},"labels":[],"label_agreement":null},{"id":"W2503370740","doi":"10.1016/j.rse.2016.07.024","title":"Revisiting the paper “Using radiometric surface temperature for surface energy flux estimation in Mediterranean drylands from a two-source perspective”","year":2016,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":89,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Ministerio de Ciencia e Innovación; Danmarks Frie Forskningsfond; European Commission; Teknologi og Produktion, Det Frie Forskningsråd","keywords":"Environmental science; Latent heat; Remote sensing; Energy balance; Sensible heat; Vegetation (pathology); Radiometry; Radiometer; Bowen ratio; Soil water; Atmospheric sciences; Soil science; Meteorology; Geology; Geography","score_opus":0.009576344545279714,"score_gpt":0.21752843857060764,"score_spread":0.2079520940253279,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2503370740","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027276447,0.031080324,0.20332652,0.37834406,0.3211977,0.0001502168,0.001954613,0.0012264345,0.03544373],"genre_scores_gemma":[0.25934917,0.03168625,0.09423453,0.25680664,0.2635969,0.00019872995,0.0016543677,0.0015505905,0.09092283],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986045,0.00033906303,0.00017116827,0.0004000017,0.00037962053,0.00010567158],"domain_scores_gemma":[0.9928168,0.0026547206,0.00025296598,0.00045949203,0.003622501,0.00019348648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002824569,0.00071811565,0.00069210853,0.0011833766,0.0010878983,0.003940783,0.0021993984,0.0032083462,0.0038078174],"category_scores_gemma":[0.012274212,0.00023613017,0.000902724,0.0018911741,0.0012255225,0.0021685914,0.0012693336,0.0038204237,0.0030177885],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035072284,0.00016487968,0.007936412,0.0028949997,0.00057809835,0.0019456897,0.001834584,0.0048953677,0.015257759,0.0462276,0.70609397,0.21182004],"study_design_scores_gemma":[0.000073941,0.00012157421,0.015918203,0.0006913873,0.00037598464,0.00070151006,0.0014269598,0.013672607,0.010285552,0.03363024,0.92289317,0.00020882857],"about_ca_topic_score_codex":0.01951235,"about_ca_topic_score_gemma":0.02552823,"teacher_disagreement_score":0.01951235,"about_ca_system_score_codex":0.001051951,"about_ca_system_score_gemma":0.0018790325,"threshold_uncertainty_score":0.038797557},"labels":[],"label_agreement":null},{"id":"W2515329145","doi":"10.1016/j.rse.2016.08.021","title":"Bayesian updating of land-cover estimates in a data-rich environment","year":2016,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Land cover; Computer science; Remote sensing; Cover (algebra); Satellite imagery; Set (abstract data type); Bayesian probability; Sequence (biology); Tracing; Satellite; Data mining; Land use; Geography; Artificial intelligence","score_opus":0.012642293407471335,"score_gpt":0.2197099912809751,"score_spread":0.20706769787350376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2515329145","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21105301,0.000838713,0.7837609,0.0010060286,0.0000933881,0.000048712678,0.0005266266,0.00047979472,0.002192852],"genre_scores_gemma":[0.8845938,0.0004821611,0.11189016,0.0001813971,0.00013973411,0.000059968526,0.00076094514,0.000091961425,0.0017999844],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932563,0.0002664657,0.00006310846,0.0001640289,0.00013319554,0.00004755579],"domain_scores_gemma":[0.9918984,0.0063981577,0.00052674994,0.0004530891,0.0006013757,0.00012216822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027242869,0.0004987182,0.0010145705,0.00094215944,0.00043257725,0.0014162787,0.0012179079,0.0012623564,0.00090843864],"category_scores_gemma":[0.021340631,0.0009704849,0.0005122367,0.0009632178,0.00077225076,0.00245388,0.001099829,0.0012561053,0.0002935306],"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":[0.00014422009,0.000063350206,0.0054994076,0.000076057375,0.00010424035,0.00007419461,0.00008481643,0.9366432,0.0013261443,0.0060345978,0.0009883752,0.048961435],"study_design_scores_gemma":[0.000013062704,0.0000072648613,0.0009244722,0.000007160433,0.000010045175,0.000011396688,0.0000055689725,0.9935522,0.0003108152,0.004979075,0.00017220576,0.000006640165],"about_ca_topic_score_codex":0.014584812,"about_ca_topic_score_gemma":0.024458203,"teacher_disagreement_score":0.014584812,"about_ca_system_score_codex":0.0008399032,"about_ca_system_score_gemma":0.0010917863,"threshold_uncertainty_score":0.028999805},"labels":[],"label_agreement":null},{"id":"W2522055505","doi":"10.1016/j.rse.2016.09.014","title":"Multisite analysis of land surface phenology in North American temperate and boreal deciduous forests from Landsat","year":2016,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":152,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Office of Science; Canadian Foundation for Climate and Atmospheric Sciences","keywords":"Phenology; Boreal; Taiga; Temperate rainforest; Temperate climate; Temperate forest; Deciduous; Environmental science; Temperate deciduous forest; Land cover; Climate change; Physical geography; Remote sensing; Growing season; Climatology; Geography; Ecosystem; Ecology; Land use; Forestry; Geology","score_opus":0.0056944915051861545,"score_gpt":0.19973080085721398,"score_spread":0.1940363093520278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2522055505","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99938214,0.00003477804,0.0001454339,0.000006614497,0.0000022329687,0.0000016095194,0.00026946413,0.0000084900075,0.00014921068],"genre_scores_gemma":[0.99822646,0.00002090194,0.000575953,0.0000058514784,0.0000028582278,0.0000040091404,0.0009769752,0.0000044745198,0.00018253048],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99989426,0.000015382733,0.0000071035047,0.00003386728,0.000026538532,0.000022685217],"domain_scores_gemma":[0.9997489,0.000054518667,0.00004061042,0.000025626598,0.00008866511,0.000041638184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002893964,0.00017190314,0.0001855333,0.0008271738,0.00032000343,0.00032529872,0.00022337186,0.00016556386,0.00040266407],"category_scores_gemma":[0.0003479638,0.00013162651,0.00022687478,0.00072298246,0.00012046639,0.00027269588,0.0001969509,0.00012184719,0.00008792791],"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":[0.0003450156,0.00024012258,0.92849356,0.000047061203,0.000138762,0.00014862632,0.0003369782,0.0037592328,0.03475211,0.00011833554,0.0007569471,0.030863214],"study_design_scores_gemma":[0.0000040798236,0.0000073252513,0.9959223,0.0000012948667,0.000013478443,0.000030108666,0.000092066904,0.003489104,0.00028038534,0.000016504355,0.00014050888,0.0000029148175],"about_ca_topic_score_codex":0.050502717,"about_ca_topic_score_gemma":0.16889393,"teacher_disagreement_score":0.050502717,"about_ca_system_score_codex":0.0003541069,"about_ca_system_score_gemma":0.0002980741,"threshold_uncertainty_score":0.100417495},"labels":[],"label_agreement":null},{"id":"W2556030190","doi":"10.1016/j.rse.2016.10.040","title":"High-resolution global maps of 21st-century annual forest loss: Independent accuracy assessment and application in a temperate forest region of Atlantic Canada","year":2016,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Water Network; University of Waterloo; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Polygon (computer graphics); Temperate forest; Temperate rainforest; Raster graphics; Thematic map; Remote sensing; Forest inventory; Random forest; Temperate climate; Environmental science; Forest ecology; Scale (ratio); Physical geography; Forest management; Geography; Cartography; Ecosystem; Computer science; Ecology; Agroforestry","score_opus":0.004464807362212741,"score_gpt":0.20318001620724452,"score_spread":0.19871520884503177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2556030190","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9901175,0.00020256145,0.0009697568,0.00011045682,0.00000753931,0.000027002656,0.006216297,0.0000927884,0.002256034],"genre_scores_gemma":[0.9920042,0.00015691854,0.0026569527,0.000014419162,0.000004459969,0.000011355775,0.0041417787,0.000018361992,0.0009914663],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99975544,0.000021973901,0.000014506004,0.000044959135,0.00010230276,0.000060826747],"domain_scores_gemma":[0.99859554,0.0001450237,0.00012213665,0.0001347165,0.00088544545,0.00011717371],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077456015,0.00026212735,0.00020329241,0.0012664371,0.0006416081,0.00088791107,0.0005185538,0.00023294277,0.0007025208],"category_scores_gemma":[0.001726902,0.00016701724,0.0002414539,0.0022811433,0.00034471857,0.0003920016,0.0005290451,0.0002799373,0.00015107938],"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":[0.0004990212,0.000234898,0.8197697,0.00010371835,0.0002642531,0.00022510396,0.0013335627,0.05945116,0.004582542,0.0008009327,0.0046316274,0.10810355],"study_design_scores_gemma":[0.000032845743,0.000011061291,0.9772479,0.000016737305,0.000043043638,0.000040055096,0.00044861613,0.019362008,0.00046444216,0.00010318702,0.002210741,0.000019311168],"about_ca_topic_score_codex":0.9587066,"about_ca_topic_score_gemma":0.979676,"teacher_disagreement_score":0.041293383,"about_ca_system_score_codex":0.0066386624,"about_ca_system_score_gemma":0.005614169,"threshold_uncertainty_score":0.08307308},"labels":[],"label_agreement":null},{"id":"W2557040271","doi":"10.1016/j.rse.2016.10.038","title":"Lidar-based estimates of aboveground biomass in the continental US and Mexico using ground, airborne, and satellite observations","year":2016,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":116,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Science Mission Directorate; National Aeronautics and Space Administration","keywords":"Lidar; Environmental science; Remote sensing; Estimator; Satellite; Forest inventory; Sampling (signal processing); Geology; Mathematics; Statistics; Forest management; Detector; Computer science; Physics","score_opus":0.022684677663750753,"score_gpt":0.23428321472690702,"score_spread":0.21159853706315626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2557040271","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9971486,0.00010053161,0.00083280314,0.00002947672,0.0000015899213,0.000011105639,0.001070158,0.0000209482,0.0007849375],"genre_scores_gemma":[0.9925269,0.00019152391,0.00404587,0.000014042294,0.000004819017,0.000033513577,0.0026715877,0.000005050884,0.00050671236],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999014,0.00002220125,0.000009416814,0.00003263108,0.000024065834,0.000010203846],"domain_scores_gemma":[0.99967444,0.0000800349,0.00011232032,0.000020938249,0.00009457282,0.000017644457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047568133,0.00016943256,0.0001248056,0.00093640503,0.00022875147,0.00032646782,0.0003396322,0.00015188118,0.00055375847],"category_scores_gemma":[0.000893112,0.00012092595,0.00016728339,0.00095783034,0.00009270809,0.00035266898,0.00028120226,0.00013692859,0.00008896446],"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":[0.000039648516,0.000063689404,0.9728032,0.000015948777,0.000063782034,0.000030112069,0.00012501,0.006287301,0.000582681,0.0002367161,0.0004429361,0.019309172],"study_design_scores_gemma":[0.000014847298,0.000033555953,0.9761528,0.00002219358,0.00004658018,0.000028171648,0.00045133373,0.02139377,0.00036170043,0.00017015621,0.0013169781,0.000007844111],"about_ca_topic_score_codex":0.121598154,"about_ca_topic_score_gemma":0.2218526,"teacher_disagreement_score":0.121598154,"about_ca_system_score_codex":0.00066763384,"about_ca_system_score_gemma":0.00037656716,"threshold_uncertainty_score":0.24178076},"labels":[],"label_agreement":null},{"id":"W2570633972","doi":"10.1016/j.rse.2016.12.020","title":"Comparison of commonly-used microwave radiative transfer models for snow remote sensing","year":2017,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":80,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Center for Northern Studies; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Environment Canada; Goddard Space Flight Center","keywords":"Radiative transfer; Snow; Snowpack; Atmospheric radiative transfer codes; Materials science; Brightness temperature; Standard deviation; Microwave; Scattering; Environmental science; Remote sensing; Computational physics; Polarization (electrochemistry); Brightness; Atmospheric sciences; Optics; Physics; Meteorology; Geology; Mathematics; Chemistry","score_opus":0.06870269122654661,"score_gpt":0.2704664805389094,"score_spread":0.20176378931236283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2570633972","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8705084,0.003013602,0.101286225,0.00047060888,0.00052635826,0.0002092427,0.007824061,0.0060955673,0.01006601],"genre_scores_gemma":[0.96026707,0.0010593953,0.029690105,0.00010782978,0.000071287235,0.00014107433,0.0062089907,0.000767959,0.0016861785],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995695,0.00012899589,0.00004668999,0.00010284831,0.00010428548,0.000047730846],"domain_scores_gemma":[0.9988147,0.00055513246,0.00006174351,0.00014850477,0.00037699248,0.000042970605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014779071,0.0010695395,0.0006671507,0.0010733472,0.00055014313,0.00090722897,0.0016444025,0.0008395947,0.0018896341],"category_scores_gemma":[0.0026060739,0.00026342462,0.0012087714,0.0011829772,0.00016914944,0.0013316466,0.0004106575,0.00052132417,0.0008399572],"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":[0.000944199,0.0004903708,0.027799126,0.0005556062,0.0008385003,0.00013432803,0.0001581226,0.83135384,0.010690773,0.00211553,0.005278603,0.119640954],"study_design_scores_gemma":[0.00010706965,0.00018652988,0.013065117,0.000036974412,0.00023496171,0.000050261177,0.000107803666,0.9759828,0.006211733,0.00065354496,0.0033071775,0.000056143326],"about_ca_topic_score_codex":0.023675032,"about_ca_topic_score_gemma":0.021400506,"teacher_disagreement_score":0.023675032,"about_ca_system_score_codex":0.0010043465,"about_ca_system_score_gemma":0.00086770195,"threshold_uncertainty_score":0.047074437},"labels":[],"label_agreement":null},{"id":"W2582059183","doi":"10.1016/j.rse.2017.01.021","title":"Validation of SMAP surface soil moisture products with core validation sites","year":2017,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":739,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Guelph","funders":"Jet Propulsion Laboratory; Canadian Space Agency; Australian Research Council; Kuwait Foundation for the Advancement of Sciences; Environment Canada; California Institute of Technology; National Aeronautics and Space Administration","keywords":"Environmental science; Remote sensing; Radiometer; Radar; Water content; Calibration; Pixel; Mean squared error; Moisture; Scale (ratio); Image resolution; Meteorology; Computer science; Geology; Mathematics; Statistics","score_opus":0.02188963571086238,"score_gpt":0.23131434340876303,"score_spread":0.20942470769790064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2582059183","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9732414,0.000043720946,0.018694924,0.00004135861,0.000026142672,0.00023961706,0.0048200097,0.00084366766,0.002049276],"genre_scores_gemma":[0.9606665,0.000031928666,0.02613836,0.00005377422,0.000008095297,0.0002848725,0.01183416,0.00024102506,0.0007411676],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99826473,0.00036484504,0.000143883,0.0003663628,0.00068103225,0.00017920691],"domain_scores_gemma":[0.99453896,0.0010367904,0.00049563433,0.0013730978,0.0023691878,0.0001863498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048555024,0.00082439336,0.0005288934,0.00092748215,0.0006993079,0.00084618945,0.001133277,0.0007160757,0.0010049763],"category_scores_gemma":[0.007260227,0.0003682925,0.00066738634,0.0012582996,0.00054116064,0.0010834546,0.00094700255,0.0005423855,0.00072208623],"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":[0.0032286376,0.0020835982,0.41997266,0.00035418652,0.00044172895,0.0006660774,0.0009129465,0.2578173,0.18162902,0.0016149763,0.009197022,0.12208194],"study_design_scores_gemma":[0.00046040077,0.0015795963,0.43879274,0.000054299584,0.00011063464,0.00034201326,0.00044156326,0.4035306,0.14616136,0.0006159428,0.007816058,0.000094671435],"about_ca_topic_score_codex":0.010958709,"about_ca_topic_score_gemma":0.011007387,"teacher_disagreement_score":0.010958709,"about_ca_system_score_codex":0.00066510245,"about_ca_system_score_gemma":0.0007347475,"threshold_uncertainty_score":0.025678635},"labels":[],"label_agreement":null},{"id":"W2588158318","doi":"10.1016/j.rse.2017.01.038","title":"Characterizing streams and riparian areas with airborne laser scanning data","year":2017,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Fondation du Risque","keywords":"Riparian zone; Environmental science; STREAMS; Remote sensing; Sinuosity; Canopy; Hydrology (agriculture); Ecotone; Geology; Geography; Habitat; Ecology; Computer science; Geomorphology","score_opus":0.022433244676094782,"score_gpt":0.240923480683133,"score_spread":0.2184902360070382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2588158318","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9574761,0.00021096466,0.022362635,0.000058161088,0.000009067822,0.00018865662,0.012191355,0.0006826942,0.0068204347],"genre_scores_gemma":[0.93207896,0.00023302803,0.056252006,0.000038527163,0.000016140048,0.00015261669,0.010068323,0.000033708435,0.0011266732],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997296,0.00003287621,0.000030598938,0.000054031174,0.000113448186,0.00003938153],"domain_scores_gemma":[0.99950016,0.00009236127,0.000101749814,0.00004746771,0.00021171727,0.000046500925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002773397,0.00019216666,0.0001573345,0.002409016,0.0002538353,0.0005835212,0.00026249772,0.00020803657,0.0013367586],"category_scores_gemma":[0.0007369161,0.000103097205,0.00019685007,0.0021480804,0.0001037305,0.00042642286,0.00040763296,0.00013554638,0.00066267943],"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":[0.0001178002,0.0001162964,0.7512694,0.0001282966,0.00007942571,0.0003337906,0.0004569736,0.017590836,0.019587688,0.00072967785,0.0033034387,0.20628628],"study_design_scores_gemma":[0.000023720328,0.000071916875,0.7930952,0.00006804162,0.000056249366,0.00029078056,0.0013172979,0.1864258,0.008343276,0.0012454197,0.009014576,0.000047747326],"about_ca_topic_score_codex":0.012644519,"about_ca_topic_score_gemma":0.036066774,"teacher_disagreement_score":0.012644519,"about_ca_system_score_codex":0.00022517682,"about_ca_system_score_gemma":0.00043034105,"threshold_uncertainty_score":0.025141835},"labels":[],"label_agreement":null},{"id":"W2591291746","doi":"10.1016/j.rse.2017.02.006","title":"Application of a Markov Chain Monte Carlo algorithm for snow water equivalent retrieval from passive microwave measurements","year":2017,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":60,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Space Agency; European Space Agency; China Scholarship Council; National Aeronautics and Space Administration","keywords":"Snow; Outlier; Markov chain Monte Carlo; Remote sensing; Algorithm; Microwave; Brightness temperature; Computer science; Monte Carlo method; Environmental science; Bayesian probability; Meteorology; Artificial intelligence; Mathematics; Statistics; Geology; Physics; Telecommunications","score_opus":0.03192171417351844,"score_gpt":0.22679553310305253,"score_spread":0.1948738189295341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2591291746","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01586975,0.00010101119,0.9823741,0.00013284724,0.00003842756,0.000057870027,0.000058279376,0.00049086567,0.00087683497],"genre_scores_gemma":[0.3460551,0.00021046864,0.6496802,0.00016743912,0.00009177217,0.00036184688,0.0005320631,0.0002600096,0.0026410883],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991905,0.00036255168,0.00005547305,0.00013721336,0.0001799445,0.00007418698],"domain_scores_gemma":[0.99245775,0.006114059,0.00023298,0.0002955678,0.00074419024,0.00015556028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027523849,0.0005836936,0.0012018747,0.0009008174,0.0012669933,0.0011047571,0.0017402861,0.0015130902,0.0035294052],"category_scores_gemma":[0.0083731655,0.0010442596,0.0009109691,0.0010604792,0.0009023959,0.0013357474,0.0012839969,0.0016273567,0.0006627912],"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":[0.00013372555,0.00006575197,0.00083899585,0.000033479533,0.00005260599,0.00004443141,0.000046812125,0.96093124,0.0007493615,0.00888153,0.0005407122,0.027681302],"study_design_scores_gemma":[0.000008741455,0.0000043980795,0.00003625199,0.0000019503484,0.0000026938355,0.0000048815077,0.0000015942222,0.9984205,0.00012951509,0.0013127236,0.000073407035,0.0000033761949],"about_ca_topic_score_codex":0.028464787,"about_ca_topic_score_gemma":0.02698093,"teacher_disagreement_score":0.028464787,"about_ca_system_score_codex":0.0011105298,"about_ca_system_score_gemma":0.0034544591,"threshold_uncertainty_score":0.056598246},"labels":[],"label_agreement":null},{"id":"W2594466018","doi":"10.1016/j.rse.2017.02.014","title":"Application of polarization signature to land cover scattering mechanism analysis and classification using multi-temporal C-band polarimetric RADARSAT-2 imagery","year":2017,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":77,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; Western University","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada; University of Electronic Science and Technology of China","keywords":"Scattering; Polarimetry; Remote sensing; Land cover; Random forest; Environmental science; Classifier (UML); Computer science; Physics; Geology; Optics; Artificial intelligence; Land use; Ecology","score_opus":0.013364954166101681,"score_gpt":0.2340664616417401,"score_spread":0.22070150747563844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2594466018","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78980887,0.00042828172,0.20352946,0.0002412796,0.00007866958,0.00006861846,0.0004989679,0.00049911067,0.0048467596],"genre_scores_gemma":[0.90671754,0.000364319,0.09123964,0.000045622186,0.000028213417,0.0000174151,0.00046922578,0.000039592738,0.0010784271],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9999262,0.000017129933,0.0000041677135,0.00001291901,0.000024230008,0.000015369455],"domain_scores_gemma":[0.999859,0.00003059114,0.000016876236,0.000016754506,0.000058855174,0.000017905362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025315757,0.00033507848,0.00014786271,0.0011284568,0.00017173031,0.0004891898,0.00016137691,0.00023434465,0.0004295513],"category_scores_gemma":[0.00031663338,0.0001521499,0.00028492702,0.00071397086,0.00014481902,0.0003576814,0.00023782116,0.00019343695,0.00019647973],"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":[0.00038207148,0.00033238853,0.04357585,0.00012472547,0.0001035877,0.00034872672,0.00016218814,0.036192246,0.47869864,0.0017125134,0.0014405956,0.4369265],"study_design_scores_gemma":[0.00004100409,0.00015896434,0.08088121,0.000014457374,0.00012497784,0.00044460705,0.00022859428,0.8061802,0.107572794,0.0016814986,0.0026258437,0.000045884666],"about_ca_topic_score_codex":0.0013649969,"about_ca_topic_score_gemma":0.0019182722,"teacher_disagreement_score":0.0013649969,"about_ca_system_score_codex":0.00011937891,"about_ca_system_score_gemma":0.00037008396,"threshold_uncertainty_score":0.0027140975},"labels":[],"label_agreement":null},{"id":"W2604571421","doi":"10.1016/j.rse.2017.03.027","title":"Validation of GlobSnow-2 snow water equivalent over Eastern Canada","year":2017,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":100,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Sherbrooke; Hydro-Québec; Center for Northern Studies","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Mitacs; Institut national de la recherche scientifique","keywords":"Water equivalent; Remote sensing; Snow; Environmental science; Geology; Geomorphology","score_opus":0.023603578570794433,"score_gpt":0.20960502811991025,"score_spread":0.18600144954911582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604571421","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98174244,0.00009461983,0.001961337,0.0000696342,0.000024770921,0.000049760103,0.011589057,0.0005353676,0.0039330255],"genre_scores_gemma":[0.97451425,0.00007582505,0.003220437,0.000050506183,0.000005504096,0.000033401117,0.020124236,0.000114277405,0.0018614995],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99961627,0.000023278202,0.000019231964,0.00009699465,0.00015036685,0.000093872644],"domain_scores_gemma":[0.99907696,0.00005393924,0.0000502022,0.00008341018,0.00064471446,0.00009069204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006655824,0.00059442525,0.00030936926,0.0008166891,0.0010592368,0.00091767096,0.00084513676,0.00039935487,0.0011530916],"category_scores_gemma":[0.0011920723,0.00019989793,0.00036618457,0.0010049308,0.00032775674,0.0006112698,0.00056119816,0.00029214832,0.0005065478],"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":[0.0011523376,0.00041435976,0.7632463,0.00018527267,0.0004874435,0.00044521558,0.00096747564,0.09619207,0.04240452,0.0012090206,0.012854054,0.080441974],"study_design_scores_gemma":[0.00018878323,0.000050522434,0.8407669,0.00005821744,0.0001089201,0.00007918666,0.0006718358,0.13367611,0.012315862,0.00020598796,0.011818959,0.00005864882],"about_ca_topic_score_codex":0.926358,"about_ca_topic_score_gemma":0.9593978,"teacher_disagreement_score":0.073642015,"about_ca_system_score_codex":0.0045240424,"about_ca_system_score_gemma":0.0070730452,"threshold_uncertainty_score":0.14815134},"labels":[],"label_agreement":null},{"id":"W2606223706","doi":"10.1016/j.rse.2017.03.045","title":"Multi-scale analysis of relationship between imperviousness and urban tree height using airborne remote sensing","year":2017,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Impervious surface; Remote sensing; Lidar; Environmental science; Scale (ratio); Land cover; Spatial ecology; Physical geography; Geography; Land use; Cartography; Ecology","score_opus":0.040832805154085584,"score_gpt":0.2639550823066847,"score_spread":0.2231222771525991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606223706","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.998307,0.000029512537,0.0012883818,0.000011024286,0.0000042874995,0.000002641593,0.00011188804,0.00002671904,0.00021863119],"genre_scores_gemma":[0.9987016,0.000011215297,0.0010797537,0.0000037513623,0.0000032037324,0.000002429731,0.00011994958,0.0000029700293,0.00007508387],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99989915,0.000018778266,0.0000053941453,0.000030479929,0.000024692228,0.000021581849],"domain_scores_gemma":[0.9996344,0.00016823113,0.00005775527,0.00003521846,0.0000746536,0.000029790943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026510237,0.00022429314,0.00021501705,0.0006637391,0.00023058872,0.00027849496,0.00021738808,0.00020590646,0.00052772433],"category_scores_gemma":[0.0004401189,0.0001460467,0.000349345,0.00061901956,0.00014243498,0.00034548022,0.00020045295,0.0001749871,0.00010654117],"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":[0.00042133135,0.00046623143,0.8253144,0.00008845003,0.0004138694,0.0002938001,0.0003277877,0.040604237,0.071218684,0.00038980847,0.0006876969,0.059773624],"study_design_scores_gemma":[0.000006292843,0.000036749276,0.92247534,0.0000024802698,0.000039316692,0.00005026989,0.00016861291,0.075972356,0.0010257958,0.00008911923,0.00012180935,0.000011822171],"about_ca_topic_score_codex":0.011857143,"about_ca_topic_score_gemma":0.022841169,"teacher_disagreement_score":0.011857143,"about_ca_system_score_codex":0.0001846523,"about_ca_system_score_gemma":0.00015770459,"threshold_uncertainty_score":0.02357626},"labels":[],"label_agreement":null},{"id":"W2606669148","doi":"10.1016/j.rse.2017.03.042","title":"Hyperspectral characterization of freezing injury and its biochemical impacts in oilseed rape leaves","year":2017,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":53,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada","funders":"National Natural Science Foundation of China","keywords":"Hyperspectral imaging; Principal component analysis; Environmental science; Biological system; Remote sensing; Partial least squares regression; Reflectivity; Mathematics; Horticulture; Statistics; Biology; Geology","score_opus":0.009189483641180815,"score_gpt":0.21871347666567106,"score_spread":0.20952399302449026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606669148","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9980478,0.0001782218,0.0009346788,0.000018921733,0.0000028931095,0.0000049337277,0.00018712145,0.000013530324,0.00061188085],"genre_scores_gemma":[0.9976392,0.00016904957,0.00073666027,0.000020519114,0.0000033253045,0.00000858503,0.00039470108,0.000006113838,0.0010218843],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.999969,0.000002282364,0.0000014086053,0.000008068696,0.000010753497,0.000008428258],"domain_scores_gemma":[0.9999634,0.000005609876,0.000010288642,0.0000026852563,0.0000106477355,0.0000074744617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000051855695,0.0001673536,0.000094384995,0.00021501459,0.0001318036,0.0001396996,0.00009507678,0.00013127754,0.0006303915],"category_scores_gemma":[0.000055618348,0.000076248485,0.00015525393,0.00016720654,0.000097451404,0.00014868869,0.000076773955,0.00024402377,0.00009452046],"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":[0.00007745587,0.0000093706285,0.00087049865,0.000009566699,0.0000036657152,0.00001717689,0.000015794812,0.000055907934,0.9979214,0.000011942317,0.000013663954,0.0009935849],"study_design_scores_gemma":[0.000004187682,0.00010758233,0.14493454,0.000004207293,0.000027719081,0.00019698153,0.00018290669,0.0023313165,0.8516968,0.00004895194,0.00045305255,0.0000117432],"about_ca_topic_score_codex":0.0029707104,"about_ca_topic_score_gemma":0.002855501,"teacher_disagreement_score":0.0029707104,"about_ca_system_score_codex":0.00013731091,"about_ca_system_score_gemma":0.000078016186,"threshold_uncertainty_score":0.0059068203},"labels":[],"label_agreement":null},{"id":"W2609894405","doi":"10.1016/j.rse.2017.04.016","title":"An improved scheme for rice phenology estimation based on time-series multispectral HJ-1A/B and polarimetric RADARSAT-2 data","year":2017,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":77,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada","funders":"Institute of Remote Sensing and Digital Earth; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Multispectral image; Remote sensing; Phenology; Synthetic aperture radar; Computer science; Polarimetry; Radar; Multispectral pattern recognition; Feature (linguistics); Geography","score_opus":0.017014141248461796,"score_gpt":0.2562862938706607,"score_spread":0.23927215262219892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2609894405","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11227517,0.0006218369,0.8826728,0.000115269635,0.00020919148,0.00013912104,0.0006787662,0.0018742087,0.0014136136],"genre_scores_gemma":[0.36167556,0.00027859505,0.6308983,0.00008702186,0.00012763955,0.00016320661,0.0019802968,0.00009604125,0.004693338],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972445,0.000039138937,0.000018040544,0.00009995203,0.00008069589,0.000037742528],"domain_scores_gemma":[0.9997689,0.000026565107,0.000021118416,0.000044339937,0.00011938286,0.00001963011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005244308,0.0007011189,0.000675943,0.000953727,0.00035930975,0.0004518233,0.00087637367,0.00070801843,0.0018340137],"category_scores_gemma":[0.0006729583,0.0003662942,0.00059557986,0.0010980493,0.0001498747,0.00088054966,0.0005928783,0.00051290257,0.000992078],"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":[0.0005389959,0.00022698245,0.008680563,0.00022871535,0.00021679273,0.00012913658,0.0001441656,0.07156,0.17363909,0.0013631661,0.0039248024,0.7393476],"study_design_scores_gemma":[0.00008517913,0.000096590404,0.014941445,0.000009424043,0.00010886642,0.000073110925,0.000027725575,0.9674973,0.013016136,0.00073141477,0.0033581704,0.00005456057],"about_ca_topic_score_codex":0.009065878,"about_ca_topic_score_gemma":0.014396563,"teacher_disagreement_score":0.009065878,"about_ca_system_score_codex":0.00027237943,"about_ca_system_score_gemma":0.00084899605,"threshold_uncertainty_score":0.018026173},"labels":[],"label_agreement":null},{"id":"W2610639429","doi":"10.1016/j.rse.2017.04.031","title":"A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems","year":2017,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":191,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Park Service; National Natural Science Foundation of China; Northeastern States Research Cooperative; U.S. Department of Energy; Office of Science; National Science Foundation","keywords":"Normalized Difference Vegetation Index; Snowmelt; Enhanced vegetation index; Environmental science; Phenology; Vegetation (pathology); Deciduous; Snow; Eddy covariance; Primary production; Ecosystem; Physical geography; Climatology; Atmospheric sciences; Remote sensing; Leaf area index; Vegetation Index; Ecology; Geography; Geology; Meteorology","score_opus":0.015038493146872221,"score_gpt":0.2361772372189685,"score_spread":0.22113874407209627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2610639429","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9866903,0.00033277142,0.010407565,0.000025507328,0.00003050304,0.00004557523,0.0014035448,0.00017800066,0.0008862372],"genre_scores_gemma":[0.9695967,0.00016307629,0.027757991,0.00002754932,0.000029879446,0.00006119917,0.0016913475,0.00003819956,0.0006341113],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998914,0.000025742444,0.000010332513,0.000027099095,0.000034494733,0.000010962355],"domain_scores_gemma":[0.9996669,0.00009844528,0.000064768334,0.000022128113,0.00009397531,0.000053712916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048782481,0.00028426712,0.00041593626,0.0014164575,0.00032559628,0.00046226147,0.00040035075,0.00029188106,0.0005392506],"category_scores_gemma":[0.00062970276,0.000210618,0.00020704951,0.0008884832,0.000087820066,0.0005144714,0.00023872007,0.00023476261,0.00014151615],"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":[0.0016366688,0.00057622144,0.6038039,0.0002739313,0.00036767867,0.0001708704,0.00030905477,0.009878966,0.19758242,0.0003395069,0.0020771883,0.18298364],"study_design_scores_gemma":[0.000067476336,0.0002449423,0.9012162,0.000016705213,0.00017569374,0.00023407598,0.000101650876,0.07855312,0.017509036,0.00019738733,0.001637225,0.00004660276],"about_ca_topic_score_codex":0.0063744914,"about_ca_topic_score_gemma":0.022471644,"teacher_disagreement_score":0.0063744914,"about_ca_system_score_codex":0.00033983952,"about_ca_system_score_gemma":0.00032490015,"threshold_uncertainty_score":0.012674749},"labels":[],"label_agreement":null},{"id":"W2734435928","doi":"10.1016/j.rse.2017.06.044","title":"Modeling and assessment of wavelength displacements of characteristic absorption features of common rock forming minerals encrusted by lichens","year":2017,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Geocenter Danmark","keywords":"Lichen; Geology; Absorption (acoustics); Remote sensing; Mineral; Wavelength; Environmental science; Mineralogy; Materials science; Optoelectronics; Ecology","score_opus":0.018318763530911907,"score_gpt":0.2582679744213187,"score_spread":0.2399492108904068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2734435928","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9914376,0.000034932807,0.0071845422,0.00002689436,0.0000035474648,0.00000710593,0.00005592542,0.000069362824,0.0011801231],"genre_scores_gemma":[0.9981602,0.000018725596,0.0014694595,0.000002233103,0.0000011408328,0.0000038455696,0.000031900614,0.000005299618,0.00030706226],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99996364,0.0000058488295,0.0000019858348,0.000011390676,0.000006456831,0.000010697509],"domain_scores_gemma":[0.999848,0.000077402525,0.000019893403,0.000012004108,0.000024542172,0.000018066483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013949527,0.00030909094,0.0001951061,0.0002614382,0.0003128785,0.00038342664,0.00049641577,0.0006408942,0.00059026835],"category_scores_gemma":[0.00037644978,0.00022593643,0.00040280705,0.0002523094,0.00024971156,0.00037351082,0.00021401177,0.00024544247,0.00007117697],"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":[0.000031472173,0.000042559597,0.007904216,0.000010009808,0.000010448972,0.000034242632,0.000024212568,0.9861866,0.0026816083,0.00015576996,0.000036188027,0.0028827726],"study_design_scores_gemma":[0.000002463832,0.000010703868,0.0022898335,7.074632e-7,0.0000036831236,0.000005434118,0.0000176141,0.99708503,0.0005114691,0.00004360958,0.000027000726,0.000002354184],"about_ca_topic_score_codex":0.044289395,"about_ca_topic_score_gemma":0.02485446,"teacher_disagreement_score":0.044289395,"about_ca_system_score_codex":0.0005227171,"about_ca_system_score_gemma":0.0006493485,"threshold_uncertainty_score":0.08806324},"labels":[],"label_agreement":null},{"id":"W2736287765","doi":"10.1016/j.rse.2017.07.005","title":"Estimation of monthly bulk nitrate deposition in China based on satellite NO2 measurement by the Ozone Monitoring Instrument","year":2017,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":52,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Deposition (geology); Environmental science; Satellite; Precipitation; Atmospheric sciences; Aerosol; Nitrate; Spatial variability; Ozone; Ozone Monitoring Instrument; Nitrogen; Remote sensing; Seasonality; Meteorology; Climatology; Geography; Geology; Chemistry; Structural basin","score_opus":0.01716012249348833,"score_gpt":0.2005506868403083,"score_spread":0.18339056434681997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2736287765","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99858105,0.00008496785,0.0005107322,0.000017334969,0.000006681441,0.000005337059,0.0004362878,0.00002216265,0.00033555014],"genre_scores_gemma":[0.99822086,0.000082623315,0.00043359053,0.000008265818,0.00000781287,0.000006502583,0.0009270261,0.000003879095,0.00030942445],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979717,0.000021114925,0.00002028721,0.00007898598,0.000052802894,0.000029693445],"domain_scores_gemma":[0.9997135,0.00005612463,0.000049418933,0.00002636184,0.00010099602,0.000053580858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005398757,0.00073616963,0.0005488848,0.0010636273,0.0003820356,0.00040268307,0.00049866515,0.00037866118,0.00037714664],"category_scores_gemma":[0.00037859476,0.00043035482,0.0006314008,0.00076062715,0.00022720642,0.0005984207,0.00033920008,0.00014286194,0.00009178525],"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":[0.00040438562,0.00014175032,0.9217109,0.00013792749,0.000489427,0.00042660115,0.00017364448,0.036744438,0.024629286,0.00021302512,0.00068862934,0.014240097],"study_design_scores_gemma":[0.000035708632,0.000036293834,0.9483398,0.0000040497907,0.0000984255,0.000030143616,0.000055924564,0.04967348,0.0014215738,0.000071673705,0.00021187305,0.000021037375],"about_ca_topic_score_codex":0.101279,"about_ca_topic_score_gemma":0.092587754,"teacher_disagreement_score":0.101279,"about_ca_system_score_codex":0.0010858316,"about_ca_system_score_gemma":0.0010618333,"threshold_uncertainty_score":0.20137894},"labels":[],"label_agreement":null},{"id":"W2757797739","doi":"10.1016/j.rse.2017.07.031","title":"Tracking crop phenological development using multi-temporal polarimetric Radarsat-2 data","year":2017,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":143,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nipissing University; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Canadian Space Agency; Northern Ontario Heritage Fund Corporation","keywords":"Phenology; Polarimetry; Remote sensing; Synthetic aperture radar; Normalized Difference Vegetation Index; Environmental science; Canola; Leaf area index; Vegetation (pathology); Agronomy; Geography; Biology; Physics","score_opus":0.0999140805174812,"score_gpt":0.2900416715286488,"score_spread":0.1901275910111676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2757797739","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98289293,0.00031121072,0.011424569,0.0000734436,0.00003133877,0.00002421003,0.0030151731,0.00037661998,0.0018504758],"genre_scores_gemma":[0.9774065,0.0002273227,0.017395841,0.000022988224,0.000015247183,0.000019916746,0.003939096,0.00002517624,0.00094799575],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99992347,0.000006103458,0.0000026738312,0.000033206954,0.00001919208,0.000015457965],"domain_scores_gemma":[0.9998398,0.000027260474,0.00003244772,0.000013751053,0.00006129712,0.000025517398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018213496,0.00032946916,0.00018764868,0.0013903282,0.00013695091,0.0003115147,0.0001791878,0.00025121865,0.0004153902],"category_scores_gemma":[0.00027481234,0.00014172845,0.000143479,0.0010507467,0.00006532221,0.00030224147,0.00019211115,0.000167386,0.00025857848],"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":[0.00054293533,0.0003382498,0.44670087,0.00023053901,0.00020044985,0.00034700806,0.00025479373,0.0850291,0.24900632,0.0006049556,0.0044345264,0.21231025],"study_design_scores_gemma":[0.000037789625,0.00010327026,0.6162723,0.000016568922,0.00008412924,0.00014663504,0.000116739306,0.36136264,0.018086215,0.0002568936,0.0034874845,0.000029447603],"about_ca_topic_score_codex":0.008526486,"about_ca_topic_score_gemma":0.021927465,"teacher_disagreement_score":0.008526486,"about_ca_system_score_codex":0.00022641581,"about_ca_system_score_gemma":0.00027098277,"threshold_uncertainty_score":0.016953707},"labels":[],"label_agreement":null},{"id":"W2759775556","doi":"10.1016/j.rse.2017.09.030","title":"A spectral mixture analysis approach to quantify Arctic first-year sea ice melt pond fraction using QuickBird and MODIS reflectance data","year":2017,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Killam Trusts; University of Manitoba; Johns Hopkins University","keywords":"Remote sensing; Sea ice; Environmental science; Arctic; Reflectivity; Geology; Climatology; Oceanography","score_opus":0.04141981313268978,"score_gpt":0.260989647085119,"score_spread":0.21956983395242924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2759775556","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34718657,0.00032956625,0.6486229,0.00010773672,0.000047263202,0.000070101676,0.00050175365,0.001414227,0.0017198976],"genre_scores_gemma":[0.7405592,0.00018701416,0.25394592,0.000047420053,0.00004620166,0.00011424861,0.0016302264,0.0002521653,0.0032176673],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9995975,0.00008451091,0.00003394866,0.00011734409,0.0001119119,0.00005477456],"domain_scores_gemma":[0.9994814,0.00018070964,0.000060444487,0.00007079391,0.00017533179,0.000031340605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014130938,0.0006783508,0.0005204523,0.0030319544,0.00075050956,0.00094820926,0.00059113343,0.00047336967,0.0010456702],"category_scores_gemma":[0.0014288035,0.00043735714,0.001529897,0.0012463968,0.0002747429,0.0011994055,0.0010440064,0.0004810331,0.00053506123],"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":[0.0016191697,0.0008260628,0.086186156,0.00014469518,0.001151616,0.000219386,0.00050976925,0.1748506,0.084906474,0.006286378,0.0023864452,0.6409132],"study_design_scores_gemma":[0.000012688041,0.000057724694,0.02529408,0.0000068837,0.00013363101,0.000067748944,0.00007157134,0.9648059,0.006569262,0.0018203168,0.0011241641,0.000036047688],"about_ca_topic_score_codex":0.0077483295,"about_ca_topic_score_gemma":0.010769657,"teacher_disagreement_score":0.0077483295,"about_ca_system_score_codex":0.0003637586,"about_ca_system_score_gemma":0.00058916275,"threshold_uncertainty_score":0.015406489},"labels":[],"label_agreement":null},{"id":"W2761672078","doi":"10.1016/j.rse.2017.07.036","title":"Reconstruction of Landsat time series in the presence of irregular and sparse observations: Development and assessment in north-eastern Alberta, Canada","year":2017,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Natural Resources Canada; Environment and Climate Change Canada","funders":"","keywords":"Remote sensing; Environmental science; Pixel; Time series; Series (stratigraphy); Multispectral image; Meteorology; Computer science; Statistics; Geography; Geology; Mathematics","score_opus":0.013873820812148626,"score_gpt":0.19756039495548897,"score_spread":0.18368657414334033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2761672078","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99094135,0.00040759565,0.0019781492,0.00019182856,0.000012784966,0.00005919065,0.0037631853,0.00010066941,0.0025452205],"genre_scores_gemma":[0.9881914,0.00038368843,0.005307398,0.000021741034,0.0000040333257,0.000015339503,0.0036923175,0.000025250967,0.0023588254],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956256,0.000044017354,0.000024020377,0.00006877201,0.00020680821,0.000093712595],"domain_scores_gemma":[0.9981818,0.00021080139,0.00012855466,0.000077398254,0.0012860673,0.00011541195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012362183,0.00047816258,0.00031198282,0.0015088158,0.0007983308,0.0017084081,0.0012426274,0.0004443723,0.0010264531],"category_scores_gemma":[0.002761168,0.00034529995,0.0003038467,0.002975266,0.00062463724,0.00047417425,0.00035536714,0.00036603602,0.00022054516],"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":[0.0011230045,0.00049982115,0.6055574,0.00029216657,0.00029155923,0.00070830004,0.0010944535,0.22867452,0.0071045584,0.0020937542,0.005788504,0.14677197],"study_design_scores_gemma":[0.000072164425,0.000041622494,0.7801154,0.00006169401,0.00010252916,0.00007615296,0.0012742885,0.21192253,0.0018888831,0.00019406065,0.004185356,0.00006525571],"about_ca_topic_score_codex":0.9916952,"about_ca_topic_score_gemma":0.9936,"teacher_disagreement_score":0.014725272,"about_ca_system_score_codex":0.014725272,"about_ca_system_score_gemma":0.01786861,"threshold_uncertainty_score":0.106839776},"labels":[],"label_agreement":null},{"id":"W2762180336","doi":"10.1016/j.rse.2017.08.025","title":"Development and assessment of the SMAP enhanced passive soil moisture product","year":2017,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":486,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Guelph","funders":"California Institute of Technology; Jet Propulsion Laboratory; National Aeronautics and Space Administration","keywords":"Environmental science; Radiometer; Remote sensing; Radar; Brightness temperature; Meteorology; Water content; Calibration; CubeSat; Moisture; Satellite; Computer science; Geology; Microwave; Engineering; Geography","score_opus":0.012586945760471138,"score_gpt":0.2394348902765506,"score_spread":0.22684794451607945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2762180336","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72514874,0.00053153175,0.22984909,0.0005512995,0.0001232635,0.00091841025,0.017841024,0.0042098784,0.020826785],"genre_scores_gemma":[0.73294586,0.00027882922,0.24753469,0.00009432487,0.000035074507,0.000319937,0.014660939,0.00030279186,0.0038276056],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999603,0.00004712751,0.0000125952865,0.00006222594,0.0002495419,0.000025545367],"domain_scores_gemma":[0.9992778,0.000083271385,0.00006305335,0.0000954154,0.0004158022,0.00006470201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014422656,0.00042588744,0.00024759144,0.0004797635,0.00019603773,0.000515345,0.0008061397,0.00051337923,0.0013514536],"category_scores_gemma":[0.0013054824,0.00027220987,0.00023085004,0.00042669568,0.0001753121,0.0011408216,0.0005513986,0.0004039663,0.00083645893],"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":[0.0010372184,0.0011564917,0.09548253,0.00050859374,0.00016955603,0.00045717217,0.0002609389,0.12164712,0.2912244,0.004849699,0.011935622,0.4712706],"study_design_scores_gemma":[0.0003018444,0.0015965598,0.1466062,0.00005898824,0.0001445884,0.0003291088,0.00012358955,0.6436653,0.1663859,0.0011689937,0.03953326,0.000085688516],"about_ca_topic_score_codex":0.0037794327,"about_ca_topic_score_gemma":0.00649859,"teacher_disagreement_score":0.0037794327,"about_ca_system_score_codex":0.00033949237,"about_ca_system_score_gemma":0.00086932105,"threshold_uncertainty_score":0.007627547},"labels":[],"label_agreement":null},{"id":"W2767689241","doi":"10.1016/j.rse.2017.10.045","title":"Estimating surface soil moisture from SMAP observations using a Neural Network technique","year":2017,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":137,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"National Aeronautics and Space Administration Postdoctoral Program; Canadian Space Agency; Universities Space Research Association; Environment and Climate Change Canada; National Aeronautics and Space Administration","keywords":"Water content; Radiometer; Environmental science; Remote sensing; Scatterometer; Satellite; Moderate-resolution imaging spectroradiometer; Anomaly (physics); Spectroradiometer; Vegetation (pathology); Meteorology; Wind speed; Geology; Reflectivity","score_opus":0.030748483240712932,"score_gpt":0.2466015338181902,"score_spread":0.21585305057747725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767689241","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49286333,0.00026923415,0.5031451,0.00011117565,0.00005625249,0.000037526206,0.0003329203,0.00093989953,0.0022446376],"genre_scores_gemma":[0.9350846,0.00009946463,0.06364319,0.000015608866,0.000023885446,0.000025079213,0.00016734701,0.000023819297,0.0009170441],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999931,0.000011505296,0.000004423272,0.000023635946,0.000020467753,0.00000900774],"domain_scores_gemma":[0.9998042,0.00009395416,0.00002625877,0.000015952757,0.000051810664,0.000007885447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022109172,0.00037785486,0.00026139212,0.0004970611,0.00019932352,0.000260489,0.00031349002,0.0004428698,0.00063190015],"category_scores_gemma":[0.0009948204,0.00029427034,0.00022076059,0.00055331236,0.00012722088,0.0006895412,0.00023620942,0.00044588392,0.00021481869],"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":[0.00023407553,0.00015025718,0.028796878,0.00008922187,0.000118608885,0.00014230514,0.00006159119,0.61453265,0.06766737,0.0010042756,0.00077919575,0.28642362],"study_design_scores_gemma":[0.0000043171135,0.000009963418,0.0056269947,0.0000021436558,0.000006841797,0.000010532273,0.0000039958804,0.99146676,0.0025171728,0.0002435392,0.00010294255,0.000004799705],"about_ca_topic_score_codex":0.0070554325,"about_ca_topic_score_gemma":0.011435188,"teacher_disagreement_score":0.0070554325,"about_ca_system_score_codex":0.00022393737,"about_ca_system_score_gemma":0.00024961776,"threshold_uncertainty_score":0.014028728},"labels":[],"label_agreement":null},{"id":"W2772509791","doi":"10.1016/j.rse.2017.12.007","title":"Validation of the SMAP freeze/thaw product using categorical triple collocation","year":2017,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Center for Northern Studies; University of British Columbia; Université de Sherbrooke; University of Guelph; Environment and Climate Change Canada","funders":"National Key Research and Development Program of China; Center for the Environment, Harvard University; National Natural Science Foundation of China; Canadian Space Agency; Tsinghua National Laboratory for Information Science and Technology; National Science Foundation","keywords":"Representativeness heuristic; Environmental science; Satellite; Categorical variable; Remote sensing; Scale (ratio); Collocation (remote sensing); Latitude; Meteorology; Product (mathematics); Temporal resolution; Climatology; Atmospheric sciences; Statistics; Mathematics; Geography; Cartography; Geodesy; Geology","score_opus":0.022934669843291566,"score_gpt":0.2417241550974193,"score_spread":0.21878948525412772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772509791","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84581465,0.00018452064,0.13251667,0.00013441547,0.00020964035,0.00016982004,0.009823872,0.005760703,0.005385715],"genre_scores_gemma":[0.8829089,0.000063783395,0.10624389,0.000073477415,0.00003225481,0.00014229659,0.008401638,0.000494566,0.001639237],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994254,0.0000907749,0.000033052416,0.00016026123,0.00024297953,0.000047630347],"domain_scores_gemma":[0.99784505,0.00040002223,0.00018312299,0.0004908296,0.001007385,0.00007357997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013351789,0.0005234388,0.00031413414,0.0008580004,0.00046360676,0.00064647646,0.00077935937,0.00061935233,0.0029132932],"category_scores_gemma":[0.0037825687,0.00022033793,0.00040264908,0.00058000657,0.00027341757,0.00079070526,0.0006986074,0.00050321635,0.001660808],"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":[0.0022721814,0.00062377757,0.12566756,0.0005259928,0.00035849243,0.00043213705,0.00060810754,0.1241396,0.30502594,0.0025295408,0.01587296,0.42194375],"study_design_scores_gemma":[0.00016836471,0.0003332366,0.18622153,0.000059797276,0.000081173035,0.0002575092,0.00024388319,0.7193331,0.08364106,0.0011040812,0.008462217,0.000094139934],"about_ca_topic_score_codex":0.0063807885,"about_ca_topic_score_gemma":0.008011975,"teacher_disagreement_score":0.0063807885,"about_ca_system_score_codex":0.0002484472,"about_ca_system_score_gemma":0.00043060287,"threshold_uncertainty_score":0.0126873255},"labels":[],"label_agreement":null},{"id":"W2779279532","doi":"10.1016/j.rse.2017.12.014","title":"Remote sensing of biodiversity: Soil correction and data dimension reduction methods improve assessment of α-diversity (species richness) in prairie ecosystems","year":2017,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":127,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures; National Aeronautics and Space Administration; National Science Foundation","keywords":"Species richness; Biodiversity; Remote sensing; Environmental science; Ecosystem; Species diversity; Diversity (politics); Geography; Ecology; Biology","score_opus":0.04851037737795735,"score_gpt":0.29435717260143845,"score_spread":0.2458467952234811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2779279532","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.967261,0.00022492062,0.030715395,0.00012779172,0.0000171587,0.000032769338,0.0003532727,0.00021816735,0.0010494757],"genre_scores_gemma":[0.93766445,0.0001794673,0.060730528,0.000049490947,0.000016721277,0.000031578347,0.00056891114,0.000018476174,0.0007402889],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99965525,0.00009736916,0.000022382754,0.00009512485,0.00010388752,0.00002595643],"domain_scores_gemma":[0.99940276,0.00023770728,0.000097650656,0.00007466529,0.00015246746,0.000034700864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011326068,0.00028218064,0.00019236428,0.0011642606,0.00027312915,0.00055448257,0.00041723147,0.0002608628,0.00075484545],"category_scores_gemma":[0.0021441379,0.00025048558,0.0002728129,0.00080263254,0.0002510532,0.00076485344,0.00054638117,0.00029829555,0.00018221208],"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":[0.0004990531,0.00038346942,0.35721222,0.0002724571,0.0003419699,0.00008126441,0.00058106164,0.05181287,0.091949716,0.0009726627,0.0010813458,0.49481192],"study_design_scores_gemma":[0.00009558883,0.0002461358,0.64361274,0.000038693488,0.00020951043,0.00017720452,0.00036507938,0.3342914,0.017510563,0.0010469051,0.002350902,0.000055199456],"about_ca_topic_score_codex":0.013021728,"about_ca_topic_score_gemma":0.026064413,"teacher_disagreement_score":0.013021728,"about_ca_system_score_codex":0.00027178277,"about_ca_system_score_gemma":0.00039519902,"threshold_uncertainty_score":0.0258919},"labels":[],"label_agreement":null},{"id":"W2779835965","doi":"10.1016/j.rse.2017.12.027","title":"Predicting the minimum height of forest fire smoke within the atmosphere using machine learning and data from the CALIPSO satellite","year":2017,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":89,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Centre for Disease Control; University of British Columbia","funders":"Australian Research Council; British Columbia Lung Association","keywords":"Smoke; Environmental science; Remote sensing; Satellite; Meteorology; Population; Atmosphere (unit); Geography; Environmental health","score_opus":0.025373033175745657,"score_gpt":0.24446978022699736,"score_spread":0.2190967470512517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2779835965","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9971353,0.00006938961,0.0018918149,0.000046486388,0.000012753691,0.0000032968483,0.00043334355,0.000069543494,0.00033801995],"genre_scores_gemma":[0.99738044,0.000027894206,0.0016192677,0.0000075864277,0.000009058997,0.000002057595,0.0008533979,0.0000070013457,0.00009316397],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99992955,0.000006552111,0.000004141644,0.000024325456,0.000015208668,0.000020092488],"domain_scores_gemma":[0.9996724,0.00016115862,0.000035252633,0.000024097613,0.000063745756,0.00004332564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024055157,0.00033385755,0.00025766963,0.000695038,0.00023356658,0.0003483905,0.00030265813,0.00056550204,0.00035997987],"category_scores_gemma":[0.00069608964,0.00020885143,0.00039310128,0.00042385363,0.00015190421,0.0003980457,0.00017209665,0.00050126744,0.00015096678],"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":[0.0006115005,0.0004233285,0.61319685,0.000075874166,0.00019006214,0.00021236557,0.00008906827,0.30105457,0.027138693,0.00030048407,0.0017188911,0.054988362],"study_design_scores_gemma":[0.000024011075,0.00004353753,0.27778304,0.0000071821473,0.000033437267,0.000043208645,0.000060382088,0.71720773,0.00435361,0.00023234489,0.00019701825,0.000014475554],"about_ca_topic_score_codex":0.018693909,"about_ca_topic_score_gemma":0.030812558,"teacher_disagreement_score":0.018693909,"about_ca_system_score_codex":0.0003493465,"about_ca_system_score_gemma":0.00035112028,"threshold_uncertainty_score":0.03717023},"labels":[],"label_agreement":null},{"id":"W2780821100","doi":"10.1016/j.rse.2017.12.011","title":"Quantifying the relative contributions of vegetation and soil moisture conditions to polarimetric C-Band SAR response in a temperate peatland","year":2017,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":76,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Science and Engineering Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Environmental science; Vegetation (pathology); Water content; Synthetic aperture radar; Remote sensing; Soil science; Peat; Moisture; Backscatter (email); Geology; Meteorology; Ecology","score_opus":0.01731478456176441,"score_gpt":0.2708366530921596,"score_spread":0.2535218685303952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2780821100","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9996556,0.000009015123,0.00020378231,0.0000028532634,4.3232828e-7,0.0000011249217,0.000016035998,0.0000037901968,0.00010720227],"genre_scores_gemma":[0.99965835,0.0000126723735,0.00021289219,0.000003082234,7.936543e-7,0.0000013203858,0.000030705993,0.000002147861,0.00007804718],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9999522,0.000008550869,0.000002510235,0.0000153521,0.000007357502,0.000014022213],"domain_scores_gemma":[0.99989223,0.000051139767,0.000011799323,0.0000072524344,0.00001984846,0.000017683578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019533264,0.00021017187,0.00013144188,0.0002856004,0.00016342044,0.00030978452,0.000116985146,0.00020127323,0.00037804584],"category_scores_gemma":[0.00031005635,0.00013498157,0.000117811294,0.00017915262,0.00021442988,0.00022674786,0.00015436558,0.00009458809,0.00006475712],"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":[0.0009707663,0.00022326081,0.32264367,0.000083658786,0.00009458308,0.00030642905,0.00035126918,0.02439773,0.6284954,0.00030064146,0.00012322767,0.022009367],"study_design_scores_gemma":[0.000017274064,0.000089419336,0.916696,0.000004788374,0.000047889796,0.00011249086,0.0002893478,0.06608902,0.016371602,0.00012868734,0.00013790625,0.00001556466],"about_ca_topic_score_codex":0.013510664,"about_ca_topic_score_gemma":0.023451243,"teacher_disagreement_score":0.013510664,"about_ca_system_score_codex":0.00020611074,"about_ca_system_score_gemma":0.00022766592,"threshold_uncertainty_score":0.026864052},"labels":[],"label_agreement":null},{"id":"W2782438771","doi":"10.1016/j.rse.2017.11.005","title":"Fisher Linear Discriminant Analysis of coherency matrix for wetland classification using PolSAR imagery","year":2018,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":86,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"Directorate of Environment and Climate Change, India; Research and Development Corporation of Newfoundland and Labrador; Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Remote sensing; Land cover; Computer science; Polarimetry; Weighting; Linear discriminant analysis; Pattern recognition (psychology); Contextual image classification; Artificial intelligence; Satellite imagery; Feature (linguistics); Land use; Geography; Image (mathematics)","score_opus":0.026240133202554965,"score_gpt":0.27456027569513497,"score_spread":0.24832014249258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782438771","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30156338,0.0006542489,0.6924067,0.00022571684,0.00007636342,0.000066855086,0.0005696877,0.0010431653,0.0033937716],"genre_scores_gemma":[0.8258946,0.0003503428,0.16867691,0.00004622759,0.000042715055,0.000072246796,0.0013065105,0.00010447684,0.0035059813],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998148,0.000041154814,0.000012592706,0.000043143325,0.00005966671,0.00002861568],"domain_scores_gemma":[0.99965227,0.000113683636,0.00003117853,0.000029472347,0.00015550689,0.000017810251],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046009687,0.00036241225,0.0003222512,0.001086704,0.00026486994,0.00032700173,0.00025985597,0.00018087125,0.0016129875],"category_scores_gemma":[0.0010401674,0.00012242299,0.00039938648,0.0006228874,0.00016140907,0.00045445497,0.00029935935,0.0003092808,0.0004647246],"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":[0.0004966218,0.00015300844,0.006313216,0.00013621488,0.00006031514,0.000070747046,0.00012784188,0.022415804,0.08749138,0.003568808,0.004887164,0.8742789],"study_design_scores_gemma":[0.000027283453,0.00012238516,0.022868177,0.00001921587,0.000071686125,0.00010186709,0.00015999067,0.95101935,0.020603344,0.0019391244,0.003035273,0.00003240021],"about_ca_topic_score_codex":0.0028073993,"about_ca_topic_score_gemma":0.003693216,"teacher_disagreement_score":0.0028073993,"about_ca_system_score_codex":0.00016492132,"about_ca_system_score_gemma":0.0005149694,"threshold_uncertainty_score":0.005582094},"labels":[],"label_agreement":null},{"id":"W2782454659","doi":"10.1016/j.rse.2017.12.005","title":"Selection of HyspIRI optimal band positions for the earth compositional mapping using HyTES data","year":2018,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Jet Propulsion Laboratory; National Aeronautics and Space Administration","keywords":"Hyperspectral imaging; Remote sensing; Spectral bands; Multispectral image; Emissivity; Earth observation; VNIR; Infrared; Imaging spectrometer; Calibration; Spectral signature; Environmental science; Spectrometer; Satellite; Optics; Geology; Physics; Astronomy","score_opus":0.050507993644296235,"score_gpt":0.25611923500699096,"score_spread":0.20561124136269474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782454659","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77163815,0.0005155886,0.21509455,0.00022578142,0.0000891762,0.00012936808,0.00265857,0.002238208,0.0074106366],"genre_scores_gemma":[0.6818747,0.00021371763,0.3110685,0.000042971584,0.000020198911,0.00010869257,0.0039570243,0.00035400782,0.0023602191],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998671,0.000023106084,0.0000058861287,0.00003533507,0.000029745976,0.00003881294],"domain_scores_gemma":[0.99984133,0.000018566845,0.000012260109,0.00001914477,0.00008841399,0.000020190711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039963517,0.00059268594,0.00038565954,0.0011868208,0.00045513993,0.0006324563,0.00041198294,0.0005493061,0.0025722182],"category_scores_gemma":[0.00074336346,0.00030699786,0.00040087564,0.0007620495,0.00017380297,0.000418271,0.00027300548,0.00044638535,0.0013579444],"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":[0.0019409456,0.0007196775,0.040026806,0.00028506227,0.00014127887,0.00042151657,0.00042779822,0.058844816,0.40504128,0.0034542393,0.011025743,0.4776708],"study_design_scores_gemma":[0.00032025576,0.0001919517,0.09055595,0.00006207435,0.00025112173,0.00017299865,0.00060194166,0.70233625,0.19208747,0.0028596562,0.010463056,0.000097257005],"about_ca_topic_score_codex":0.0050860443,"about_ca_topic_score_gemma":0.0091362195,"teacher_disagreement_score":0.0050860443,"about_ca_system_score_codex":0.00017489537,"about_ca_system_score_gemma":0.00090160477,"threshold_uncertainty_score":0.010112882},"labels":[],"label_agreement":null},{"id":"W2789438068","doi":"10.1016/j.rse.2018.03.018","title":"Deblurring DMSP nighttime lights: A new method using Gaussian filters and frequencies of illumination","year":2018,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Impact of Light on Environment and Health","field":"Environmental Science","cited_by":97,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"TRIUMF","funders":"National Oceanic and Atmospheric Administration; World Bank Group","keywords":"Defense Meteorological Satellite Program; Computer science; Brightness; Remote sensing; Deblurring; Satellite; Pixel; Sample (material); Filter (signal processing); Point spread function; Artificial intelligence; Computer vision; Computer graphics (images); Meteorology; Geography; Optics; Image processing; Image restoration; Image (mathematics); Physics","score_opus":0.024813913298786544,"score_gpt":0.2836102357359637,"score_spread":0.2587963224371772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789438068","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008413476,0.0002188419,0.9897261,0.000072283496,0.000044772532,0.00003339339,0.00007653759,0.00062816474,0.0007862933],"genre_scores_gemma":[0.03689669,0.00042251163,0.95911896,0.00007683707,0.000043109085,0.00007059948,0.00018259588,0.0001932124,0.0029954275],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995733,0.00008122798,0.000020484018,0.000102466096,0.00018222032,0.000040349598],"domain_scores_gemma":[0.9994424,0.00016401538,0.000056245757,0.00010176665,0.00019460171,0.000040970426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065082643,0.0010334448,0.00059031555,0.0011501384,0.0005031737,0.0008342771,0.0011642682,0.0011387027,0.0022172534],"category_scores_gemma":[0.0012758431,0.0005132403,0.00080938294,0.0011437088,0.0005769809,0.0014286402,0.0010237844,0.0009320239,0.0014404448],"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":[0.00040337662,0.0001802818,0.002540482,0.0003556693,0.00018180237,0.00015937726,0.00027483088,0.01818559,0.3504985,0.009545684,0.0029446566,0.6147297],"study_design_scores_gemma":[0.000086638196,0.0001684718,0.005910167,0.000041182408,0.00021547212,0.00078568474,0.00014381149,0.7557411,0.20371026,0.004223959,0.028750189,0.00022306354],"about_ca_topic_score_codex":0.004521041,"about_ca_topic_score_gemma":0.0102860145,"teacher_disagreement_score":0.004521041,"about_ca_system_score_codex":0.00051969656,"about_ca_system_score_gemma":0.0010857509,"threshold_uncertainty_score":0.008989453},"labels":[],"label_agreement":null},{"id":"W2789492941","doi":"10.1016/j.rse.2017.12.006","title":"A mathematical framework to describe the effect of beam incidence angle on metrics derived from airborne LiDAR: The case of forest canopies approaching turbid medium behaviour","year":2018,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lidar; Remote sensing; Canopy; Environmental science; Nadir; Point cloud; Tree canopy; Point (geometry); Mathematics; Geology; Computer science; Geography; Geometry; Physics","score_opus":0.013460515567182576,"score_gpt":0.24368012669894837,"score_spread":0.23021961113176578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789492941","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009524088,0.00074947474,0.98222095,0.00045504703,0.000105894316,0.000029828469,0.00009985223,0.00006257204,0.0067523206],"genre_scores_gemma":[0.66122615,0.005041497,0.30834958,0.000814959,0.00071319717,0.00044350154,0.000419175,0.00044055365,0.022551386],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993,0.00022876688,0.000048000547,0.0001240195,0.00021421333,0.00008505514],"domain_scores_gemma":[0.99622846,0.0020998907,0.00061649445,0.00022374642,0.0007194149,0.00011198973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002735148,0.0013170701,0.00083998725,0.0018397719,0.00084676576,0.0019282823,0.0018976732,0.0018562035,0.002041927],"category_scores_gemma":[0.008967206,0.0006104117,0.0012187394,0.0010239717,0.0016973909,0.0030361086,0.0017579023,0.0025561776,0.0006660021],"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":[0.000012597813,0.00007112774,0.0012250378,0.00018343148,0.000052606305,0.0003328885,0.0002641547,0.46935856,0.009496744,0.50533134,0.0024527395,0.01121878],"study_design_scores_gemma":[0.0000020207713,0.000026300804,0.0004875957,0.000019576139,0.000014262903,0.00013882402,0.000037294594,0.9501303,0.0004380141,0.04720349,0.0014744673,0.00002783623],"about_ca_topic_score_codex":0.00624052,"about_ca_topic_score_gemma":0.005452801,"teacher_disagreement_score":0.00624052,"about_ca_system_score_codex":0.0010674024,"about_ca_system_score_gemma":0.0011159765,"threshold_uncertainty_score":0.014465034},"labels":[],"label_agreement":null},{"id":"W2790602384","doi":"10.1016/j.rse.2017.12.020","title":"Large-area mapping of Canadian boreal forest cover, height, biomass and other structural attributes using Landsat composites and lidar plots","year":2018,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":289,"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 Forest Service; Natural Resources Canada; Canadian Space Agency; Université de Lausanne; Compute Canada","keywords":"Lidar; Remote sensing; Thematic Mapper; Environmental science; Basal area; Elevation (ballistics); Satellite imagery; Geography; Forestry","score_opus":0.02068396238971561,"score_gpt":0.22074584927787666,"score_spread":0.20006188688816104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790602384","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9617858,0.0008070092,0.02146803,0.00019853069,0.000015887066,0.00013588286,0.009070541,0.000634053,0.005884138],"genre_scores_gemma":[0.97812724,0.00045599884,0.015007491,0.00003034075,0.0000049189857,0.000043153756,0.0050380933,0.00003399403,0.0012587345],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99965703,0.000025101535,0.000011509441,0.00009355205,0.00015829189,0.000054504664],"domain_scores_gemma":[0.9993523,0.00007091362,0.00010362641,0.000036476267,0.00039293812,0.000043846136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006251708,0.00064721075,0.000229321,0.0016105397,0.0011828121,0.0008768979,0.0006975932,0.00017527866,0.00075456634],"category_scores_gemma":[0.0012731168,0.00020626133,0.0005507998,0.0029615515,0.00036044177,0.00035383995,0.00047319828,0.000295987,0.00018445309],"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":[0.000110404224,0.00018528398,0.6531368,0.00023946312,0.000261719,0.00019181492,0.0006651036,0.13535221,0.006945159,0.00142285,0.0054588113,0.19603035],"study_design_scores_gemma":[0.000018177936,0.000032799955,0.83804536,0.00005106634,0.000078726305,0.000078968355,0.00060996803,0.1526901,0.001736639,0.0004282192,0.006176739,0.00005322846],"about_ca_topic_score_codex":0.9846229,"about_ca_topic_score_gemma":0.9932794,"teacher_disagreement_score":0.015377104,"about_ca_system_score_codex":0.0101076765,"about_ca_system_score_gemma":0.010845143,"threshold_uncertainty_score":0.07333672},"labels":[],"label_agreement":null},{"id":"W2792077377","doi":"10.1016/j.rse.2018.02.041","title":"An algorithm for the retrieval of the clumping index (CI) from the MODIS BRDF product using an adjusted version of the kernel-driven BRDF model","year":2018,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":116,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Key Clinical Specialty Discipline Construction Program of China; National Natural Science Foundation of China","keywords":"Bidirectional reflectance distribution function; Hotspot (geology); Algorithm; Computer science; Remote sensing; Moderate-resolution imaging spectroradiometer; Pathfinder; Outlier; Geology; Reflectivity; Artificial intelligence; Physics; Satellite","score_opus":0.022425566461276726,"score_gpt":0.23540022081016682,"score_spread":0.2129746543488901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792077377","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002410134,0.000057829537,0.9961964,0.000029858735,0.000046290636,0.000046999077,0.000055249307,0.0009735692,0.00018373386],"genre_scores_gemma":[0.023277404,0.00007003993,0.9747444,0.000039060273,0.000024595287,0.00014213532,0.0003965216,0.00024241388,0.0010635129],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995105,0.000057900976,0.00003741364,0.00013233937,0.00021701616,0.000044819622],"domain_scores_gemma":[0.9992512,0.0001601572,0.000054617536,0.00013595374,0.00036448814,0.000033477514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009481103,0.0009741315,0.000911599,0.000950313,0.00073724805,0.001261029,0.0028088547,0.0012736032,0.003334103],"category_scores_gemma":[0.0028312437,0.00067728874,0.0010506355,0.0012793922,0.00035939866,0.0012627286,0.0013411038,0.001979859,0.0026373772],"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":[0.0002770221,0.0002104344,0.002177396,0.00014032653,0.00020943276,0.00012589974,0.0001085774,0.11915201,0.06402001,0.011912352,0.007698571,0.79396796],"study_design_scores_gemma":[0.00004764653,0.000038868497,0.001300847,0.000007715499,0.000033710137,0.00011230317,0.000015561973,0.97565496,0.012631771,0.004499036,0.005603131,0.000054540807],"about_ca_topic_score_codex":0.009439512,"about_ca_topic_score_gemma":0.010975792,"teacher_disagreement_score":0.009439512,"about_ca_system_score_codex":0.0008397616,"about_ca_system_score_gemma":0.0017033791,"threshold_uncertainty_score":0.018769085},"labels":[],"label_agreement":null},{"id":"W2794046722","doi":"10.1016/j.rse.2018.02.003","title":"Assessment of offshore oil/gas platform status in the northern Gulf of Mexico using multi-source satellite time-series images","year":2018,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Oil Spill Detection and Mitigation","field":"Environmental Science","cited_by":45,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Farm Service Agency; Key Technologies Research and Development Program; Institute of Infection and Immunity; Bureau of Safety and Environmental Enforcement; Natural Science Foundation of Jiangsu Province; Gulf of Mexico Research Initiative","keywords":"Geocoding; Satellite; Computer science; Remote sensing; Offshore oil and gas; Submarine pipeline; Environmental science; Geolocation; Geology","score_opus":0.015358612843715475,"score_gpt":0.24701432177455676,"score_spread":0.23165570893084128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794046722","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9983285,0.000054459746,0.00017920298,0.000033865945,0.0000032157202,0.000006988253,0.0006726832,0.000015117864,0.0007059966],"genre_scores_gemma":[0.9974468,0.00012952606,0.0007172861,0.0000068230083,0.0000036122035,0.000008725554,0.0010798827,0.0000023739142,0.0006051415],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999367,0.0000044444137,0.0000057464936,0.000017882727,0.000016665816,0.000018513485],"domain_scores_gemma":[0.99979776,0.00001806337,0.000067220346,0.000008272404,0.000084032414,0.00002465147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001859167,0.00025757038,0.00012203464,0.0011565109,0.0002830334,0.0004952845,0.00017221272,0.00031037576,0.00054935506],"category_scores_gemma":[0.00042659402,0.00012348179,0.00015491544,0.0008056163,0.00013412745,0.00031910563,0.00030261255,0.00012545733,0.00008950189],"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":[0.00026504888,0.000089981615,0.9492668,0.00005945584,0.00010110635,0.00035567637,0.0003222817,0.008446834,0.010914625,0.00016702081,0.0009997047,0.02901141],"study_design_scores_gemma":[0.000008781373,0.000020784291,0.9908317,0.000015062934,0.000038149057,0.000026200541,0.0005691834,0.006765311,0.0009413643,0.000016495347,0.00076045905,0.0000065233676],"about_ca_topic_score_codex":0.1875726,"about_ca_topic_score_gemma":0.27376208,"teacher_disagreement_score":0.1875726,"about_ca_system_score_codex":0.00064971804,"about_ca_system_score_gemma":0.0005543235,"threshold_uncertainty_score":0.37296158},"labels":[],"label_agreement":null},{"id":"W2796208506","doi":"10.1016/j.rse.2018.02.071","title":"A continent-wide search for Antarctic petrel breeding sites with satellite remote sensing","year":2018,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Avian ecology and behavior","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Petrel; Population; Satellite; Geography; Seabird; Ecology; Oceanography; Biology; Geology; Predation","score_opus":0.021236903162416748,"score_gpt":0.2443474435447327,"score_spread":0.22311054038231595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2796208506","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99058855,0.00026973142,0.00083266804,0.0002973215,0.000017978398,0.000115645074,0.0035675364,0.00006553636,0.0042451797],"genre_scores_gemma":[0.9875969,0.00021897045,0.0071123545,0.00014362735,0.000019015568,0.000062713,0.0030901763,0.0000080754835,0.0017480971],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998733,0.0000311834,0.0000065996846,0.000026657523,0.000034054545,0.000028222545],"domain_scores_gemma":[0.9996488,0.00007633001,0.000062857514,0.000052504445,0.00008125001,0.000078326055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038188978,0.000207805,0.00017633945,0.0016890093,0.0006836242,0.0005006042,0.0003711727,0.00042314653,0.0028679234],"category_scores_gemma":[0.00080062775,0.00014051898,0.00029738716,0.0018634559,0.0001608779,0.00033375347,0.0005955881,0.00018424954,0.00041555575],"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":[0.00047577798,0.00021449146,0.8748432,0.00018895726,0.00032510716,0.0011318411,0.0009878577,0.0048430483,0.016397607,0.00031653955,0.00567483,0.09460083],"study_design_scores_gemma":[0.00006842964,0.0002675993,0.9783977,0.000047448717,0.00018072431,0.00031033714,0.0029178776,0.0081227,0.0015424632,0.00018992959,0.007932752,0.00002209156],"about_ca_topic_score_codex":0.049291346,"about_ca_topic_score_gemma":0.11930826,"teacher_disagreement_score":0.049291346,"about_ca_system_score_codex":0.00030361765,"about_ca_system_score_gemma":0.0012520184,"threshold_uncertainty_score":0.09800887},"labels":[],"label_agreement":null},{"id":"W2797957951","doi":"10.1016/j.rse.2018.04.015","title":"Lidar supported estimators of wood volume and aboveground biomass from the Danish national forest inventory (2012–2016)","year":2018,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":64,"is_retracted":false,"has_abstract":false,"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":"","keywords":"Forest inventory; Estimator; Environmental science; Lidar; Biomass (ecology); Estimation; National forest; Physical geography; Statistics; Danish; Remote sensing; Forestry; Forest management; Geography; Mathematics; Ecology; Agroforestry","score_opus":0.012654013659105649,"score_gpt":0.22330930552398967,"score_spread":0.21065529186488402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2797957951","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94472766,0.0015776271,0.0025575273,0.00009843193,0.000047495636,0.000013500364,0.04830726,0.000070459726,0.0026000692],"genre_scores_gemma":[0.94161606,0.00034112614,0.0034264766,0.00002138255,0.000012012771,0.00001478157,0.053064793,0.000028353412,0.001474924],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942905,0.00007466568,0.00007618554,0.00019887107,0.00012767296,0.00009354641],"domain_scores_gemma":[0.99890864,0.00021586445,0.00023079479,0.00021137031,0.00035915617,0.000074217496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013317588,0.0004176212,0.000483749,0.0015959304,0.00045523778,0.0008966078,0.0008828212,0.00054731284,0.0013053444],"category_scores_gemma":[0.002701581,0.00053692417,0.000659279,0.001983237,0.00024843914,0.00078290404,0.00080762594,0.0003897246,0.00080693816],"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":[0.00065839366,0.00013509818,0.85914767,0.00067399413,0.0010890606,0.0002924187,0.00073409034,0.059800994,0.0057054725,0.00155794,0.013391881,0.056812916],"study_design_scores_gemma":[0.000013787502,0.000020290683,0.97569704,0.00008539495,0.00014755446,0.00016377274,0.0003625821,0.011794482,0.001162476,0.00021589917,0.01028838,0.00004831929],"about_ca_topic_score_codex":0.12856856,"about_ca_topic_score_gemma":0.2661288,"teacher_disagreement_score":0.12856856,"about_ca_system_score_codex":0.0010036425,"about_ca_system_score_gemma":0.00095245213,"threshold_uncertainty_score":0.2556404},"labels":[],"label_agreement":null},{"id":"W2799281304","doi":"10.1016/j.rse.2018.04.028","title":"Unit-level and area-level small area estimation under heteroscedasticity using digital aerial photogrammetry data","year":2018,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":48,"is_retracted":false,"has_abstract":false,"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":"Norsk institutt for Bioøkonomi","keywords":"Heteroscedasticity; Estimator; Sample (material); Variable (mathematics); Computer science; Photogrammetry; Statistics; Residual; Small area estimation; Remote sensing; Sample size determination; Data mining; Mathematics; Algorithm; Geography; Artificial intelligence","score_opus":0.13331171571000439,"score_gpt":0.2811435589266674,"score_spread":0.14783184321666304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799281304","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6453329,0.00023104236,0.35262707,0.00013001464,0.000030983665,0.000031096806,0.0006043672,0.0002649164,0.0007475992],"genre_scores_gemma":[0.9753488,0.00006336131,0.022562169,0.000014809922,0.000021377786,0.000017371747,0.0008046135,0.000025278347,0.0011422599],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979692,0.0009458304,0.00013022973,0.0005615142,0.00019280215,0.00020027802],"domain_scores_gemma":[0.99040717,0.0070540556,0.00076281145,0.0010035875,0.00064058276,0.00013176889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004524753,0.00042362118,0.00092943234,0.0006865747,0.00034424223,0.0008437813,0.0014198377,0.00047899055,0.0015429307],"category_scores_gemma":[0.010653586,0.0003755927,0.0012028298,0.0015569244,0.0005358469,0.0011044698,0.0008328322,0.0006017264,0.00028942866],"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":[0.00048397327,0.00018969591,0.2006103,0.00015069584,0.0015890968,0.00040614067,0.00029996454,0.6396834,0.0037647672,0.0134050045,0.0013906278,0.13802628],"study_design_scores_gemma":[0.000009816177,0.000048176855,0.048919637,0.000005264729,0.00010526035,0.000027109512,0.0000976144,0.9458633,0.00076936785,0.003844371,0.00029061487,0.00001954775],"about_ca_topic_score_codex":0.018993992,"about_ca_topic_score_gemma":0.020424098,"teacher_disagreement_score":0.018993992,"about_ca_system_score_codex":0.00054044026,"about_ca_system_score_gemma":0.0011607717,"threshold_uncertainty_score":0.037766874},"labels":[],"label_agreement":null},{"id":"W2799403837","doi":"10.1016/j.rse.2018.05.012","title":"Globally scalable alpine snow metrics","year":2018,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":54,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Global Institute for Water Security; University of Northern British Columbia; University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Alberta Agriculture and Forestry; Canada First Research Excellence Fund; Global Water Futures; University of Calgary","keywords":"Snow; Meltwater; Snowmelt; Environmental science; Snowpack; Snow field; Surface runoff; Physical geography; Hydrology (agriculture); Remote sensing; Geology; Snow cover; Geomorphology; Geography","score_opus":0.019902388767877942,"score_gpt":0.20747444216723482,"score_spread":0.18757205339935687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799403837","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47595197,0.002228476,0.38373685,0.0011256003,0.00041622674,0.00042128874,0.06814887,0.039674293,0.028296327],"genre_scores_gemma":[0.8381588,0.00032112564,0.10900024,0.00008441276,0.00013496904,0.00012226927,0.04800325,0.00082605274,0.0033489068],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99946374,0.00010302944,0.0000322092,0.00017249149,0.00016594121,0.00006256427],"domain_scores_gemma":[0.9990361,0.00013264909,0.000095525065,0.00038072944,0.00028792996,0.000067132634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007987444,0.0011603616,0.00070428924,0.0014173345,0.00031664272,0.0011202722,0.0008850346,0.00049077853,0.003838366],"category_scores_gemma":[0.0035323647,0.00023856718,0.0003931198,0.0013862356,0.00021522935,0.0023460155,0.0014510122,0.00046154714,0.0012306859],"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":[0.00072810124,0.0002182332,0.026606247,0.0002762014,0.0004001427,0.00017325897,0.0001557719,0.4749579,0.011829118,0.008869455,0.05947135,0.4163142],"study_design_scores_gemma":[0.00007953068,0.00009326382,0.010219849,0.000026245527,0.000033470562,0.000067595305,0.00009886951,0.95975894,0.004046821,0.0121045485,0.013450588,0.00002024551],"about_ca_topic_score_codex":0.012910212,"about_ca_topic_score_gemma":0.022988023,"teacher_disagreement_score":0.012910212,"about_ca_system_score_codex":0.0007025891,"about_ca_system_score_gemma":0.00089537195,"threshold_uncertainty_score":0.02567017},"labels":[],"label_agreement":null},{"id":"W2801864473","doi":"10.1016/j.rse.2018.04.011","title":"The SMAP mission combined active-passive soil moisture product at 9 km and 3 km spatial resolutions","year":2018,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":113,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"NASA Headquarters; Jet Propulsion Laboratory; National Aeronautics and Space Administration","keywords":"Remote sensing; Environmental science; Radiometer; Radar; Water content; Image resolution; Meteorology; Geology; Computer science; Geography","score_opus":0.008172142906028117,"score_gpt":0.21112197929678758,"score_spread":0.20294983639075947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2801864473","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56457454,0.0008509204,0.05431087,0.0007300533,0.00076352066,0.00071670196,0.32973388,0.01551307,0.032806415],"genre_scores_gemma":[0.71406543,0.00039807655,0.0971585,0.0005984528,0.00035607917,0.0007914787,0.17887488,0.0013255698,0.006431607],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995266,0.0000429482,0.000032316333,0.00012768699,0.00020180053,0.00006865617],"domain_scores_gemma":[0.99948645,0.000036473895,0.000059552873,0.00010783941,0.00026378574,0.000045880945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077043666,0.0011797363,0.00070655235,0.002186193,0.0005151446,0.0007135757,0.0010767914,0.0009286914,0.0047093313],"category_scores_gemma":[0.0008059811,0.00056669634,0.00088589144,0.0021282386,0.00029271236,0.0011768877,0.0007984043,0.0007997311,0.0024620688],"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":[0.0019746744,0.000947489,0.10867169,0.0017326646,0.0018738075,0.0006327195,0.00064664235,0.07486549,0.38027984,0.004046596,0.123092405,0.30123606],"study_design_scores_gemma":[0.0008633415,0.0004991365,0.45841783,0.00019846413,0.0010047128,0.00069174636,0.00033880255,0.20230864,0.13021012,0.004084919,0.20091565,0.00046663944],"about_ca_topic_score_codex":0.011323413,"about_ca_topic_score_gemma":0.01590249,"teacher_disagreement_score":0.011323413,"about_ca_system_score_codex":0.0004318243,"about_ca_system_score_gemma":0.000879895,"threshold_uncertainty_score":0.022514999},"labels":[],"label_agreement":null},{"id":"W2802773210","doi":"10.1016/j.rse.2018.04.022","title":"Development of a conceptual warning system for toxic levels of Alexandrium fundyense in the Bay of Fundy based on remote sensing data","year":2018,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Fisheries and Oceans Canada; Bedford Institute of Oceanography","funders":"Canadian Space Agency; National Oceanic and Atmospheric Administration; Natural Environment Research Council; Sight Research UK","keywords":"Bay; Remote sensing; Environmental science; Warning system; Aerial survey; Oceanography; Computer science; Geology; Telecommunications","score_opus":0.059199477295336575,"score_gpt":0.23470643990283177,"score_spread":0.1755069626074952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802773210","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60615873,0.00026489672,0.3711147,0.0019150983,0.00026352349,0.0010516124,0.0036000672,0.0057932693,0.009838195],"genre_scores_gemma":[0.86775506,0.00009090091,0.12923081,0.00008489105,0.000019369136,0.00020947354,0.0015302578,0.000025693287,0.0010535618],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997521,0.000041648997,0.000038567694,0.00007546005,0.000059464375,0.00003271706],"domain_scores_gemma":[0.999225,0.000117625,0.00014743001,0.00005405785,0.00037641506,0.00007942429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097035826,0.00038255236,0.00030246947,0.0013123682,0.0004842334,0.0012210946,0.0009197648,0.0006904214,0.0011469672],"category_scores_gemma":[0.0018931087,0.00023430376,0.00027621584,0.00057244615,0.00030299535,0.0011272412,0.0012830542,0.00048305595,0.00023938996],"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":[0.000675519,0.0006593196,0.23671952,0.0007414343,0.0002714278,0.00096107024,0.0018809548,0.22449206,0.111623295,0.014920488,0.013447893,0.3936071],"study_design_scores_gemma":[0.000106075546,0.0002854692,0.08264683,0.00010760152,0.0001549546,0.00008698161,0.0011448449,0.8847576,0.017811589,0.0022641786,0.010537531,0.00009631834],"about_ca_topic_score_codex":0.030173348,"about_ca_topic_score_gemma":0.02207283,"teacher_disagreement_score":0.96982664,"about_ca_system_score_codex":0.0010415333,"about_ca_system_score_gemma":0.0025296418,"threshold_uncertainty_score":0.059995472},"labels":[],"label_agreement":null},{"id":"W2806808428","doi":"10.1016/j.rse.2018.08.002","title":"Landscape variability of vegetation change across the forest to tundra transition of central Canada","year":2018,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":42,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Queen's University; National Aeronautics and Space Administration","keywords":"Tundra; Normalized Difference Vegetation Index; Ecotone; Environmental science; Vegetation (pathology); Arctic vegetation; Physical geography; Taiga; Boreal; Arctic; Ecosystem; Climate change; Remote sensing; Shrub; Geography; Ecology; Forestry","score_opus":0.023888356858254536,"score_gpt":0.21855298601753093,"score_spread":0.1946646291592764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806808428","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9970932,0.000245512,0.00006370437,0.00015113312,0.0000060285533,0.000007383641,0.0011879241,0.000009996117,0.0012351248],"genre_scores_gemma":[0.9986015,0.00009112672,0.00006487818,0.000027909291,0.0000014370904,0.0000033328126,0.00043806245,0.0000038194275,0.0007679028],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996871,0.000022482734,0.000013703999,0.00007459015,0.000055043296,0.00014711259],"domain_scores_gemma":[0.9990983,0.00007611023,0.00009032299,0.000036931568,0.00048182163,0.00021658208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029721964,0.00014435673,0.00026230782,0.0012452168,0.0016364978,0.0013993818,0.00074436434,0.00033283298,0.0018142996],"category_scores_gemma":[0.0009665641,0.00015467707,0.00034889168,0.0025481272,0.00070638873,0.00032111187,0.00068239134,0.00034084535,0.00012324723],"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":[0.00024715418,0.000050524162,0.9756024,0.000042274143,0.00018586685,0.0001954114,0.002456457,0.0015352806,0.0024864916,0.000616546,0.002085883,0.014495717],"study_design_scores_gemma":[0.0000038934927,0.0000036822546,0.99723107,0.000008746843,0.0000141692835,0.000016698523,0.0011587883,0.0005654147,0.000061813684,0.000024293913,0.0009055906,0.000005720695],"about_ca_topic_score_codex":0.9971083,"about_ca_topic_score_gemma":0.9986511,"teacher_disagreement_score":0.021149669,"about_ca_system_score_codex":0.021149669,"about_ca_system_score_gemma":0.016472625,"threshold_uncertainty_score":0.15345228},"labels":[],"label_agreement":null},{"id":"W2883859996","doi":"10.1016/j.rse.2018.06.015","title":"Comparison of visible and multi-satellite global inundation datasets at high-spatial resolution","year":2018,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":60,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Joint Research Centre; Centre National d’Etudes Spatiales; European Commission; National Aeronautics and Space Administration","keywords":"Downscaling; Satellite; Environmental science; Surface water; Remote sensing; Image resolution; High resolution; Vegetation (pathology); Scale (ratio); Satellite imagery; Geology; Meteorology; Geography; Computer science; Cartography; Precipitation","score_opus":0.02148396900620829,"score_gpt":0.29378012231315437,"score_spread":0.27229615330694606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883859996","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9235783,0.00031312494,0.002104946,0.00032035794,0.00011587381,0.00005403701,0.06761177,0.0006252917,0.0052762944],"genre_scores_gemma":[0.8609615,0.00025885444,0.0057107387,0.00008547225,0.000052753832,0.000057667086,0.13092285,0.00015594813,0.0017943094],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994442,0.00009083524,0.00007488502,0.00013257893,0.00016868892,0.000088776156],"domain_scores_gemma":[0.9981517,0.00040182527,0.00022629955,0.0003084176,0.0008100884,0.0001016106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013604915,0.0004221035,0.00038642352,0.0023164041,0.00033425563,0.0008646581,0.00067231915,0.00075917697,0.0023180058],"category_scores_gemma":[0.0025021227,0.00024543126,0.00050939707,0.003399726,0.00020792651,0.0011716265,0.0005504639,0.0003135701,0.00084785203],"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":[0.004481964,0.0018053195,0.58479536,0.0009766117,0.001961333,0.00068622845,0.00088317157,0.09863068,0.055957712,0.002428327,0.051886853,0.19550647],"study_design_scores_gemma":[0.00015509059,0.00008578191,0.93629295,0.000057797974,0.00023458911,0.00013735902,0.00050638465,0.0443693,0.0057656127,0.00029187262,0.012030513,0.000072742274],"about_ca_topic_score_codex":0.04878821,"about_ca_topic_score_gemma":0.0864771,"teacher_disagreement_score":0.04878821,"about_ca_system_score_codex":0.00067825936,"about_ca_system_score_gemma":0.0006835724,"threshold_uncertainty_score":0.09700847},"labels":[],"label_agreement":null},{"id":"W2887022345","doi":"10.1016/j.rse.2018.08.006","title":"Semi-empirical ocean surface model for compact-polarimetry mode SAR of RADARSAT Constellation Mission","year":2018,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Ocean Waves and Remote Sensing","field":"Earth and Planetary Sciences","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bedford Institute of Oceanography; Environment and Climate Change Canada; Fisheries and Oceans Canada","funders":"Office of Energy Research and Development; Excellent Youth Foundation of Jiangsu Scientific Committee; Canadian Space Agency; National Oceanic and Atmospheric Administration; National Youth Foundation of China; National Natural Science Foundation of China","keywords":"Buoy; Synthetic aperture radar; Remote sensing; Wind wave; Wind speed; Environmental science; Meteorology; Radar; Geology; Polarimetry; Mode (computer interface); Computer science; Oceanography; Physics; Scattering","score_opus":0.03309186572327152,"score_gpt":0.264039617307141,"score_spread":0.23094775158386946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2887022345","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36601123,0.0014626728,0.61146086,0.0006836864,0.00028330175,0.000098865574,0.005265002,0.0029083535,0.011826109],"genre_scores_gemma":[0.94921654,0.0005342732,0.040110312,0.00008441821,0.000051012925,0.000121685625,0.004192551,0.00030224893,0.0053869523],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99992394,0.000018848705,0.0000056276645,0.000022077073,0.000018399456,0.00001116374],"domain_scores_gemma":[0.99986005,0.0000398959,0.000017664235,0.000018999419,0.000054192587,0.00000916772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016628452,0.0004159033,0.0004196815,0.00028226164,0.00022281625,0.00044563858,0.0011605556,0.0008157631,0.0017067988],"category_scores_gemma":[0.0006618611,0.00033076518,0.00048055872,0.00036478546,0.00020272816,0.0008181164,0.0004031993,0.00067318085,0.00076262304],"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":[0.000025732581,0.00001792173,0.0012038491,0.000027748032,0.000018622895,0.000039484723,0.000015485992,0.98926276,0.00085541717,0.0007315306,0.00070649164,0.0070949253],"study_design_scores_gemma":[0.000004336568,0.0000028814745,0.00029922332,0.0000018817826,0.0000027627063,0.0000050498775,0.000003167697,0.9991092,0.00009276112,0.00023683,0.00023933162,0.0000025333022],"about_ca_topic_score_codex":0.019723032,"about_ca_topic_score_gemma":0.013121093,"teacher_disagreement_score":0.019723032,"about_ca_system_score_codex":0.00036227255,"about_ca_system_score_gemma":0.0006417111,"threshold_uncertainty_score":0.03921646},"labels":[],"label_agreement":null},{"id":"W2888521623","doi":"10.1016/j.rse.2018.08.021","title":"Identification of typical diurnal patterns for clear-sky climatology of surface urban heat islands","year":2018,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":129,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; Nanjing University; National Natural Science Foundation of China","keywords":"Noon; Urban heat island; Megacity; Environmental science; Diurnal cycle; Climatology; Morning; Sky; Satellite; Daytime; Meteorology; Atmospheric sciences; Geography; Geology","score_opus":0.011739322964146358,"score_gpt":0.23318932781005497,"score_spread":0.2214500048459086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888521623","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9932476,0.00011752482,0.0035023848,0.000028588234,0.000012779573,0.000016124248,0.001086541,0.00010878073,0.0018796306],"genre_scores_gemma":[0.9965928,0.000036090783,0.0018913348,0.0000049195955,0.0000074131103,0.000008346668,0.0012375945,0.000016646922,0.00020476777],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99992776,0.00001030902,0.000005675157,0.000024974192,0.000011028797,0.000020142334],"domain_scores_gemma":[0.9997199,0.000061594175,0.000038440874,0.00003876314,0.0000782892,0.00006294999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015395341,0.00014020658,0.00011666253,0.00068218104,0.00022735813,0.00031550185,0.00014326985,0.0001791386,0.0006959577],"category_scores_gemma":[0.0004942929,0.00009654054,0.00018938107,0.0005163225,0.00008490207,0.00018060861,0.00016115946,0.0001251894,0.00019155857],"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":[0.00042045122,0.00019146196,0.8097971,0.00016547939,0.00015445439,0.00039022166,0.00074854307,0.0066406387,0.09460137,0.0006212847,0.0034205283,0.08284839],"study_design_scores_gemma":[0.000005069327,0.000024215955,0.9849954,0.0000047585645,0.00001451616,0.00010027045,0.00014665851,0.012293681,0.0016441985,0.000074408934,0.00068980537,0.000007016658],"about_ca_topic_score_codex":0.0057237404,"about_ca_topic_score_gemma":0.01756318,"teacher_disagreement_score":0.0057237404,"about_ca_system_score_codex":0.00011053853,"about_ca_system_score_gemma":0.00023104557,"threshold_uncertainty_score":0.011380851},"labels":[],"label_agreement":null},{"id":"W2889410258","doi":"10.1016/j.rse.2018.08.025","title":"Preliminary assessment of 20-m surface albedo retrievals from sentinel-2A surface reflectance and MODIS/VIIRS surface anisotropy measures","year":2018,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":79,"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":"European Commission; U.S. Geological Survey; National Aeronautics and Space Administration","keywords":"Bidirectional reflectance distribution function; Remote sensing; Environmental science; Albedo (alchemy); Moderate-resolution imaging spectroradiometer; Shortwave; Visible Infrared Imaging Radiometer Suite; Satellite; Atmospheric radiative transfer codes; Radiometer; Spectroradiometer; Pyranometer; Radiative transfer; Geology; Radiation; Reflectivity; Physics; Optics","score_opus":0.01374268339373919,"score_gpt":0.24923451546098443,"score_spread":0.23549183206724525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889410258","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.959409,0.0008064145,0.031271998,0.00020891482,0.000056437857,0.0002849814,0.0021099627,0.00086675404,0.004985547],"genre_scores_gemma":[0.9538413,0.00031243346,0.040871102,0.00006554785,0.000024361309,0.000088870984,0.0039045173,0.000106666936,0.0007851709],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843794,0.0004477364,0.000115984614,0.00026735917,0.00062255835,0.00010837856],"domain_scores_gemma":[0.99724615,0.00092456146,0.00023522999,0.0003303828,0.0011268525,0.0001368051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051039355,0.0007660128,0.00047166573,0.0007835735,0.00039751027,0.00073605805,0.0007072041,0.00072013703,0.0008087122],"category_scores_gemma":[0.006838225,0.00027418006,0.0005655966,0.0007801429,0.0002684534,0.0013267475,0.0005481105,0.0003048396,0.0005123346],"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":[0.005107894,0.0017797834,0.22748841,0.00075619336,0.0006909544,0.00056160055,0.0005772328,0.25986317,0.23313476,0.0013004204,0.0037031362,0.26503643],"study_design_scores_gemma":[0.000595073,0.0031849744,0.31374428,0.00014868769,0.0003639274,0.00036598276,0.000425656,0.56666154,0.104780324,0.00070141745,0.008800637,0.00022747238],"about_ca_topic_score_codex":0.011157316,"about_ca_topic_score_gemma":0.012909476,"teacher_disagreement_score":0.011157316,"about_ca_system_score_codex":0.0005302353,"about_ca_system_score_gemma":0.00087186735,"threshold_uncertainty_score":0.0269925},"labels":[],"label_agreement":null},{"id":"W2891557443","doi":"10.1016/j.rse.2018.08.003","title":"Potential of a two-component polarimetric decomposition at C-band for soil moisture retrieval over agricultural fields","year":2018,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":65,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Science and Engineering Research Council; Canadian Space Agency; National Aeronautics and Space Administration","keywords":"Environmental science; Remote sensing; Scattering; Polarimetry; Water content; Vegetation (pathology); Surface roughness; Canola; Soil science; Moisture; Attenuation; Synthetic aperture radar; C band; Volume (thermodynamics); Agronomy; Materials science; Meteorology; Physics; Geology; Optics","score_opus":0.0065176037767266815,"score_gpt":0.23013632564011344,"score_spread":0.22361872186338674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891557443","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5267098,0.0012918254,0.45920363,0.0006081805,0.00021930809,0.0001675584,0.0018696262,0.0018182457,0.0081118895],"genre_scores_gemma":[0.7587089,0.0008161105,0.23546049,0.00017651563,0.000094860574,0.000088385044,0.0018721868,0.00020861535,0.0025738976],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99991,0.000020187515,0.000003938475,0.000023647377,0.000026657533,0.00001553115],"domain_scores_gemma":[0.99979573,0.000056858622,0.000011054782,0.000026305266,0.000090666275,0.000019285011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002339098,0.0007305493,0.00025664843,0.00055043295,0.00019330365,0.0006121802,0.0003228736,0.0004249913,0.0015925507],"category_scores_gemma":[0.0004925752,0.0002486259,0.00039329223,0.00071421586,0.00017317188,0.0008976049,0.00030188554,0.0005302833,0.0004547466],"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":[0.0008957861,0.0004514397,0.005827363,0.00030752592,0.00016104594,0.00010946378,0.00007846006,0.089606985,0.5486001,0.001504891,0.0024274124,0.3500295],"study_design_scores_gemma":[0.00011647094,0.000110761204,0.009753416,0.000017539513,0.00012594077,0.00005495327,0.00004356441,0.940725,0.04606653,0.00086807105,0.002063441,0.000054350974],"about_ca_topic_score_codex":0.0048354384,"about_ca_topic_score_gemma":0.0071111196,"teacher_disagreement_score":0.0048354384,"about_ca_system_score_codex":0.00017792845,"about_ca_system_score_gemma":0.00060580485,"threshold_uncertainty_score":0.009614587},"labels":[],"label_agreement":null},{"id":"W2895784174","doi":"10.1016/j.rse.2018.10.014","title":"Discrimination of liana and tree leaves from a Neotropical Dry Forest using visible-near infrared and longwave infrared reflectance spectra","year":2018,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Inter-American Institute for Global Change Research","keywords":"Liana; Remote sensing; Longwave; Environmental science; Near-infrared spectroscopy; Geology; Botany; Physics; Optics; Radiative transfer; Biology","score_opus":0.020416193413698387,"score_gpt":0.2602222012958118,"score_spread":0.23980600788211343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895784174","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9991616,0.000061709485,0.00015900514,0.000006292278,0.0000011057377,0.0000041800763,0.000055058612,0.0000070037026,0.0005441101],"genre_scores_gemma":[0.99876356,0.0000697484,0.00045606104,0.000023126435,0.0000020157822,0.0000066591365,0.00029901657,0.0000042244087,0.00037569765],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99993765,0.000010139087,0.0000049594405,0.000022331951,0.000013810517,0.000011089509],"domain_scores_gemma":[0.9998443,0.000047969614,0.000023812832,0.000008442257,0.000028136508,0.00004732361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020472094,0.0002154016,0.00020092407,0.0006651395,0.00030417886,0.00038811847,0.00014555718,0.00016755334,0.00033537793],"category_scores_gemma":[0.00023456775,0.0001358217,0.00010444414,0.00028342498,0.00025466067,0.00026865158,0.00018256808,0.00016717384,0.00015245183],"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":[0.001063815,0.000101705904,0.22246295,0.000095857096,0.00006704833,0.00027490797,0.0012747045,0.00033511428,0.7615723,0.00008805977,0.00006806958,0.012595433],"study_design_scores_gemma":[0.000016882448,0.000088216504,0.9916521,0.000003984837,0.00002815455,0.00017374066,0.00050296355,0.0006458278,0.006534129,0.00003343158,0.00031241542,0.000008094147],"about_ca_topic_score_codex":0.017195273,"about_ca_topic_score_gemma":0.041609984,"teacher_disagreement_score":0.017195273,"about_ca_system_score_codex":0.00026844366,"about_ca_system_score_gemma":0.0002129576,"threshold_uncertainty_score":0.034190416},"labels":[],"label_agreement":null},{"id":"W2898331420","doi":"10.1016/j.rse.2018.10.032","title":"Satellite remote sensing of canopy-forming kelp on a complex coastline: A novel procedure using the Landsat image archive","year":2018,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Marine and coastal plant biology","field":"Earth and Planetary Sciences","cited_by":69,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Fisheries and Oceans Canada; Tula Foundation; University of Victoria","funders":"Fisheries and Oceans Canada; Hakai Institute; Victoria University; DigitalGlobe Foundation","keywords":"Kelp; Kelp forest; Remote sensing; Environmental science; Satellite imagery; Satellite; Geography; Ecology","score_opus":0.027296895293994674,"score_gpt":0.22591770075595677,"score_spread":0.1986208054619621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898331420","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8752422,0.00027075247,0.11179743,0.00009545881,0.00005251875,0.00029091467,0.0061639105,0.000796754,0.0052902224],"genre_scores_gemma":[0.74588734,0.00024574978,0.24524422,0.000049554583,0.000039386618,0.00019722141,0.006016543,0.00011040374,0.0022095155],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9998895,0.000010460942,0.00000879924,0.000043988857,0.000031919484,0.000015251617],"domain_scores_gemma":[0.9998267,0.000017666644,0.000024021181,0.00004591977,0.000065640364,0.00001991648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022019846,0.0002853288,0.00023702568,0.001342702,0.00042442052,0.00045628066,0.00041577787,0.00027212995,0.001140539],"category_scores_gemma":[0.00038851233,0.0001944722,0.0003392806,0.0010874014,0.00017576032,0.0004216724,0.00044580895,0.00023511969,0.00036095583],"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":[0.00025080304,0.00029500038,0.21114701,0.0002442036,0.0002428262,0.0004998352,0.00066884543,0.01603061,0.3319728,0.0010736072,0.0037369549,0.43383744],"study_design_scores_gemma":[0.000058148613,0.00014073301,0.83829904,0.00003622297,0.00023764795,0.0005980129,0.0007416738,0.11641888,0.03315656,0.0007230614,0.00950886,0.00008114586],"about_ca_topic_score_codex":0.015138354,"about_ca_topic_score_gemma":0.061778173,"teacher_disagreement_score":0.015138354,"about_ca_system_score_codex":0.00020974626,"about_ca_system_score_gemma":0.0008230637,"threshold_uncertainty_score":0.030100465},"labels":[],"label_agreement":null},{"id":"W2901843700","doi":"10.1016/j.rse.2018.11.017","title":"Integration of multi-resource remotely sensed data and allometric models for forest aboveground biomass estimation in China","year":2018,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":155,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"National Key Research and Development Program of China; Chinese Academy of Sciences","keywords":"Tree allometry; Environmental science; Allometry; Remote sensing; Forest inventory; Biomass (ecology); Synthetic aperture radar; Forest management; Geology; Agroforestry; Oceanography","score_opus":0.038436112596709746,"score_gpt":0.2749367397435365,"score_spread":0.23650062714682674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901843700","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9967937,0.00018809718,0.002361024,0.00005075697,0.0000065550444,0.000005909972,0.00013189911,0.000033392156,0.00042878036],"genre_scores_gemma":[0.99824345,0.00007559283,0.0012135685,0.0000062333106,0.0000040216196,0.0000039857596,0.00022740882,0.000005542657,0.00022010719],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995912,0.000075471944,0.000039150294,0.00012112146,0.00008707677,0.00008592345],"domain_scores_gemma":[0.99960035,0.00008809334,0.00006938121,0.00005489688,0.00013879543,0.000048552796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014967481,0.00059916795,0.000535649,0.0017333745,0.0004958634,0.0007663158,0.0007837393,0.0003434658,0.00042255744],"category_scores_gemma":[0.0008282492,0.00038435456,0.00068306935,0.0023692609,0.00024888013,0.0010996829,0.0005318771,0.00017788602,0.000093426315],"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":[0.0002550016,0.00042689056,0.5301127,0.00013962756,0.000590107,0.00039757838,0.00028011747,0.29384702,0.008432417,0.0010880632,0.00089253776,0.1635379],"study_design_scores_gemma":[0.000020103915,0.000045494864,0.39080995,0.000012773782,0.00023852747,0.00003678486,0.00018893943,0.6065667,0.0011224047,0.00046601595,0.00045289996,0.000039365084],"about_ca_topic_score_codex":0.15895541,"about_ca_topic_score_gemma":0.17649505,"teacher_disagreement_score":0.15895541,"about_ca_system_score_codex":0.0016034372,"about_ca_system_score_gemma":0.0021119528,"threshold_uncertainty_score":0.31606036},"labels":[],"label_agreement":null},{"id":"W2902807621","doi":"10.1016/j.rse.2018.11.022","title":"Proximal remote sensing of tree physiology at northern treeline: Do late-season changes in the photochemical reflectance index (PRI) respond to climate or photoperiod?","year":2018,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Center for Northern Studies","funders":"Innovative Research Group Project of the National Natural Science Foundation of China; National Aeronautics and Space Administration","keywords":"Photochemical Reflectance Index; Evergreen; Environmental science; Climate change; Phenology; Growing season; Vegetation (pathology); Ecotone; Tundra; Atmospheric sciences; Ecology; Climatology; Physical geography; Ecosystem; Normalized Difference Vegetation Index; Geography; Biology; Habitat","score_opus":0.01309384423504615,"score_gpt":0.2462763144593936,"score_spread":0.23318247022434746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902807621","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99693215,0.0003353635,0.0013411745,0.00007251561,0.000009246561,0.000005890153,0.00018903008,0.000025434047,0.0010891309],"genre_scores_gemma":[0.9985732,0.00014034809,0.0005220077,0.000055505756,0.000012517716,0.000003991786,0.00027721052,0.000010624357,0.00040463966],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999012,0.000025502299,0.0000032726728,0.000040955667,0.000011520753,0.000017561959],"domain_scores_gemma":[0.9996923,0.00008484924,0.000073705014,0.000022257867,0.000053212472,0.00007370747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038731087,0.00017085129,0.00033408648,0.00021771582,0.00023145966,0.00054044824,0.0002513286,0.00034153703,0.0008663825],"category_scores_gemma":[0.0007179114,0.00016181405,0.00018712874,0.00030719023,0.00018609974,0.0006501262,0.00026460845,0.00024872096,0.00024140558],"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":[0.0007572482,0.00012309282,0.80985653,0.00011512532,0.0001441679,0.00007532511,0.00061234034,0.00094540993,0.16236806,0.0002738549,0.00050919276,0.024219662],"study_design_scores_gemma":[0.0000029586636,0.000020902748,0.99823546,0.0000032323178,0.000010091562,0.000019093815,0.00010395072,0.0007041147,0.00069258065,0.000043729775,0.00016066687,0.0000031677082],"about_ca_topic_score_codex":0.011856858,"about_ca_topic_score_gemma":0.026070677,"teacher_disagreement_score":0.011856858,"about_ca_system_score_codex":0.0001668762,"about_ca_system_score_gemma":0.00021533301,"threshold_uncertainty_score":0.023575664},"labels":[],"label_agreement":null},{"id":"W2907047183","doi":"10.1016/j.rse.2018.12.032","title":"Assessment of red-edge vegetation indices for crop leaf area index estimation","year":2018,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":285,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nipissing University; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Northern Ontario Heritage Fund Corporation","keywords":"Leaf area index; Canopy; Remote sensing; Environmental science; Vegetation (pathology); Markov chain Monte Carlo; Mathematics; Monte Carlo method; Crop; Agronomy; Statistics; Geography; Forestry","score_opus":0.01355143423924488,"score_gpt":0.2556883715705187,"score_spread":0.24213693733127384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907047183","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9183503,0.00072956714,0.07661974,0.00006791615,0.000020276646,0.00006780264,0.00041681266,0.00020969743,0.0035180617],"genre_scores_gemma":[0.9519691,0.00016531315,0.046739787,0.000015290561,0.000010524856,0.000015671883,0.0003891242,0.000020635656,0.0006745],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995987,0.00015733331,0.000017289303,0.00005772909,0.00014030276,0.00002873733],"domain_scores_gemma":[0.99861157,0.0007229658,0.0000840188,0.00009147009,0.00042758862,0.00006234311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002248504,0.000487242,0.00029707036,0.0007838327,0.00018559735,0.0006240056,0.00047689714,0.00041231635,0.0006526436],"category_scores_gemma":[0.0028764287,0.00016919394,0.00029247664,0.00036869824,0.00010925242,0.0006932162,0.00034550493,0.0002515319,0.0001996975],"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":[0.002681161,0.0010089813,0.19319502,0.00029980167,0.00043490872,0.00021768706,0.00018787698,0.15351237,0.123686105,0.0024465309,0.0015359942,0.5207935],"study_design_scores_gemma":[0.000041112587,0.0004493969,0.07230118,0.000015740352,0.00012309694,0.000060855808,0.00007917166,0.9040482,0.021801442,0.00041662581,0.00063657697,0.000026572803],"about_ca_topic_score_codex":0.002930621,"about_ca_topic_score_gemma":0.004656803,"teacher_disagreement_score":0.002930621,"about_ca_system_score_codex":0.00022739042,"about_ca_system_score_gemma":0.0002993825,"threshold_uncertainty_score":0.011891425},"labels":[],"label_agreement":null},{"id":"W2908207024","doi":"10.1016/j.rse.2018.12.024","title":"Sub-metre mapping of surface soil moisture in proglacial valleys of the tropical Andes using a multispectral unmanned aerial vehicle","year":2018,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":78,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; McGill University","funders":"Geological Society of America; National Sleep Foundation; American Geographical Society; Ohio State University; Explorers Club; American Philosophical Society","keywords":"Environmental science; Water content; Remote sensing; Multispectral image; Vegetation (pathology); Hydrology (agriculture); Normalized Difference Vegetation Index; Soil map; Soil water; Geology; Soil science; Climate change","score_opus":0.02105439359597118,"score_gpt":0.20944135681906462,"score_spread":0.18838696322309345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908207024","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99864334,0.000036915986,0.00040244186,0.000015314863,0.0000041777726,0.000006236819,0.00035035115,0.00002807863,0.000513247],"genre_scores_gemma":[0.99814713,0.000023950912,0.0011609175,0.000008913039,0.000004598057,0.000006157518,0.000356432,0.0000036879082,0.00028821366],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999416,0.0000049728155,0.000002806971,0.000020064124,0.000013870007,0.000016686285],"domain_scores_gemma":[0.9998946,0.000014738249,0.000016636719,0.00001351126,0.00003474431,0.000025731284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009746804,0.00017629607,0.00017885392,0.00054622,0.0002324263,0.00024009353,0.0002592682,0.0002207827,0.0007106537],"category_scores_gemma":[0.00018043941,0.000121482204,0.00016763213,0.00038091769,0.00015274633,0.00028058176,0.00029937876,0.0001402221,0.00014767292],"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":[0.00092053943,0.00063031365,0.71947616,0.000150082,0.00028960727,0.0006952832,0.0013289158,0.017072188,0.16132872,0.00035371288,0.001878805,0.09587562],"study_design_scores_gemma":[0.000025315341,0.00004642715,0.98307115,0.000005292152,0.00002494393,0.00009539115,0.00023263619,0.014567932,0.0013674784,0.0000537471,0.0004997259,0.00001000644],"about_ca_topic_score_codex":0.01842217,"about_ca_topic_score_gemma":0.039548658,"teacher_disagreement_score":0.01842217,"about_ca_system_score_codex":0.0001657746,"about_ca_system_score_gemma":0.0002852358,"threshold_uncertainty_score":0.036629856},"labels":[],"label_agreement":null},{"id":"W2913897118","doi":"10.1016/j.rse.2019.01.035","title":"Colour remote sensing of the impact of artificial light at night (I): The potential of the International Space Station and other DSLR-based platforms","year":2019,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Impact of Light on Environment and Health","field":"Environmental Science","cited_by":131,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dawson College; Cegep de Trois-Rivieres; Cegep de Thetford; Cégep de Sherbrooke","funders":"Horizon 2020 Framework Programme; Ministerio de Economía y Competitividad; Natural Environment Research Council; Fonds Québécois de la Recherche sur la Nature et les Technologies; Ministerio de Ciencia e Innovación; Sight Research UK; European Cooperation in Science and Technology; Johnson Space Center; European Commission; H2020 European Research Council; National Aeronautics and Space Administration","keywords":"Remote sensing; Panchromatic film; Environmental science; Satellite; Computer science; Light pollution; Photochemical Reflectance Index; Index (typography); Meteorology; Multispectral image; Leaf area index; Normalized Difference Vegetation Index; Geography","score_opus":0.011062124881969029,"score_gpt":0.23619464630751805,"score_spread":0.22513252142554901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913897118","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77274936,0.015432057,0.105190955,0.004042197,0.0022413903,0.0002563487,0.009031972,0.0026153177,0.08844037],"genre_scores_gemma":[0.8851667,0.0035420305,0.09820722,0.00104572,0.0005278174,0.000075330085,0.0024228815,0.00039400227,0.008618302],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99959093,0.00008023929,0.00000957969,0.00009088557,0.00018413011,0.000044327306],"domain_scores_gemma":[0.9995803,0.00007847822,0.00005341404,0.00004678361,0.0001810712,0.000059853526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071693404,0.0004885973,0.00046476937,0.0008574606,0.00031790437,0.0012330655,0.00047006676,0.00065337424,0.0023416993],"category_scores_gemma":[0.00058005797,0.0001647244,0.00052094064,0.001015717,0.00034021895,0.0009237754,0.001012654,0.00079127215,0.0007723233],"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":[0.0023439385,0.00033187133,0.09462519,0.0010158579,0.0005052192,0.00029152806,0.0006144288,0.01388345,0.442971,0.0031840645,0.024484908,0.41574854],"study_design_scores_gemma":[0.0003024522,0.0015189835,0.5708096,0.00047715596,0.0010408357,0.0011885368,0.0018769827,0.07510386,0.19372167,0.0061897202,0.14720052,0.00056966563],"about_ca_topic_score_codex":0.0056298138,"about_ca_topic_score_gemma":0.010818587,"teacher_disagreement_score":0.0056298138,"about_ca_system_score_codex":0.00042627574,"about_ca_system_score_gemma":0.00039820757,"threshold_uncertainty_score":0.01119405},"labels":[],"label_agreement":null},{"id":"W2920930972","doi":"10.1016/j.rse.2019.02.015","title":"Current status of Landsat program, science, and applications","year":2019,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":1106,"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; University of British Columbia; Canadian Forest Service","funders":"U.S. Geological Survey; National Aeronautics and Space Administration","keywords":"Remote sensing; Context (archaeology); Data quality; Earth observation; Data management; Earth system science; Computer science; Satellite; Environmental resource management; Data science; Environmental science; Geography; Database; Business; Geology; Engineering","score_opus":0.005920521512526606,"score_gpt":0.22307147492278318,"score_spread":0.21715095341025656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2920930972","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009889215,0.5609873,0.052773014,0.16879958,0.018647859,0.0007542496,0.009680828,0.0048178034,0.1736501],"genre_scores_gemma":[0.09257688,0.6253881,0.13846363,0.045700166,0.022677973,0.0018076081,0.025892673,0.0028209167,0.044672135],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.994331,0.0015462077,0.0005003351,0.000687287,0.0025440198,0.0003911652],"domain_scores_gemma":[0.97516835,0.00525809,0.001389659,0.0019161716,0.014567619,0.0017000837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019638322,0.00088152126,0.00089009065,0.0037400099,0.0020542548,0.0069739856,0.0035875004,0.0029437363,0.013520875],"category_scores_gemma":[0.024186945,0.00047716906,0.0005791744,0.008364804,0.004024733,0.011349048,0.0024484384,0.0044929464,0.009485368],"study_design_candidate":"not_applicable","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":[0.00010142213,0.00010412198,0.0026461163,0.0022000906,0.00003112321,0.000048168455,0.00028707617,0.00059812213,0.0009591302,0.04067888,0.18001436,0.7723314],"study_design_scores_gemma":[0.000009780002,0.00004593604,0.00215881,0.0010172455,0.000017820435,0.0000783842,0.00024760287,0.00058304984,0.00035555163,0.0072728875,0.98817885,0.000033975124],"about_ca_topic_score_codex":0.015038786,"about_ca_topic_score_gemma":0.01003756,"teacher_disagreement_score":0.019638322,"about_ca_system_score_codex":0.0040368196,"about_ca_system_score_gemma":0.01392093,"threshold_uncertainty_score":0.10385859},"labels":[],"label_agreement":null},{"id":"W2932791400","doi":"10.1016/j.rse.2019.03.020","title":"Validation of the Sentinel Simplified Level 2 Product Prototype Processor (SL2P) for mapping cropland biophysical variables using Sentinel-2/MSI and Landsat-8/OLI data","year":2019,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":104,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; Agriculture and Agri-Food Canada; Natural Resources Canada","funders":"Canadian Space Agency; Natural Resources Canada; U.S. Geological Survey; European Space Agency","keywords":"Environmental science; Remote sensing; Vegetation (pathology); In situ; Leaf area index; Mean squared error; Mathematics; Meteorology; Statistics; Agronomy; Geology; Geography","score_opus":0.04327977694104449,"score_gpt":0.24617759750392046,"score_spread":0.20289782056287597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2932791400","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95779294,0.000058617694,0.033272266,0.00011272541,0.000059442355,0.00014734997,0.0031536887,0.0024058938,0.0029970258],"genre_scores_gemma":[0.9437897,0.000040435363,0.049309924,0.00009760749,0.000010747708,0.00012522964,0.005489691,0.00020085176,0.00093572406],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99959964,0.00009742913,0.000026956657,0.00009849414,0.00014035348,0.000037158876],"domain_scores_gemma":[0.99903905,0.0003044089,0.00005723161,0.00016204653,0.00037635077,0.00006094005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013984918,0.00040275697,0.00022675305,0.0003475232,0.00018006783,0.00050924555,0.00064405566,0.00036850548,0.0021706768],"category_scores_gemma":[0.0025362764,0.00021267068,0.00025059975,0.0003405681,0.0001990028,0.00084093417,0.00042191363,0.00023357595,0.00087050383],"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":[0.0039074267,0.0012333473,0.1950343,0.00034845917,0.00033453674,0.00039504727,0.00046492802,0.16180412,0.286243,0.0016143412,0.01779679,0.3308237],"study_design_scores_gemma":[0.0004860639,0.0013360252,0.13802563,0.000023879358,0.000106250336,0.0001784853,0.00019644151,0.79672915,0.05646709,0.00068763696,0.0057118894,0.00005140989],"about_ca_topic_score_codex":0.0059093097,"about_ca_topic_score_gemma":0.006726531,"teacher_disagreement_score":0.0059093097,"about_ca_system_score_codex":0.00026041403,"about_ca_system_score_gemma":0.0005531848,"threshold_uncertainty_score":0.011749864},"labels":[],"label_agreement":null},{"id":"W2944792157","doi":"10.1016/j.rse.2019.04.031","title":"Estimating melt onset over Arctic sea ice from time series multi-sensor Sentinel-1 and RADARSAT-2 backscatter","year":2019,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":50,"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 Calgary; Environment and Climate Change Canada","funders":"","keywords":"Backscatter (email); Remote sensing; Arctic; Geology; Synthetic aperture radar; Temporal resolution; Terrain; Sea ice; Image resolution; Arctic ice pack; Scatterometer; Climatology; Environmental science; Wind speed; Oceanography; Cartography; Computer science; Geography; Artificial intelligence","score_opus":0.0069196154728253375,"score_gpt":0.18782663682239173,"score_spread":0.1809070213495664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944792157","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9951212,0.00019519679,0.0027058125,0.00001691024,0.000011081273,0.000012285789,0.0011308652,0.00009019711,0.00071648706],"genre_scores_gemma":[0.99045324,0.00019252561,0.0055172406,0.000015306847,0.000014902893,0.000014045641,0.0034080483,0.000027807164,0.00035698398],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998789,0.000008228257,0.000008280163,0.000033663186,0.000038146478,0.000032845106],"domain_scores_gemma":[0.999826,0.000023755805,0.00003928933,0.000008842464,0.00007740724,0.000024767045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030532214,0.00048630152,0.00026136969,0.001602925,0.00036141186,0.00058188115,0.00024303304,0.00021871847,0.00038598446],"category_scores_gemma":[0.00047664266,0.000177242,0.0005661673,0.00066606374,0.00016051887,0.00039733865,0.00034434744,0.00021728489,0.0001208425],"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":[0.00029648302,0.000096476935,0.8620521,0.0000856536,0.00018796774,0.00028919725,0.00032848708,0.038190775,0.04120422,0.00025946478,0.00087962335,0.05612958],"study_design_scores_gemma":[0.000015348176,0.00004299259,0.9174265,0.00003086772,0.000089381756,0.00007129038,0.00028703647,0.07392537,0.006372992,0.00013497904,0.0015799946,0.000023122358],"about_ca_topic_score_codex":0.12172207,"about_ca_topic_score_gemma":0.26231885,"teacher_disagreement_score":0.12172207,"about_ca_system_score_codex":0.00078329956,"about_ca_system_score_gemma":0.0008696234,"threshold_uncertainty_score":0.24202716},"labels":[],"label_agreement":null},{"id":"W2951147814","doi":"10.1016/j.rse.2019.111218","title":"Remote sensing of terrestrial plant biodiversity","year":2019,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":380,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Canada Foundation for Innovation; Alberta Innovates - Technology Futures; Natural Sciences and Engineering Research Council of Canada; University of Nebraska-Lincoln; Keck Institute for Space Studies; Alberta Biodiversity Monitoring Institute; National Sleep Foundation; National Aeronautics and Space Administration; National Science Foundation","keywords":"Biodiversity; Remote sensing; Ecosystem services; Measurement of biodiversity; Ecosystem; Environmental resource management; Environmental science; Ecosystem diversity; Geography; Ecology; Biology; Biodiversity conservation","score_opus":0.010522896565493494,"score_gpt":0.1871959626231379,"score_spread":0.1766730660576444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951147814","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8921743,0.010988067,0.040825643,0.0004529702,0.000076619064,0.00007725388,0.006546421,0.0009449213,0.04791376],"genre_scores_gemma":[0.9741641,0.0014848582,0.01945579,0.00010225461,0.000040336927,0.0000169462,0.0023748898,0.000032925804,0.0023278792],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99987245,0.000034809935,0.000004414686,0.00002651536,0.00004968635,0.000012062251],"domain_scores_gemma":[0.9998392,0.000049393388,0.00003412842,0.0000232076,0.000038040846,0.000016074822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030770904,0.00020206142,0.00015863305,0.0013229417,0.00016398409,0.0002561187,0.00021780822,0.00016026241,0.0015315935],"category_scores_gemma":[0.00029862477,0.000093747956,0.00015489933,0.0012458361,0.00013723498,0.00033237066,0.0002483975,0.00015811069,0.00032573508],"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":[0.0003518564,0.00020506266,0.13996969,0.00048398622,0.00034083583,0.00015733695,0.00025252384,0.017531041,0.33438402,0.003946951,0.0061127413,0.4962641],"study_design_scores_gemma":[0.00006443552,0.00017475164,0.8987694,0.000094035466,0.00014634238,0.0004479854,0.00036396683,0.036132555,0.03216315,0.004015799,0.027579978,0.000047683086],"about_ca_topic_score_codex":0.007273478,"about_ca_topic_score_gemma":0.013121203,"teacher_disagreement_score":0.007273478,"about_ca_system_score_codex":0.00023538839,"about_ca_system_score_gemma":0.00016544046,"threshold_uncertainty_score":0.014462233},"labels":[],"label_agreement":null},{"id":"W2955928210","doi":"10.1016/j.rse.2019.111234","title":"Crop phenology retrieval via polarimetric SAR decomposition and Random Forest algorithm","year":2019,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":98,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Phenology; Random forest; Remote sensing; Polarimetry; Synthetic aperture radar; Environmental science; Radar; Computer science; Machine learning; Agronomy; Scattering; Geography","score_opus":0.00408140750855005,"score_gpt":0.19826945219769246,"score_spread":0.19418804468914241,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955928210","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04169218,0.00038862604,0.9558314,0.000060502836,0.000032238713,0.000024619352,0.00019783669,0.0008871686,0.00088533876],"genre_scores_gemma":[0.35867652,0.000495583,0.6360697,0.000056135406,0.00006719836,0.000074501346,0.0015432454,0.00016025656,0.0028568562],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998702,0.000019762518,0.0000054636803,0.000041747615,0.000038248876,0.000024597595],"domain_scores_gemma":[0.99988127,0.00003198626,0.000017105833,0.000017649621,0.00004260812,0.000009313309],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002087887,0.000515359,0.0005283452,0.00075669965,0.00021828264,0.0003725519,0.000389444,0.00037402322,0.0009115453],"category_scores_gemma":[0.0004217751,0.0002697169,0.00061414845,0.00088083575,0.0001437507,0.00068977725,0.00028667907,0.00042164483,0.00069443154],"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":[0.00027859764,0.00015944232,0.003180261,0.0001452955,0.00012799448,0.00012368734,0.00004382678,0.26931942,0.104464576,0.0024905172,0.0032730063,0.6163934],"study_design_scores_gemma":[0.000013221512,0.000016536096,0.0013954816,0.0000032981106,0.000017477014,0.00004375214,0.0000068308923,0.99323493,0.0038898378,0.0006681149,0.0007008849,0.000009565861],"about_ca_topic_score_codex":0.0050204117,"about_ca_topic_score_gemma":0.0062776227,"teacher_disagreement_score":0.0050204117,"about_ca_system_score_codex":0.00017272992,"about_ca_system_score_gemma":0.0006061508,"threshold_uncertainty_score":0.0099823475},"labels":[],"label_agreement":null},{"id":"W2956697767","doi":"10.1016/j.rse.2019.04.030","title":"Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress","year":2019,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":741,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Australian Research Council; Goddard Space Flight Center; European Space Agency; National Aeronautics and Space Administration","keywords":"Remote sensing; Chlorophyll fluorescence; Photochemical Reflectance Index; Radiative transfer; Environmental science; Vegetation (pathology); Remote sensing application; Computer science; Hyperspectral imaging; Physics; Geology; Fluorescence; Optics","score_opus":0.008870609504127488,"score_gpt":0.21147120888926244,"score_spread":0.20260059938513494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2956697767","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.124302976,0.84215313,0.020347154,0.00247775,0.0004799789,0.00024717292,0.0013054353,0.00032811004,0.008358336],"genre_scores_gemma":[0.31458715,0.58730894,0.08252972,0.0028358176,0.0017624996,0.00027209768,0.0061387825,0.00020984514,0.0043551475],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982134,0.00037137565,0.00013198247,0.00060431164,0.00050132343,0.00017765112],"domain_scores_gemma":[0.9907828,0.0050559896,0.0006140084,0.0005863364,0.0024708176,0.0004899917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020257857,0.0022949523,0.002220112,0.0023841453,0.00054110057,0.0028749355,0.0016152266,0.0017293814,0.001586851],"category_scores_gemma":[0.0061376933,0.00069694,0.0013102313,0.003746579,0.0021489605,0.0041941484,0.0018984437,0.001452928,0.00041927304],"study_design_candidate":"not_applicable","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":[0.0008058104,0.000863033,0.040309727,0.0054092947,0.0010567497,0.0000897601,0.0015960471,0.008418671,0.039978117,0.002707122,0.008075016,0.89069074],"study_design_scores_gemma":[0.00029284268,0.0037535308,0.36222863,0.006712007,0.0025647827,0.0006997731,0.0035849214,0.025357662,0.06566521,0.009113211,0.51938224,0.0006451846],"about_ca_topic_score_codex":0.018115,"about_ca_topic_score_gemma":0.01678127,"teacher_disagreement_score":0.020257857,"about_ca_system_score_codex":0.0016755259,"about_ca_system_score_gemma":0.002727665,"threshold_uncertainty_score":0.10713506},"labels":[],"label_agreement":null},{"id":"W2958784608","doi":"10.1016/j.rse.2019.111314","title":"Exploring SMAP and OCO-2 observations to monitor soil moisture control on photosynthetic activity of global drylands and croplands","year":2019,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; University of Toronto","funders":"Canadian Space Agency; Environment and Climate Change Canada","keywords":"Environmental science; Water content; Arid; Terrestrial ecosystem; Atmospheric sciences; Topsoil; Ecosystem; Limiting; Soil water; Soil science; Ecology; Geology","score_opus":0.0181383933941075,"score_gpt":0.19606195123183104,"score_spread":0.17792355783772354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2958784608","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9964194,0.00012200853,0.00096423127,0.00004130608,0.000010805404,0.000013421009,0.0010716005,0.00006339602,0.0012937593],"genre_scores_gemma":[0.9968359,0.000052122454,0.0019638774,0.000024943622,0.000011614994,0.000016405405,0.000897967,0.00001639896,0.00018073304],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999908,0.0000128115435,0.0000038245266,0.00003589401,0.00001814079,0.000021440816],"domain_scores_gemma":[0.99977607,0.00007368841,0.000037177204,0.000024579147,0.000047630157,0.00004081953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003961756,0.00038271872,0.00025852697,0.00058879843,0.00027194998,0.00040037092,0.0003095123,0.00044164996,0.00058494834],"category_scores_gemma":[0.0003951574,0.00018995164,0.00026790623,0.0008634676,0.00015153906,0.0005578748,0.0002750931,0.00029521904,0.00010549464],"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":[0.0016226812,0.0004095726,0.51313823,0.00025799932,0.00034447445,0.00032151767,0.00048343372,0.02154134,0.4089889,0.0005779911,0.0015693547,0.050744623],"study_design_scores_gemma":[0.00010434312,0.00008425042,0.93981445,0.000012345175,0.00009079236,0.00005469887,0.00013400515,0.046267014,0.011760358,0.00015401894,0.0015032067,0.00002060273],"about_ca_topic_score_codex":0.016379315,"about_ca_topic_score_gemma":0.039222967,"teacher_disagreement_score":0.016379315,"about_ca_system_score_codex":0.0002813519,"about_ca_system_score_gemma":0.00039092347,"threshold_uncertainty_score":0.032567978},"labels":[],"label_agreement":null},{"id":"W2962218352","doi":"10.1016/j.rse.2019.111306","title":"Tropical bird species richness is strongly associated with patterns of primary productivity captured by the Dynamic Habitat Indices","year":2019,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Aeronautics and Space Administration","keywords":"Species richness; Biodiversity; Ecology; Productivity; Generalist and specialist species; Habitat; Species diversity; Global biodiversity; Biology; Geography","score_opus":0.010363667954044625,"score_gpt":0.1939728928897784,"score_spread":0.18360922493573378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2962218352","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99872464,0.00011663195,0.00029542137,0.000022798336,0.0000031882007,0.000001837771,0.00013009719,0.0000055908367,0.0006997293],"genre_scores_gemma":[0.99948883,0.000051727533,0.00012595476,0.000009033973,0.0000052392334,0.0000020872471,0.0001258953,0.0000035127855,0.00018767132],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99975723,0.00007272042,0.000020265188,0.00007450565,0.000034338475,0.000041016956],"domain_scores_gemma":[0.99706906,0.0009784148,0.0011739265,0.00021457941,0.00017216538,0.00039189926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032315784,0.00015964029,0.00023450982,0.0005462936,0.0003359687,0.00047991466,0.00019542112,0.00013577339,0.0025914847],"category_scores_gemma":[0.0021026665,0.00023512843,0.00023618287,0.00069847493,0.0003854008,0.00026856427,0.00041639266,0.0002454832,0.00020231762],"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":[0.000052798245,0.0000102132435,0.9966258,0.0000065838226,0.0000891906,0.000028540187,0.00006994037,0.000078228724,0.0017071569,0.000018097997,0.000034930807,0.0012785844],"study_design_scores_gemma":[5.485329e-7,0.00000794738,0.99968636,8.2971394e-7,0.0000064756337,0.000067477136,0.000043173182,0.000093116505,0.00004211564,0.000014632896,0.000036348352,0.0000011110012],"about_ca_topic_score_codex":0.005568396,"about_ca_topic_score_gemma":0.01791643,"teacher_disagreement_score":0.005568396,"about_ca_system_score_codex":0.00016872885,"about_ca_system_score_gemma":0.000134444,"threshold_uncertainty_score":0.01107192},"labels":[],"label_agreement":null},{"id":"W2963662460","doi":"10.1016/j.rse.2019.111290","title":"Probabilistic assessment of remote sensing-based terrestrial vegetation vulnerability to drought stress of the Loess Plateau in China","year":2019,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":237,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin; State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering; National Key Research and Development Program of China; Xi'an University of Technology; China Scholarship Council; China Institute of Water Resources and Hydropower Research; Ministry of Science and Technology of the People's Republic of China; Shaanxi Provincial Department of Water Resources; National Natural Science Foundation of China","keywords":"Normalized Difference Vegetation Index; Vegetation (pathology); Environmental science; Enhanced vegetation index; Physical geography; Desertification; Hydrology (agriculture); Climate change; Geography; Ecology; Geology; Vegetation Index","score_opus":0.008643668674853194,"score_gpt":0.24520162674250792,"score_spread":0.23655795806765473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963662460","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9990664,0.000043033764,0.0005809121,0.00001913535,7.7255686e-7,0.000004165373,0.0001119533,0.000011928811,0.0001617809],"genre_scores_gemma":[0.9997212,0.0000125780025,0.00010232384,0.000002207328,0.0000014908717,0.0000021548794,0.000113054,7.545548e-7,0.000044227258],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997638,0.00006248931,0.00001860532,0.00006581863,0.000042051633,0.000047217156],"domain_scores_gemma":[0.9991586,0.00035690924,0.0001929112,0.00005953737,0.000135352,0.00009672232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011813134,0.00036764648,0.00023468115,0.0016190175,0.00030949345,0.0005323754,0.00041052516,0.00036040464,0.0004098518],"category_scores_gemma":[0.0012768534,0.00025994817,0.0003974078,0.00068137277,0.00038227695,0.00046399917,0.0005252363,0.0000999125,0.00004642027],"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":[0.0002327045,0.00007836602,0.8239945,0.00004187948,0.00020300545,0.00035227434,0.00014704214,0.16046691,0.0031844433,0.00043397173,0.0002185177,0.0106462855],"study_design_scores_gemma":[0.000018782379,0.00006585861,0.55155975,0.0000069477514,0.00006998008,0.00007295733,0.0001554284,0.44694874,0.00046571344,0.00046974135,0.00014426194,0.000021787007],"about_ca_topic_score_codex":0.015078277,"about_ca_topic_score_gemma":0.019478656,"teacher_disagreement_score":0.015078277,"about_ca_system_score_codex":0.0008272325,"about_ca_system_score_gemma":0.0005236953,"threshold_uncertainty_score":0.029981017},"labels":[],"label_agreement":null},{"id":"W2965731873","doi":"10.1016/j.rse.2019.111344","title":"Diverse photosynthetic capacity of global ecosystems mapped by satellite chlorophyll fluorescence measurements","year":2019,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":116,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Canadian Space Agency; National Key Research and Development Program of China; Jet Propulsion Laboratory; National Natural Science Foundation of China","keywords":"Geography; Plant functional type; Remote sensing; Ecosystem; Atmospheric sciences; Mathematics; Physics; Ecology; Biology","score_opus":0.012263778394001404,"score_gpt":0.1847287423619339,"score_spread":0.17246496396793248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965731873","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.999706,0.000028523991,0.00007795226,0.000003100855,2.4975867e-7,3.9274198e-7,0.00006674889,0.000002453458,0.00011463323],"genre_scores_gemma":[0.99966013,0.000021512255,0.00009719141,0.0000031935072,5.5872187e-7,0.0000010296432,0.00018515522,0.0000020334237,0.000029173083],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999199,0.000014122276,0.000004294009,0.000031427404,0.000011205978,0.00001902429],"domain_scores_gemma":[0.9997913,0.00008603726,0.000041472984,0.000030856987,0.000023710234,0.000026575688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022248592,0.00019253786,0.00017956598,0.0006365066,0.00015522137,0.0002859688,0.00011128892,0.00015246811,0.00035828224],"category_scores_gemma":[0.00043610943,0.00014345397,0.00019125396,0.0007081156,0.00031089224,0.0004288923,0.00041228236,0.000106609165,0.000053359072],"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":[0.00049801765,0.00005416135,0.80508786,0.00005780542,0.0003058621,0.00014620552,0.0005360691,0.0049644313,0.17232838,0.00040053955,0.00010931664,0.015511286],"study_design_scores_gemma":[0.000004582843,0.00002486833,0.99680364,0.0000019397207,0.000027442356,0.00005829082,0.000108931505,0.0014442949,0.0012537694,0.00014026917,0.00012732093,0.0000046450564],"about_ca_topic_score_codex":0.0026381852,"about_ca_topic_score_gemma":0.0044764476,"teacher_disagreement_score":0.0026381852,"about_ca_system_score_codex":0.00019362,"about_ca_system_score_gemma":0.00010787352,"threshold_uncertainty_score":0.005245626},"labels":[],"label_agreement":null},{"id":"W2969890884","doi":"10.1016/j.rse.2019.111296","title":"Global 500 m clumping index product derived from MODIS BRDF data (2001–2017)","year":2019,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":96,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Chinese Academy of Sciences; National Natural Science Foundation of China; National Key Research and Development Program of China; National Aeronautics and Space Administration","keywords":"Bidirectional reflectance distribution function; Deciduous; Evergreen; Enhanced vegetation index; Environmental science; Leaf area index; Vegetation (pathology); Evapotranspiration; Normalized Difference Vegetation Index; Canopy; Remote sensing; Physical geography; Atmospheric sciences; Climatology; Vegetation Index; Geography; Reflectivity; Ecology; Geology","score_opus":0.020129069652344813,"score_gpt":0.2308405504289702,"score_spread":0.21071148077662538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969890884","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48712534,0.0017002549,0.0056282184,0.00052236236,0.0004496192,0.00016840408,0.48725465,0.0016181569,0.015533053],"genre_scores_gemma":[0.464546,0.0008540621,0.010077474,0.00021548555,0.00008063484,0.00013617547,0.5203944,0.0002902086,0.003405548],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999818,0.00001032614,0.000017989212,0.000051112813,0.000066336244,0.000036137037],"domain_scores_gemma":[0.99949884,0.000017453931,0.00009194326,0.00004897317,0.00031590654,0.000026862532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043010115,0.0006969692,0.00029139727,0.0019972126,0.00022444375,0.0004824458,0.00053392793,0.00049931277,0.0016720225],"category_scores_gemma":[0.0007086756,0.00024711763,0.00039189888,0.0031118605,0.00018321331,0.0008820485,0.00036686313,0.00035624584,0.0013588766],"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":[0.0014240053,0.00039499498,0.4335444,0.0020388481,0.00091505557,0.0007484772,0.00038418928,0.040870186,0.048817378,0.0026891823,0.28587255,0.18230073],"study_design_scores_gemma":[0.00011457314,0.00007292031,0.8528592,0.00013382928,0.00025655297,0.00026344418,0.00021660823,0.017821664,0.01169944,0.00052427826,0.11594865,0.00008883262],"about_ca_topic_score_codex":0.06917938,"about_ca_topic_score_gemma":0.054471243,"teacher_disagreement_score":0.06917938,"about_ca_system_score_codex":0.00079455203,"about_ca_system_score_gemma":0.001129799,"threshold_uncertainty_score":0.13755345},"labels":[],"label_agreement":null},{"id":"W2970054414","doi":"10.1016/j.rse.2019.111373","title":"Simulating emission and scattering of solar-induced chlorophyll fluorescence at far-red band in global vegetation with different canopy structures","year":2019,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":57,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Canopy; Satellite; Remote sensing; Environmental science; Scattering; Boreal ecosystem; Biosphere; Primary production; Atmospheric sciences; Chlorophyll fluorescence; Vegetation (pathology); Computational physics; Physics; Ecosystem; Boreal; Geology; Fluorescence; Geography; Optics; Ecology","score_opus":0.0051672054049947385,"score_gpt":0.19329175368155732,"score_spread":0.18812454827656258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970054414","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99789596,0.000016341424,0.0013205267,0.000027113243,0.0000054908082,0.0000042478187,0.00006480273,0.000029724984,0.00063578034],"genre_scores_gemma":[0.99881506,0.000013351675,0.0008030125,0.000009021195,0.000001713999,0.00000392473,0.000084471016,0.00000960543,0.00025977351],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999076,0.00001511167,0.0000038058743,0.000025505335,0.000011613578,0.000036444366],"domain_scores_gemma":[0.9996809,0.00019475381,0.000023362823,0.000021492442,0.000033223892,0.00004631277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024913606,0.00044282296,0.0003024127,0.00025099478,0.0004393269,0.00044222016,0.0006078401,0.0010858026,0.00097528164],"category_scores_gemma":[0.0005313043,0.00035121635,0.000942854,0.0003864845,0.0005052023,0.00049252826,0.0002651648,0.00044140648,0.000072037416],"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":[0.000065088236,0.00007421693,0.0054995907,0.000012047108,0.00002466873,0.00005344406,0.000027594624,0.98907286,0.004057681,0.00018353247,0.00004074248,0.0008884662],"study_design_scores_gemma":[0.000025020185,0.000030686962,0.0047142073,0.0000013835621,0.000011422676,0.000010255228,0.000033967335,0.9940103,0.0010428414,0.000079827856,0.000033189786,0.0000068832223],"about_ca_topic_score_codex":0.040959515,"about_ca_topic_score_gemma":0.02454038,"teacher_disagreement_score":0.040959515,"about_ca_system_score_codex":0.0010610987,"about_ca_system_score_gemma":0.00072422065,"threshold_uncertainty_score":0.08144218},"labels":[],"label_agreement":null},{"id":"W2972262484","doi":"10.1016/j.rse.2019.111380","title":"The SMAP and Copernicus Sentinel 1A/B microwave active-passive high resolution surface soil moisture product","year":2019,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":324,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"NASA Headquarters; California Institute of Technology; Jet Propulsion Laboratory; Massachusetts Institute of Technology","keywords":"Remote sensing; Radiometer; Radar; Environmental science; Synthetic aperture radar; L band; Microwave imaging; Microwave radiometer; Microwave; Meteorology; Geology; Computer science; Physics; Telecommunications","score_opus":0.005183438843909577,"score_gpt":0.18563663543539133,"score_spread":0.18045319659148176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972262484","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34567645,0.0016146424,0.050229277,0.0009203391,0.00064708135,0.00055428303,0.52573925,0.0070549473,0.06756371],"genre_scores_gemma":[0.5402344,0.000983548,0.06600928,0.00065286027,0.00020552684,0.0007002545,0.37226877,0.00085610367,0.018089347],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996458,0.000038959723,0.00001718563,0.000083314444,0.00017466898,0.00003998699],"domain_scores_gemma":[0.99912196,0.00008978501,0.00016869871,0.0001539577,0.0003840389,0.0000816499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005434992,0.00052828976,0.00022392222,0.0010342263,0.00019933208,0.00036823825,0.000500447,0.00040329059,0.004565496],"category_scores_gemma":[0.0015096817,0.0002200121,0.0002540481,0.0015321195,0.00017172031,0.00067570055,0.0004509713,0.00042327627,0.0027316317],"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":[0.0024050542,0.00035008436,0.13276577,0.0014571276,0.0006240607,0.00044325096,0.00022826332,0.047054213,0.1627051,0.005851094,0.24053791,0.40557808],"study_design_scores_gemma":[0.00033541265,0.00028082053,0.580033,0.00014033122,0.00020516528,0.0003968276,0.00019238492,0.08225915,0.037662983,0.0029980398,0.29532745,0.00016850333],"about_ca_topic_score_codex":0.011820499,"about_ca_topic_score_gemma":0.016122747,"teacher_disagreement_score":0.011820499,"about_ca_system_score_codex":0.0004058194,"about_ca_system_score_gemma":0.00072984997,"threshold_uncertainty_score":0.023503363},"labels":[],"label_agreement":null},{"id":"W2979662483","doi":"10.1016/j.rse.2019.111412","title":"Automating offshore infrastructure extractions using synthetic aperture radar &amp; Google Earth Engine","year":2019,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Oil Spill Detection and Mitigation","field":"Environmental Science","cited_by":48,"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":"Nippon Foundation; University of British Columbia","keywords":"Environmental science; Synthetic aperture radar; Offshore wind power; Submarine pipeline; Remote sensing; Earth observation; Wind power; Environmental resource management; Meteorology; Computer science; Geology; Oceanography; Geography; Satellite","score_opus":0.007666002356057106,"score_gpt":0.20735247154222886,"score_spread":0.19968646918617175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979662483","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37159133,0.00023245983,0.5904293,0.00026189533,0.00009852173,0.00027302667,0.0044519254,0.02770302,0.0049584997],"genre_scores_gemma":[0.7642062,0.00016347418,0.22887154,0.000093603194,0.000015745938,0.00009206522,0.0044045667,0.00029847387,0.0018543265],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998492,0.000011639244,0.0000114525665,0.000045177054,0.00005228171,0.000030229085],"domain_scores_gemma":[0.9998405,0.000030758016,0.00003367332,0.000027869775,0.000054835913,0.0000122687425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018609795,0.00086300157,0.00033656947,0.001286513,0.00023888926,0.00048493993,0.00052551297,0.0002576097,0.0013550674],"category_scores_gemma":[0.0005905823,0.00025888288,0.0005962478,0.00052872096,0.00014845507,0.00047721952,0.0007608696,0.00028980695,0.0010005868],"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":[0.0003146493,0.00021251148,0.058188833,0.00034599743,0.00026087335,0.0010200357,0.00048106714,0.23280127,0.08354601,0.0018136518,0.016474588,0.6045406],"study_design_scores_gemma":[0.000020800946,0.000038549635,0.017730532,0.00001651384,0.00003226467,0.00015168794,0.00017009431,0.95911676,0.01831835,0.00082602084,0.0035586243,0.000019885916],"about_ca_topic_score_codex":0.020459356,"about_ca_topic_score_gemma":0.022722354,"teacher_disagreement_score":0.020459356,"about_ca_system_score_codex":0.00026046988,"about_ca_system_score_gemma":0.0005659021,"threshold_uncertainty_score":0.040680528},"labels":[],"label_agreement":null},{"id":"W2980627149","doi":"10.1016/j.rse.2019.111400","title":"Mapping three decades of annual irrigation across the US High Plains Aquifer using Landsat and Google Earth Engine","year":2019,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":218,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Aeronautics and Space Administration; National Institute of Food and Agriculture; National Science Foundation","keywords":"Aquifer; Land cover; Remote sensing; Irrigation; Environmental science; Groundwater; Hydrology (agriculture); Land use; Geography; Geology","score_opus":0.007474108547512845,"score_gpt":0.20300871297839607,"score_spread":0.19553460443088322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980627149","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95401454,0.00018943768,0.0022456124,0.00022682258,0.000022944212,0.00003229678,0.039845373,0.00044678524,0.002976108],"genre_scores_gemma":[0.96064425,0.00020209598,0.005605496,0.000068497444,0.000016255484,0.000049003636,0.032371573,0.000045312092,0.0009976081],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99989057,0.000011629252,0.00001098536,0.00003354558,0.0000310443,0.000022329301],"domain_scores_gemma":[0.9997093,0.000029173263,0.000054340595,0.000028056404,0.0001534649,0.000025585974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022432375,0.00018929604,0.000116487114,0.0013396963,0.00018477046,0.0002713962,0.0002212832,0.0001600184,0.00089842884],"category_scores_gemma":[0.0005467036,0.00007807357,0.00024343377,0.002103656,0.00013138287,0.00035262902,0.0003777749,0.00018156934,0.00022847357],"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":[0.00016771028,0.00020849977,0.83912957,0.00015053581,0.00019030798,0.0003791523,0.00075399596,0.020341799,0.007360476,0.0009284237,0.036595695,0.09379376],"study_design_scores_gemma":[0.000016417016,0.000025890698,0.95609105,0.00003084673,0.000052437343,0.00007306043,0.0007276313,0.028171396,0.0016039317,0.00026007838,0.0129251685,0.000022057951],"about_ca_topic_score_codex":0.13173924,"about_ca_topic_score_gemma":0.23067369,"teacher_disagreement_score":0.13173924,"about_ca_system_score_codex":0.0004263353,"about_ca_system_score_gemma":0.00063324464,"threshold_uncertainty_score":0.2619449},"labels":[],"label_agreement":null},{"id":"W2989585671","doi":"10.1016/j.rse.2019.111479","title":"The global distribution of leaf chlorophyll content","year":2019,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":275,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Forest Research Institute; World Wildlife Fund Canada; Canada Research Chairs; McMaster University; University of Toronto","funders":"Australian Research Council","keywords":"Environmental science; Remote sensing; Atmospheric radiative transfer codes; Leaf area index; Evergreen; Canopy; Vegetation (pathology); Deciduous; Radiative transfer; Mean squared error; Atmospheric sciences; Chlorophyll; Mathematics; Botany; Geography; Geology; Biology","score_opus":0.009166896838988886,"score_gpt":0.19226533781114954,"score_spread":0.18309844097216066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2989585671","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9937609,0.0005805332,0.00087579846,0.00016162849,0.000006864551,0.0000031265279,0.0010505383,0.00004201051,0.0035185504],"genre_scores_gemma":[0.99777275,0.00023926007,0.0002886583,0.000034660563,0.000010228235,0.0000033136118,0.001088869,0.000016215807,0.00054609176],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99992836,0.000008361929,0.0000024712185,0.000039882227,0.0000082929255,0.000012515735],"domain_scores_gemma":[0.99970055,0.00008154727,0.00006718446,0.000041097795,0.000078895384,0.000030727097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021970227,0.0001860623,0.00014599838,0.00081118307,0.00010400654,0.00032116973,0.0001155956,0.00022609523,0.0014497394],"category_scores_gemma":[0.00034848013,0.00014072761,0.00020102602,0.001009659,0.0003287359,0.0005160204,0.00036867222,0.00021092469,0.0004124683],"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":[0.00033826448,0.000029542609,0.8449846,0.00008934983,0.00016170356,0.00012520197,0.00058835733,0.003513123,0.09846859,0.0012009318,0.0009447299,0.04955563],"study_design_scores_gemma":[0.0000031443292,0.000019100034,0.9978599,0.0000030405106,0.000010393088,0.000038922062,0.0000634188,0.0005396784,0.00044193398,0.00015804228,0.0008594446,0.0000029722846],"about_ca_topic_score_codex":0.0025741677,"about_ca_topic_score_gemma":0.0029140662,"teacher_disagreement_score":0.0025741677,"about_ca_system_score_codex":0.00027130617,"about_ca_system_score_gemma":0.00009856505,"threshold_uncertainty_score":0.0051183105},"labels":[],"label_agreement":null},{"id":"W2990924310","doi":"10.1016/j.rse.2019.111542","title":"L-Band response to freeze/thaw in a boreal forest stand from ground- and tower-based radiometer observations","year":2019,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":44,"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 British Columbia; University of Guelph; Environment and Climate Change Canada; Université du Québec à Trois-Rivières; Université de Sherbrooke; Center for Northern Studies; Université de Montréal","funders":"Canadian Space Agency","keywords":"Remote sensing; Taiga; Radiometer; Environmental science; Tower; Boreal; Meteorology; Geology; Geography; Forestry","score_opus":0.013415348331853207,"score_gpt":0.2109543795733046,"score_spread":0.1975390312414514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990924310","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99940884,0.000033074186,0.00018569287,0.0000046469,0.0000030851047,0.0000026257987,0.00014626302,0.000020673371,0.00019518156],"genre_scores_gemma":[0.99912804,0.000020965217,0.00033744177,0.000009948231,0.0000032482296,0.0000036357221,0.00043875302,0.0000028235645,0.000055098833],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999057,0.000010781609,0.000005456365,0.000037520065,0.000021326667,0.000019123228],"domain_scores_gemma":[0.99976605,0.00003888137,0.00007093087,0.000018185538,0.00006796634,0.000038066657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031019768,0.0002210011,0.0001636183,0.00031134428,0.0001861977,0.0003081121,0.00025361177,0.00021042612,0.00030001023],"category_scores_gemma":[0.00038725123,0.00010773556,0.00017413922,0.0002577355,0.00015761064,0.0002059697,0.00014768996,0.00015483717,0.00010807924],"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":[0.0006508336,0.00015494991,0.8774223,0.000060817376,0.00010796223,0.00022534998,0.00042979402,0.0031455634,0.095684424,0.000063339285,0.0005231094,0.021531539],"study_design_scores_gemma":[0.0000071757772,0.00005643389,0.9945642,0.0000028684458,0.00001768649,0.00006350488,0.000079159516,0.0037872246,0.0012570063,0.0000131103225,0.00014585388,0.0000057509446],"about_ca_topic_score_codex":0.0125972675,"about_ca_topic_score_gemma":0.024593996,"teacher_disagreement_score":0.0125972675,"about_ca_system_score_codex":0.00024442616,"about_ca_system_score_gemma":0.00012979051,"threshold_uncertainty_score":0.025047898},"labels":[],"label_agreement":null},{"id":"W2991052761","doi":"10.1016/j.rse.2019.111444","title":"Hydrological monitoring of high-latitude shallow water bodies from high-resolution space-borne D-InSAR","year":2019,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada; University of Victoria; Université de Sherbrooke","funders":"Canadian Space Agency","keywords":"Wetland; Interferometric synthetic aperture radar; Landform; Water level; Synthetic aperture radar; Latitude; Environmental science; Terrain; Remote sensing; Ecosystem; Hydrology (agriculture); Physical geography; Geology; Geography; Geomorphology; Ecology; Geodesy","score_opus":0.007261821590838625,"score_gpt":0.1865709287615832,"score_spread":0.17930910717074458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991052761","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9949529,0.00010526499,0.0029920416,0.000039107646,0.00001077156,0.000010519479,0.0006368684,0.00010224168,0.0011502611],"genre_scores_gemma":[0.9952055,0.00009029134,0.0035060404,0.000017746894,0.000011665477,0.0000062321337,0.0007178183,0.00000616035,0.0004385722],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999212,0.000009239227,0.0000043574696,0.000020137708,0.000027440377,0.000017635755],"domain_scores_gemma":[0.99986243,0.00001379338,0.000024689345,0.000021354788,0.000050210485,0.000027501535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018331973,0.00015350559,0.00022865838,0.0005082924,0.00023095397,0.0002848681,0.00017901431,0.00023225883,0.00041494714],"category_scores_gemma":[0.00018326573,0.00011477731,0.00013458636,0.00064922095,0.00014706377,0.00031693056,0.00028488383,0.0001235917,0.00016301285],"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":[0.00048646532,0.0005046403,0.48555443,0.00021483077,0.00019265075,0.00046143343,0.00047271492,0.036593754,0.32489765,0.0007200622,0.003804349,0.14609703],"study_design_scores_gemma":[0.000046898676,0.0001150028,0.89176995,0.00001182162,0.00010410216,0.00015008563,0.00022879805,0.087396145,0.017310457,0.0001993242,0.002637451,0.000029903495],"about_ca_topic_score_codex":0.0043355944,"about_ca_topic_score_gemma":0.013065527,"teacher_disagreement_score":0.0043355944,"about_ca_system_score_codex":0.0001692698,"about_ca_system_score_gemma":0.0003712352,"threshold_uncertainty_score":0.008620679},"labels":[],"label_agreement":null},{"id":"W2995497465","doi":"10.1016/j.rse.2019.111602","title":"A shadow constrained conditional generative adversarial net for SRTM data restoration","year":2019,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Shandong Province; National Natural Science Foundation of China; Leverhulme Trust","keywords":"Shuttle Radar Topography Mission; Computer science; Remote sensing; Artificial intelligence; Digital elevation model; Generative model; Shadow (psychology); Interpolation (computer graphics); Computer vision; Geology; Image (mathematics); Generative grammar","score_opus":0.017558073831531622,"score_gpt":0.22921356832806145,"score_spread":0.21165549449652982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995497465","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031053156,0.00017035502,0.99459475,0.00009663219,0.00006203214,0.00002751085,0.00008792061,0.0009421159,0.0009133082],"genre_scores_gemma":[0.282688,0.00047871374,0.7000924,0.0005926945,0.00017898327,0.0001948798,0.0010928593,0.00061019324,0.014071414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997055,0.00006295257,0.00001436807,0.00007539683,0.00009696945,0.000044662538],"domain_scores_gemma":[0.9995484,0.00020910836,0.00003331727,0.0000748647,0.000102570215,0.00003172263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000886539,0.00077493745,0.0009433392,0.00045060928,0.0002951059,0.00066783646,0.0018510151,0.0014395337,0.005357032],"category_scores_gemma":[0.0015356981,0.0005533548,0.00087125855,0.00053048856,0.0005528769,0.0008768423,0.0018162298,0.0020140123,0.0018587885],"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":[0.00016735157,0.00006769667,0.00024316719,0.00007915237,0.000075029704,0.000094279545,0.000042930933,0.7356448,0.006951493,0.007573176,0.005261177,0.24379973],"study_design_scores_gemma":[0.000002779773,0.000009924947,0.000022297509,0.0000031079148,0.000004381772,0.0000129460905,0.000001921979,0.9976198,0.0006486322,0.0012993017,0.00037151834,0.0000033461913],"about_ca_topic_score_codex":0.0056643193,"about_ca_topic_score_gemma":0.0076608164,"teacher_disagreement_score":0.0056643193,"about_ca_system_score_codex":0.00047223328,"about_ca_system_score_gemma":0.0010813212,"threshold_uncertainty_score":0.01792103},"labels":[],"label_agreement":null},{"id":"W2995687014","doi":"10.1016/j.rse.2019.111594","title":"Evolution of evapotranspiration models using thermal and shortwave remote sensing data","year":2019,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":327,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Evapotranspiration; Shortwave; Remote sensing; Environmental science; Latent heat; Vegetation (pathology); Shortwave radiation; Canopy; Canopy conductance; Sensible heat; Energy balance; Transpiration; Meteorology; Geography; Radiative transfer; Vapour Pressure Deficit","score_opus":0.025501335457054897,"score_gpt":0.21466080279467853,"score_spread":0.18915946733762362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995687014","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9946228,0.00012450403,0.0013155935,0.00041876896,0.00004106185,0.000008037507,0.0013587602,0.00015854089,0.001952042],"genre_scores_gemma":[0.99760395,0.00003982395,0.00095172005,0.00002557547,0.000005401106,0.0000055369733,0.00087117625,0.000039614028,0.0004572778],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998323,0.00004941401,0.000011237367,0.000056241577,0.000021543761,0.000029271781],"domain_scores_gemma":[0.99761057,0.0014822175,0.00021194734,0.00017054907,0.00037469072,0.00014999989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010204532,0.00050538284,0.00028005554,0.0006009074,0.000443259,0.0008851282,0.0006828571,0.0012799712,0.0021195142],"category_scores_gemma":[0.0041727107,0.0005208161,0.00067623396,0.0005607987,0.00042909657,0.0010848297,0.0002931542,0.0008008071,0.00037926724],"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":[0.00031662363,0.00015662708,0.06814883,0.000045015986,0.00015095538,0.00017436825,0.00009012359,0.9142071,0.007532841,0.0020394903,0.0024601324,0.0046779364],"study_design_scores_gemma":[0.000029262681,0.000024045405,0.018184947,0.0000059975278,0.00002113386,0.000029707462,0.00003646345,0.9793649,0.0015263746,0.00042381426,0.00033683985,0.00001638266],"about_ca_topic_score_codex":0.032306556,"about_ca_topic_score_gemma":0.026283702,"teacher_disagreement_score":0.032306556,"about_ca_system_score_codex":0.0017780361,"about_ca_system_score_gemma":0.00061411975,"threshold_uncertainty_score":0.064237},"labels":[],"label_agreement":null},{"id":"W3000080835","doi":"10.1016/j.rse.2019.111626","title":"Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models","year":2020,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":271,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Universität Zürich; European Space Agency; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Anthropocene; Environmental resource management; Biodiversity; Ecosystem services; Safeguarding; Disturbance (geology); Distribution (mathematics); Key (lock); Remote sensing; Land use; Ecosystem; Ecology; Environmental planning; Geography; Environmental science","score_opus":0.08299729290872769,"score_gpt":0.25007059462166453,"score_spread":0.16707330171293683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000080835","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97973675,0.00021326741,0.018547203,0.00013783386,0.000007749641,0.000008472225,0.00029811155,0.00008800042,0.00096262526],"genre_scores_gemma":[0.9927388,0.00010653374,0.006812993,0.000009738831,0.000004018498,0.000010232277,0.0001513451,0.000007516075,0.00015891703],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988616,0.000062077524,0.000004399003,0.00002277812,0.000012515064,0.000012069642],"domain_scores_gemma":[0.99966526,0.00019258099,0.000058878577,0.00003605916,0.000025194051,0.00002207016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059155497,0.0002443369,0.000185186,0.0005538107,0.00022584554,0.00032082302,0.00032291026,0.00025954028,0.0007331637],"category_scores_gemma":[0.001284135,0.0001830537,0.00026541573,0.0006482157,0.00021502197,0.0008059461,0.00028591565,0.00019735035,0.00006984646],"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":[0.00014043812,0.00013995157,0.19358262,0.000051066134,0.00022138415,0.000070858696,0.00019438841,0.75150263,0.002139321,0.00208996,0.00057965814,0.04928775],"study_design_scores_gemma":[0.000017317367,0.000041998155,0.05784522,0.000009376784,0.000031649397,0.000035277433,0.00008194102,0.93908596,0.0005117989,0.0017867516,0.00054418755,0.0000085369265],"about_ca_topic_score_codex":0.016832141,"about_ca_topic_score_gemma":0.030619027,"teacher_disagreement_score":0.016832141,"about_ca_system_score_codex":0.00046136277,"about_ca_system_score_gemma":0.0002670295,"threshold_uncertainty_score":0.033468366},"labels":[],"label_agreement":null},{"id":"W3004051460","doi":"10.1016/j.rse.2020.111675","title":"Land surface phenology in the highland pastures of montane Central Asia: Interactions with snow cover seasonality and terrain characteristics","year":2020,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":78,"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":"National Aeronautics and Space Administration","keywords":"Phenology; Normalized Difference Vegetation Index; Snow; Land cover; Environmental science; Seasonality; Physical geography; Vegetation (pathology); Terrain; Climate change; Digital elevation model; Climatology; Remote sensing; Geography; Land use; Ecology; Meteorology; Geology","score_opus":0.009064621914755013,"score_gpt":0.19812618198429402,"score_spread":0.189061560069539,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004051460","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99971026,0.000043918226,0.000029281024,0.000009759573,5.841871e-7,8.6325247e-7,0.00008948362,0.000002623759,0.00011307324],"genre_scores_gemma":[0.9996594,0.000027306549,0.000042704793,0.000005187615,0.00000154255,0.0000015097326,0.00018649964,0.000001806977,0.000074027754],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998938,0.000019383859,0.000007893853,0.00003769647,0.000013855344,0.000027426282],"domain_scores_gemma":[0.9995116,0.00012813899,0.00015969771,0.000037953963,0.000060709695,0.00010187566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033273431,0.000183012,0.00015763995,0.00048697347,0.00022111936,0.0006146251,0.00016413584,0.00013849093,0.00054509426],"category_scores_gemma":[0.0005872023,0.00010962101,0.00031864384,0.0006689025,0.00021311239,0.0002882517,0.00025728447,0.0001399624,0.000074296106],"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":[0.00003058128,0.000014387623,0.9960777,0.0000066611015,0.00004604983,0.00006963132,0.000220248,0.0003854525,0.0017253743,0.000024982002,0.00004953839,0.0013493146],"study_design_scores_gemma":[3.8315522e-7,0.0000049034325,0.9992817,0.0000010014786,0.0000044574576,0.00001574052,0.00011343954,0.00049262034,0.00003519308,0.00000554158,0.00004424506,8.746188e-7],"about_ca_topic_score_codex":0.037087344,"about_ca_topic_score_gemma":0.059036456,"teacher_disagreement_score":0.037087344,"about_ca_system_score_codex":0.0003880476,"about_ca_system_score_gemma":0.00027251447,"threshold_uncertainty_score":0.073742986},"labels":[],"label_agreement":null},{"id":"W3005813145","doi":"10.1016/j.rse.2020.111697","title":"Have satellite precipitation products improved over last two decades? A comprehensive comparison of GPM IMERG with nine satellite and reanalysis datasets","year":2020,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":629,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Global Water Futures; National Natural Science Foundation of China","keywords":"Global Precipitation Measurement; Environmental science; Satellite; Precipitation; Snow; Scale (ratio); Meteorology; Climatology; Geography; Geology","score_opus":0.03225247623199527,"score_gpt":0.2495503031227362,"score_spread":0.21729782689074095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005813145","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9461266,0.014186572,0.0015689974,0.0035685147,0.0002626112,0.00003768049,0.028888207,0.00013405143,0.005226756],"genre_scores_gemma":[0.9708528,0.0050169136,0.0021481498,0.00085912517,0.00020888101,0.000026658807,0.019952646,0.00010266067,0.00083217345],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983193,0.00037620697,0.00022444045,0.0004927214,0.00035441498,0.00023287068],"domain_scores_gemma":[0.9944554,0.0012900855,0.0014082856,0.0007130062,0.001969689,0.00016363163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0067837643,0.00045663334,0.00066135166,0.0016992739,0.00030232995,0.0019312779,0.0006349062,0.00088084035,0.0010951656],"category_scores_gemma":[0.010695877,0.00032392683,0.0010186689,0.0051127058,0.00041451858,0.0032505982,0.0009876287,0.00073902664,0.00049073854],"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":[0.0005024096,0.000083226914,0.9136882,0.0005337025,0.0016517045,0.0001782171,0.00046803916,0.0024950188,0.0028574872,0.0008133751,0.0055787205,0.07114994],"study_design_scores_gemma":[0.000012142603,0.00005302588,0.9896164,0.00008896991,0.00023392904,0.00005872364,0.00019216968,0.00074606424,0.00064748595,0.000075665645,0.008262602,0.000012923403],"about_ca_topic_score_codex":0.0355516,"about_ca_topic_score_gemma":0.053161584,"teacher_disagreement_score":0.0355516,"about_ca_system_score_codex":0.0013261763,"about_ca_system_score_gemma":0.0014697851,"threshold_uncertainty_score":0.07068932},"labels":[],"label_agreement":null},{"id":"W3011014351","doi":"10.1016/j.rse.2020.111736","title":"The spatio-temporal patterns of landfast ice in Antarctica during 2006–2011 and 2016–2017 using high-resolution SAR imagery","year":2020,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada","funders":"Antarctic Climate and Ecosystems Cooperative Research Centre; National Natural Science Foundation of China","keywords":"Sea ice; Geology; Iceberg; Ice shelf; Ice stream; Remote sensing; Oceanography; Physical geography; Cryosphere; Geography","score_opus":0.013543462130831595,"score_gpt":0.19092230215537645,"score_spread":0.17737884002454485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011014351","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9788815,0.00080561015,0.000106706415,0.00021710266,0.000045807803,0.000007748785,0.018738113,0.000023673592,0.0011736301],"genre_scores_gemma":[0.9815518,0.00075528846,0.00032464266,0.00008009609,0.000043576656,0.000016829787,0.016176686,0.000010624866,0.0010404494],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998247,0.000014679078,0.000030008985,0.00004507972,0.000034495326,0.00005112412],"domain_scores_gemma":[0.9992743,0.000071057286,0.00036730053,0.00003645256,0.00015215155,0.0000988502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038317195,0.0003377451,0.00015169663,0.00238053,0.00030738037,0.000797892,0.00027189657,0.00044689223,0.0010033379],"category_scores_gemma":[0.00070629583,0.00015836055,0.00049571285,0.0027175548,0.00039014223,0.00060433167,0.0005602703,0.00027177887,0.00032653354],"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":[0.0002486539,0.000034147928,0.9806074,0.00019914807,0.0003147861,0.0003087023,0.00076136476,0.0015589047,0.0026596643,0.00024208841,0.003962739,0.009102467],"study_design_scores_gemma":[0.0000023285434,0.000007270612,0.99617624,0.000027899134,0.000031196898,0.00008153218,0.00046662567,0.0003919363,0.00015678057,0.000018244427,0.0026339397,0.000006002813],"about_ca_topic_score_codex":0.072859466,"about_ca_topic_score_gemma":0.16656853,"teacher_disagreement_score":0.072859466,"about_ca_system_score_codex":0.00072067324,"about_ca_system_score_gemma":0.00067436014,"threshold_uncertainty_score":0.14487076},"labels":[],"label_agreement":null},{"id":"W3011712375","doi":"10.1016/j.rse.2020.111746","title":"A new approach for modeling near surface temperature lapse rate based on normalized land surface temperature data","year":2020,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":51,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Remote sensing; Mean squared error; Land cover; Digital elevation model; Advanced Spaceborne Thermal Emission and Reflection Radiometer; Environmental science; Scale (ratio); Data set; Root mean square; Coefficient of determination; Elevation (ballistics); Geology; Mathematics; Cartography; Geography; Statistics; Land use; Geometry","score_opus":0.031286821483360136,"score_gpt":0.22814851799701183,"score_spread":0.1968616965136517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011712375","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023734521,0.00016844392,0.97301,0.000082644605,0.000149717,0.000046241967,0.00026589003,0.0008014771,0.0017411382],"genre_scores_gemma":[0.57735765,0.00056378276,0.41357383,0.000120263845,0.00018443065,0.00029870775,0.00088092993,0.0005290219,0.006491374],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997967,0.000030231646,0.000014884874,0.000073829004,0.000068062545,0.000016365555],"domain_scores_gemma":[0.9997501,0.00007322087,0.000028608478,0.000033882618,0.000097518256,0.000016768952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048144616,0.00074644893,0.0006638349,0.0006036245,0.00044016464,0.0008287683,0.0017183627,0.0008143779,0.00088322506],"category_scores_gemma":[0.0010840069,0.00049708225,0.0009203663,0.00078352686,0.00030970568,0.0011415705,0.00051649526,0.0011387423,0.0003345668],"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":[0.000016742399,0.000042698455,0.0017294921,0.000028460858,0.000075842945,0.000041262043,0.000035080946,0.9698696,0.0066857357,0.0037994583,0.00043414952,0.017241433],"study_design_scores_gemma":[0.0000013073279,0.000002390788,0.00010533788,9.090015e-7,0.000004310738,0.0000049358373,0.000001509926,0.9989403,0.00038071658,0.00025048628,0.00030447726,0.000003430523],"about_ca_topic_score_codex":0.03345494,"about_ca_topic_score_gemma":0.030906267,"teacher_disagreement_score":0.03345494,"about_ca_system_score_codex":0.0007848267,"about_ca_system_score_gemma":0.00095872645,"threshold_uncertainty_score":0.06652045},"labels":[],"label_agreement":null},{"id":"W3012477797","doi":"10.1016/j.rse.2020.111750","title":"Characterizing marsh wetlands in the Great Lakes Basin with C-band InSAR observations","year":2020,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":44,"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","funders":"Canadian Space Agency","keywords":"Interferometric synthetic aperture radar; Wetland; Marsh; Water level; Environmental science; Remote sensing; Synthetic aperture radar; Hydrology (agriculture); Geology; Geography; Ecology","score_opus":0.01751792501860392,"score_gpt":0.18928198132333637,"score_spread":0.17176405630473246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012477797","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99851364,0.00003688174,0.00090557005,0.000016810975,0.000001003331,0.0000063761336,0.000102687656,0.000019826615,0.00039732386],"genre_scores_gemma":[0.99641955,0.000055984907,0.0030970748,0.000011289398,0.0000022370803,0.0000055369533,0.00024822183,0.0000036690476,0.00015647363],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999062,0.000020255298,0.0000059830204,0.00002325626,0.00002441993,0.000019883522],"domain_scores_gemma":[0.99983597,0.00003203747,0.000039893683,0.000018162922,0.000054327476,0.000019495526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024865125,0.00020074427,0.00010037459,0.0006621766,0.00016402632,0.00033775443,0.00012516756,0.00013600249,0.00015621306],"category_scores_gemma":[0.00039543925,0.000110825604,0.00010746613,0.00073531107,0.00014735517,0.00034422844,0.00022903038,0.000072787116,0.0000600129],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007653238,0.000092653034,0.8867488,0.00006398386,0.00006676858,0.0003128218,0.00036282948,0.012627265,0.04801743,0.00015299916,0.00053210394,0.05094581],"study_design_scores_gemma":[0.000007661904,0.00003163143,0.94891006,0.0000057692637,0.000023744815,0.00005873441,0.00031317983,0.047963824,0.0021008032,0.00007633981,0.0004969126,0.000011238817],"about_ca_topic_score_codex":0.04157388,"about_ca_topic_score_gemma":0.13886347,"teacher_disagreement_score":0.9584261,"about_ca_system_score_codex":0.00021799249,"about_ca_system_score_gemma":0.00041999947,"threshold_uncertainty_score":0.082663774},"labels":[],"label_agreement":null},{"id":"W3025204457","doi":"10.1016/j.rse.2020.111872","title":"Recent trends and remaining challenges for optical remote sensing of Arctic tundra vegetation: A review and outlook","year":2020,"lang":"en","type":"review","venue":"Remote Sensing of Environment","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":208,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Horizon 2020; European Commission","keywords":"Tundra; Remote sensing; Vegetation (pathology); Permafrost; Environmental science; Arctic; Normalized Difference Vegetation Index; Comparability; Arctic vegetation; Climate change; Environmental resource management; Physical geography; Geography; Ecology","score_opus":0.10644265555920605,"score_gpt":0.2957539104845266,"score_spread":0.18931125492532053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3025204457","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022703355,0.9987207,0.00013102555,0.0005429338,0.00012569556,0.0000051443535,0.000041438845,0.00000497218,0.00020102189],"genre_scores_gemma":[0.0010197676,0.9980058,0.00037314696,0.0003387777,0.00013392388,0.000008687049,0.000048115042,0.0000019515678,0.0000697818],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991308,0.00018698127,0.00027423984,0.0001525807,0.00020780184,0.00004761558],"domain_scores_gemma":[0.99105865,0.006349873,0.0009786612,0.00010348692,0.0013672126,0.0001420915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035440945,0.0007481338,0.0015243203,0.005573983,0.0003999086,0.0019004018,0.0011938814,0.0012438755,0.0020608548],"category_scores_gemma":[0.0062517077,0.00039416115,0.0016050738,0.0058275764,0.00069269317,0.002869788,0.0007943027,0.001183437,0.00040404775],"study_design_candidate":"not_applicable","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":[0.000100423415,0.000047473142,0.0017142004,0.20956539,0.00047787087,0.00029942306,0.0005474951,0.0008174273,0.0014768271,0.0032656328,0.020213218,0.76147467],"study_design_scores_gemma":[0.000019115065,0.00024110978,0.0097308075,0.17069232,0.0025593336,0.0013525434,0.001342405,0.00055178104,0.00079004175,0.0038722793,0.8087398,0.00010860613],"about_ca_topic_score_codex":0.0061330823,"about_ca_topic_score_gemma":0.011407359,"teacher_disagreement_score":0.0061330823,"about_ca_system_score_codex":0.0013115479,"about_ca_system_score_gemma":0.0053274124,"threshold_uncertainty_score":0.018743157},"labels":[],"label_agreement":null},{"id":"W3025536288","doi":"10.1016/j.rse.2020.111805","title":"Improved groundwater table and L-band brightness temperature estimates for Northern Hemisphere peatlands using new model physics and SMOS observations in a global data assimilation framework","year":2020,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Peatlands and Wetlands Ecology","field":"Environmental Science","cited_by":60,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Waterloo","funders":"Fonds Wetenschappelijk Onderzoek; Alexander von Humboldt-Stiftung; National Aeronautics and Space Administration","keywords":"Peat; Data assimilation; Environmental science; Water table; Northern Hemisphere; Boreal; Climate model; Groundwater; Climate change; Climatology; Atmospheric sciences; Hydrology (agriculture); Remote sensing; Meteorology; Geology; Geography","score_opus":0.04456872303495918,"score_gpt":0.2556109953410182,"score_spread":0.21104227230605904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3025536288","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9413368,0.00016108557,0.052811444,0.00019611097,0.00007322403,0.000026733389,0.0021787137,0.001398996,0.001816911],"genre_scores_gemma":[0.9719344,0.000047961537,0.025640877,0.000027535874,0.000016920734,0.000015169355,0.001626356,0.00010757263,0.0005832445],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998982,0.000014735617,0.000007964157,0.00003801444,0.000024300025,0.000016700988],"domain_scores_gemma":[0.99979395,0.00003571769,0.000024905092,0.000037466387,0.000087644104,0.000020359596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005111997,0.00040731014,0.00034779048,0.00046509088,0.00033029218,0.00049813377,0.0006056576,0.000517997,0.00079400674],"category_scores_gemma":[0.0009476981,0.00040573135,0.00059053395,0.00042717918,0.00018074317,0.0010590749,0.00039226533,0.00046033572,0.00025141155],"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":[0.00025913856,0.00024420687,0.04323316,0.00006798034,0.00020408395,0.00010437853,0.00018375272,0.81758755,0.05316494,0.0014333398,0.0021039327,0.08141348],"study_design_scores_gemma":[0.00005311241,0.000013049064,0.028018447,0.000006037207,0.000034795812,0.0000063474513,0.000022143271,0.96745247,0.00345051,0.00041738193,0.00049837946,0.000027370605],"about_ca_topic_score_codex":0.0871728,"about_ca_topic_score_gemma":0.14509186,"teacher_disagreement_score":0.0871728,"about_ca_system_score_codex":0.00065425626,"about_ca_system_score_gemma":0.0013171767,"threshold_uncertainty_score":0.17333078},"labels":[],"label_agreement":null},{"id":"W3025563967","doi":"10.1016/j.rse.2020.111858","title":"Comparing simple albedo scaling methods for estimating Arctic glacier mass balance","year":2020,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada; Queen's University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; W. Garfield Weston Foundation; ArcticNet; University of Ottawa","keywords":"Albedo (alchemy); Glacier; Glacier mass balance; Environmental science; Meltwater; Snow; Arctic; Atmospheric sciences; Climatology; Meteorology; Geology; Geography; Geomorphology","score_opus":0.05639571817640735,"score_gpt":0.2795051746695397,"score_spread":0.22310945649313238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3025563967","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91304266,0.0021621448,0.07902721,0.00020628858,0.00034535635,0.0001372956,0.00093354733,0.0007020041,0.0034434458],"genre_scores_gemma":[0.921265,0.0006770487,0.0750045,0.00007620674,0.00013136,0.00008368922,0.0013671106,0.00018497896,0.0012101468],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99824166,0.00084521493,0.00016245236,0.00029294039,0.00037568476,0.00008202396],"domain_scores_gemma":[0.98939997,0.0075133485,0.000399902,0.0008835504,0.0016804492,0.0001226111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005832248,0.0010761694,0.0007541656,0.0024887684,0.00053050724,0.0011560192,0.00089868874,0.00096535013,0.00128566],"category_scores_gemma":[0.015749885,0.0004072762,0.0011342041,0.0016379232,0.0004040724,0.0015964297,0.0010057057,0.0005687919,0.0003824618],"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":[0.0029687614,0.00073605863,0.15713905,0.00057517336,0.0019814176,0.00009346047,0.0005495955,0.3650665,0.010063152,0.0028475346,0.0025413174,0.4554379],"study_design_scores_gemma":[0.00016017302,0.00042839834,0.09959839,0.000050030587,0.0002802475,0.000052804946,0.00031488453,0.89194965,0.0039547416,0.0021458326,0.0009716392,0.0000931745],"about_ca_topic_score_codex":0.021113176,"about_ca_topic_score_gemma":0.017679648,"teacher_disagreement_score":0.021113176,"about_ca_system_score_codex":0.000758584,"about_ca_system_score_gemma":0.0005913884,"threshold_uncertainty_score":0.041980565},"labels":[],"label_agreement":null},{"id":"W3028374510","doi":"10.1016/j.rse.2020.111864","title":"A global near-real-time soil moisture index monitor for food security using integrated SMOS and SMAP","year":2020,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":85,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"National Aeronautics and Space Administration","keywords":"Environmental science; Remote sensing; Percentile; Meteorology; Bayesian probability; Statistics; Mathematics; Geography","score_opus":0.01180539657198666,"score_gpt":0.21839959977896523,"score_spread":0.20659420320697858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3028374510","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9608536,0.00022239728,0.01676252,0.00039060428,0.0001340976,0.00016026286,0.01266531,0.0024541728,0.006357022],"genre_scores_gemma":[0.9609021,0.00008907322,0.028908398,0.00014963918,0.000042994605,0.00009270332,0.0084973145,0.00008259291,0.0012352371],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998684,0.000016419806,0.000005320756,0.00004577237,0.000041389372,0.000022615422],"domain_scores_gemma":[0.9997961,0.000016013355,0.00003428974,0.000026372167,0.000082689556,0.000044511886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038132264,0.00054263143,0.00045873426,0.00069511257,0.00026535778,0.00034580694,0.00047923697,0.000515458,0.0011652269],"category_scores_gemma":[0.0002957825,0.0002650258,0.0003393539,0.00072245696,0.00013652365,0.0009154567,0.00053727074,0.00035849542,0.00044970427],"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":[0.0011794189,0.0016864389,0.41843635,0.00030978315,0.0008324619,0.00047518362,0.0002602674,0.09718213,0.22056517,0.0009341474,0.027002411,0.23113613],"study_design_scores_gemma":[0.0002152743,0.0003512502,0.51727957,0.0000243048,0.00020601864,0.00009856209,0.00017416068,0.46058634,0.012349033,0.00054771604,0.008097996,0.000069733134],"about_ca_topic_score_codex":0.009486386,"about_ca_topic_score_gemma":0.024702163,"teacher_disagreement_score":0.009486386,"about_ca_system_score_codex":0.00029069692,"about_ca_system_score_gemma":0.00056226464,"threshold_uncertainty_score":0.018862307},"labels":[],"label_agreement":null},{"id":"W3029159909","doi":"10.1016/j.rse.2020.111897","title":"Assessing satellite-derived fire patches with functional diversity trait methods","year":2020,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"U.S. Geological Survey; Canadian Forest Service; Université de Montpellier; European Space Agency","keywords":"Remote sensing; Environmental science; Satellite; Satellite imagery; Pixel; Fire regime; Disturbance (geology); Computer science; Physical geography; Geography; Ecology; Ecosystem; Geology; Artificial intelligence","score_opus":0.03504344905644637,"score_gpt":0.24149072589918572,"score_spread":0.20644727684273934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3029159909","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9766871,0.00015195904,0.021603875,0.000014314375,0.000005059594,0.000023186843,0.000937184,0.00009666651,0.00048079254],"genre_scores_gemma":[0.9804751,0.000042316828,0.018486599,0.000012251852,0.000007804569,0.00003433349,0.0008073393,0.000016988806,0.000117210024],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99897885,0.00039730393,0.00006389806,0.00030530666,0.000185275,0.000069349444],"domain_scores_gemma":[0.9947449,0.003114233,0.0010309309,0.0004948166,0.00046220125,0.00015292974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022306342,0.00070636254,0.000575158,0.0030953295,0.00030880317,0.0007769353,0.0005954397,0.0006546027,0.00078538485],"category_scores_gemma":[0.005342985,0.0002824315,0.00058844325,0.0021175244,0.00034578468,0.0008462387,0.0007564599,0.000261615,0.00021894946],"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":[0.00037318526,0.00018193218,0.90363926,0.00010599408,0.0009118571,0.000056542678,0.00020465527,0.027062897,0.01744426,0.00026597144,0.00023737099,0.049515978],"study_design_scores_gemma":[0.000045745895,0.00026743123,0.74565804,0.00002468964,0.00027608834,0.0003077981,0.0002940954,0.2462261,0.00549826,0.00091786566,0.000436688,0.000047089536],"about_ca_topic_score_codex":0.0055795284,"about_ca_topic_score_gemma":0.012030112,"teacher_disagreement_score":0.0055795284,"about_ca_system_score_codex":0.0004285874,"about_ca_system_score_gemma":0.00022918767,"threshold_uncertainty_score":0.011796832},"labels":[],"label_agreement":null},{"id":"W3033120841","doi":"10.1016/j.rse.2020.111919","title":"Machine learning approaches to retrieve pan-Arctic melt ponds from visible satellite imagery","year":2020,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":68,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"National Oceanic and Atmospheric Administration; Natural Environment Research Council; Sight Research UK; National Aeronautics and Space Administration","keywords":"Remote sensing; Melt pond; Moderate-resolution imaging spectroradiometer; Arctic; Mean squared error; Satellite; Environmental science; Satellite imagery; Sea ice; Geology; Climatology; Arctic ice pack; Oceanography; Sea ice thickness; Mathematics","score_opus":0.03799157577811767,"score_gpt":0.18635886620342212,"score_spread":0.14836729042530444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033120841","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27288288,0.0028526715,0.71427786,0.000821629,0.00023441829,0.0001457991,0.0010430724,0.0020947983,0.005646828],"genre_scores_gemma":[0.8346798,0.00078011776,0.15673,0.00015923008,0.000308329,0.00011413364,0.0017805742,0.0000798668,0.005367923],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979454,0.000035682948,0.000018906429,0.00006680371,0.00004621558,0.000037751226],"domain_scores_gemma":[0.9994696,0.00027636794,0.00007245371,0.0000434041,0.00011813016,0.00002020616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066903123,0.0006429505,0.000621079,0.0017076554,0.0004832321,0.0011017764,0.0010625867,0.0010103394,0.0010895696],"category_scores_gemma":[0.0016431106,0.00040858146,0.0008379682,0.0016277465,0.000363592,0.00089416606,0.0005437438,0.00093827466,0.00051212806],"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":[0.00012381517,0.00032832907,0.006926019,0.00010970662,0.00018490876,0.000072103685,0.00006507996,0.53461343,0.00918434,0.0016311015,0.0022798826,0.4444813],"study_design_scores_gemma":[0.0000058074456,0.000010497849,0.0011789871,0.0000048464804,0.000010220213,0.0000074217464,0.000013782209,0.99660987,0.0009242571,0.00096786383,0.00026209658,0.0000044036137],"about_ca_topic_score_codex":0.010823746,"about_ca_topic_score_gemma":0.012438519,"teacher_disagreement_score":0.010823746,"about_ca_system_score_codex":0.0006241505,"about_ca_system_score_gemma":0.0006898328,"threshold_uncertainty_score":0.021521509},"labels":[],"label_agreement":null},{"id":"W3035750522","doi":"10.1016/j.rse.2020.111911","title":"Influence of surface water on coarse resolution C-band backscatter: Implications for freeze/thaw retrieval from scatterometer data","year":2020,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Austrian Science Fund; National Fish and Wildlife Foundation; National Science Foundation","keywords":"Scatterometer; Remote sensing; Backscatter (email); Environmental science; Synthetic aperture radar; Permafrost; Satellite; Snow; Meteorology; Geology; Wind speed; Geography; Computer science","score_opus":0.08221375537385728,"score_gpt":0.254917171985607,"score_spread":0.1727034166117497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035750522","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9881121,0.00019809401,0.0096470425,0.00012500687,0.00003423053,0.000024766747,0.00052240567,0.000121427,0.0012149366],"genre_scores_gemma":[0.9968123,0.00012470779,0.002356486,0.00007625619,0.000009398767,0.0000074041996,0.0003097644,0.00006425146,0.00023939431],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996501,0.00008625966,0.000029249511,0.00006538469,0.0001058773,0.0000632385],"domain_scores_gemma":[0.99819857,0.0012751636,0.00009988478,0.0001275576,0.00025481213,0.00004410535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014296181,0.00026207237,0.00027206226,0.00031501346,0.0003680961,0.0006246661,0.00025717216,0.00038208824,0.0006528454],"category_scores_gemma":[0.005646399,0.00024421673,0.00034170004,0.0004254494,0.00046115922,0.00077122264,0.00033017917,0.00022937961,0.00014855151],"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":[0.0014637209,0.00024292961,0.39295924,0.000296115,0.00039879893,0.00052209344,0.0006612495,0.0949075,0.42884338,0.00091680506,0.0016635444,0.077124655],"study_design_scores_gemma":[0.000106491454,0.00014126324,0.7100237,0.000043626547,0.00022161093,0.00016221336,0.00024602888,0.19405252,0.09321079,0.0006102691,0.0011219548,0.000059424798],"about_ca_topic_score_codex":0.032198746,"about_ca_topic_score_gemma":0.047566492,"teacher_disagreement_score":0.032198746,"about_ca_system_score_codex":0.00040678767,"about_ca_system_score_gemma":0.00042416793,"threshold_uncertainty_score":0.06402266},"labels":[],"label_agreement":null},{"id":"W3037944168","doi":"10.1016/j.rse.2020.111954","title":"Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data","year":2020,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":415,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"European Regional Development Fund","keywords":"Remote sensing; Environmental science; Synthetic aperture radar; Vegetation (pathology); Radar; Leaf area index; Enhanced vegetation index; Polarimetry; Normalized Difference Vegetation Index; Vegetation Index; Geography; Computer science; Agronomy; Scattering; Telecommunications","score_opus":0.03706958007197232,"score_gpt":0.24857154505323248,"score_spread":0.21150196498126017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037944168","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9496774,0.00048286578,0.041202895,0.00009852692,0.000076435004,0.00003349797,0.0039318847,0.0007409631,0.003755482],"genre_scores_gemma":[0.9601552,0.00017306473,0.034629297,0.00003073523,0.000021082617,0.00002512604,0.0039006767,0.000039430615,0.0010252745],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9998977,0.000014816495,0.0000057824136,0.000030849915,0.000036491376,0.000014254402],"domain_scores_gemma":[0.99983776,0.0000271207,0.00002963137,0.00001736854,0.00007052207,0.000017519493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020837066,0.0002880325,0.0002395708,0.0008667281,0.00010768185,0.0002852366,0.00022314151,0.00016785717,0.00057166565],"category_scores_gemma":[0.00033686816,0.00013475856,0.00014433732,0.00053625286,0.000048239308,0.0003799827,0.00019707826,0.00018525877,0.00026582117],"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":[0.0008538738,0.0005612668,0.19000041,0.00027303628,0.00021754156,0.00016892012,0.00012477252,0.04567321,0.34366673,0.0011991573,0.009364863,0.40789616],"study_design_scores_gemma":[0.000052290907,0.00022733788,0.27669752,0.000021998072,0.00016368959,0.00018736918,0.00013373824,0.6751116,0.041694064,0.0006427952,0.0050173807,0.000050153998],"about_ca_topic_score_codex":0.0023009584,"about_ca_topic_score_gemma":0.0043959413,"teacher_disagreement_score":0.0023009584,"about_ca_system_score_codex":0.00016438283,"about_ca_system_score_gemma":0.0001898602,"threshold_uncertainty_score":0.004575193},"labels":[],"label_agreement":null},{"id":"W3045331167","doi":"10.1016/j.rse.2020.111968","title":"Landsat 9: Empowering open science and applications through continuity","year":2020,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Calibration and Measurement Techniques","field":"Engineering","cited_by":404,"is_retracted":false,"has_abstract":false,"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":"","keywords":"Remote sensing; Environmental science; Data quality; Earth observation; Spectral bands; Computer science; Satellite; Geology","score_opus":0.02804361928166454,"score_gpt":0.2538504478615524,"score_spread":0.22580682857988787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045331167","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07041771,0.0016238746,0.8262627,0.005616825,0.0010527029,0.00029153674,0.0036407372,0.021526368,0.06956755],"genre_scores_gemma":[0.39448223,0.0013112869,0.5646067,0.0015413443,0.00077663077,0.00021454805,0.007747126,0.0031463625,0.026173808],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99902356,0.00018446136,0.000045464203,0.00014850157,0.0004995941,0.00009839486],"domain_scores_gemma":[0.9977436,0.00052820053,0.00013471264,0.0006889457,0.00071460276,0.0001899153],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.00278197,0.00027077485,0.00022038457,0.00074807426,0.00039697668,0.0014788237,0.0011256492,0.0006467871,0.005127498],"category_scores_gemma":[0.003318877,0.00024587862,0.00018524128,0.0007456711,0.0008057,0.0032306013,0.0025114873,0.0014486146,0.0019906908],"study_design_candidate":"not_applicable","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":[0.00060346845,0.0005555151,0.012317104,0.00025271802,0.00008608389,0.00024086825,0.0011656645,0.008999878,0.08043354,0.100572996,0.11479761,0.67997456],"study_design_scores_gemma":[0.00032402153,0.00032809153,0.019583274,0.00015485108,0.000059310823,0.0005308224,0.0007073408,0.083416946,0.0555753,0.073519945,0.7656575,0.00014263619],"about_ca_topic_score_codex":0.0026896591,"about_ca_topic_score_gemma":0.0028530688,"teacher_disagreement_score":0.99887437,"about_ca_system_score_codex":0.00031828313,"about_ca_system_score_gemma":0.00082862185,"threshold_uncertainty_score":0.017153144},"labels":[],"label_agreement":null},{"id":"W3049568724","doi":"10.1016/j.rse.2020.112041","title":"Quantifying vertical profiles of biochemical traits for forest plantation species using advanced remote sensing approaches","year":2020,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":62,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Priority Academic Program Development of Jiangsu Higher Education Institutions; China Scholarship Council; National Natural Science Foundation of China; Nanjing Forestry University","keywords":"Hyperspectral imaging; Remote sensing; Canopy; Lidar; Environmental science; Vegetation (pathology); Photochemical Reflectance Index; Tree canopy; Leaf area index; Normalized Difference Vegetation Index; Biology; Ecology; Geography","score_opus":0.07688328596989483,"score_gpt":0.24849542581312373,"score_spread":0.1716121398432289,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3049568724","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99412906,0.00013586238,0.0047494746,0.000009440086,0.0000025215306,0.0000112030475,0.00041870735,0.00005016086,0.0004935967],"genre_scores_gemma":[0.993089,0.00007236577,0.0061773383,0.000015327118,0.0000023023006,0.000011588365,0.00046741593,0.000010450621,0.00015429156],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99990654,0.000009739489,0.000003574364,0.00004495105,0.000021667967,0.0000134515985],"domain_scores_gemma":[0.9998016,0.00006448951,0.000051524687,0.000015516825,0.000042589167,0.000024373056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019161904,0.00028315245,0.00020043844,0.00071255735,0.00022220946,0.00034821514,0.0002139614,0.00025291537,0.00043446606],"category_scores_gemma":[0.00027877098,0.0001689125,0.00025458107,0.0007891588,0.00013999925,0.00043999663,0.0002024498,0.00025059117,0.00011421165],"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":[0.00026751074,0.000136919,0.31940353,0.00009842897,0.00012923003,0.000039319002,0.0002672685,0.004372315,0.6437598,0.000228776,0.000109899534,0.031186994],"study_design_scores_gemma":[0.0000081518965,0.000109092296,0.9570178,0.000005283276,0.000044459124,0.000084366504,0.00018333348,0.01922016,0.022769043,0.00021014278,0.00032677554,0.000021538226],"about_ca_topic_score_codex":0.0090493355,"about_ca_topic_score_gemma":0.018146895,"teacher_disagreement_score":0.0090493355,"about_ca_system_score_codex":0.00031319933,"about_ca_system_score_gemma":0.00018439541,"threshold_uncertainty_score":0.017993331},"labels":[],"label_agreement":null},{"id":"W3049609438","doi":"10.1016/j.rse.2020.112039","title":"Satellite identification of atmospheric-surface-subsurface urban heat islands under clear sky","year":2020,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Fundamental Research Funds for the Central Universities; Nanjing University; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Daytime; Urban heat island; Environmental science; Atmospheric sciences; Altitude (triangle); Intensity (physics); Climatology; Satellite; Diurnal temperature variation; Atmosphere (unit); Planetary boundary layer; Seasonality; Meteorology; Geology; Geography","score_opus":0.01248114837567363,"score_gpt":0.20001763933182473,"score_spread":0.1875364909561511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3049609438","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.996707,0.000065836466,0.0010578427,0.00001930274,0.000009992417,0.0000053697095,0.000611846,0.000038947674,0.0014837695],"genre_scores_gemma":[0.99759537,0.00003640051,0.0011862298,0.000006266851,0.000006216315,0.000003255807,0.00078533,0.000004811082,0.00037606308],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99995613,0.000004403563,0.0000015331698,0.000009135243,0.000012262452,0.000016463915],"domain_scores_gemma":[0.9999199,0.000010555904,0.0000127315525,0.0000072326047,0.00003212455,0.000017406648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000094691604,0.0001432219,0.0001349983,0.00045685167,0.0001709189,0.0002493345,0.00012621122,0.00021555138,0.00055147405],"category_scores_gemma":[0.00015774055,0.00008006907,0.00011535148,0.0004472758,0.00010698699,0.00019833201,0.00018045939,0.00013402721,0.00011947946],"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":[0.0012274556,0.0002183258,0.5674968,0.00017368128,0.00023132938,0.00035042907,0.0006784436,0.041202363,0.29861727,0.0011384971,0.0043646614,0.08430079],"study_design_scores_gemma":[0.00001987405,0.000059974387,0.9393343,0.000008406893,0.000049674305,0.000058165664,0.0003054049,0.049893264,0.008922363,0.00012800247,0.0012085364,0.000012157388],"about_ca_topic_score_codex":0.018363254,"about_ca_topic_score_gemma":0.041738756,"teacher_disagreement_score":0.018363254,"about_ca_system_score_codex":0.00013547893,"about_ca_system_score_gemma":0.00029135062,"threshold_uncertainty_score":0.036512733},"labels":[],"label_agreement":null},{"id":"W3096213782","doi":"10.1016/j.rse.2020.112169","title":"Evaluating the capacity of single photon lidar for terrain characterization under a range of forest conditions","year":2020,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Natural Resources and Forestry; Ontario Forest Research Institute; Canadian Forest Service; Natural Resources Canada","funders":"","keywords":"Lidar; Remote sensing; Terrain; Digital elevation model; Ranging; Point cloud; Elevation (ballistics); Vegetation (pathology); Environmental science; Range (aeronautics); Footprint; Altitude (triangle); Reference data; Computer science; Geography; Cartography; Geodesy; Mathematics; Artificial intelligence; Database","score_opus":0.059004660318028465,"score_gpt":0.27796807458296646,"score_spread":0.21896341426493798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096213782","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99380517,0.00015632555,0.0032217158,0.000043791286,0.0000076237898,0.000030494548,0.0009330188,0.000090859496,0.0017109459],"genre_scores_gemma":[0.99193573,0.0001466833,0.006073101,0.00002106853,0.000008685491,0.000025720643,0.001555082,0.000018722463,0.00021531298],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994235,0.00007870911,0.000034030458,0.00013091684,0.00023653923,0.00009638429],"domain_scores_gemma":[0.9987513,0.0004800195,0.0001482678,0.0001502621,0.0004005992,0.00006956333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014011713,0.00037847098,0.0002936723,0.0011565991,0.00033597765,0.000830956,0.000601977,0.0005983049,0.00058999326],"category_scores_gemma":[0.0030307728,0.00016336124,0.00032129165,0.0011700401,0.00026448257,0.001489838,0.000504266,0.0003459527,0.00032726777],"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":[0.00077268406,0.00067037524,0.6720509,0.00034017736,0.00025338415,0.00040445285,0.00052076805,0.11428911,0.05237236,0.0006365498,0.0012358708,0.15645339],"study_design_scores_gemma":[0.000043767785,0.0006633563,0.6472533,0.00008903068,0.00016681825,0.00038079784,0.0012455416,0.3211275,0.024588313,0.0006400899,0.003703618,0.000097861484],"about_ca_topic_score_codex":0.009136362,"about_ca_topic_score_gemma":0.014647163,"teacher_disagreement_score":0.009136362,"about_ca_system_score_codex":0.00034914506,"about_ca_system_score_gemma":0.00031842123,"threshold_uncertainty_score":0.018166363},"labels":[],"label_agreement":null},{"id":"W3105998209","doi":"10.1016/j.rse.2020.112176","title":"Spectral subdomains and prior estimation of leaf structure improves PROSPECT inversion on reflectance or transmittance alone","year":2020,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":73,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Memorial University of Newfoundland; Agence Nationale de la Recherche","keywords":"Transmittance; Inversion (geology); Remote sensing; Reflectivity; Chlorophyll; Radiative transfer; Biological system; Atmospheric radiative transfer codes; Environmental science; Carotenoid; Computer science; Botany; Materials science; Biology; Optics; Geology; Physics; Optoelectronics","score_opus":0.00946980156124784,"score_gpt":0.20912715590930547,"score_spread":0.19965735434805765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3105998209","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3255837,0.00026621003,0.66389865,0.00031488086,0.00007225971,0.00004710943,0.0006957434,0.0022888863,0.006832619],"genre_scores_gemma":[0.80113083,0.00026241172,0.1926029,0.00014473617,0.000036548787,0.000053834014,0.0016508397,0.00039020166,0.0037276153],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998671,0.000033222695,0.000007209367,0.000043900953,0.00003148319,0.000017112656],"domain_scores_gemma":[0.99950767,0.00016125226,0.000035724628,0.00018342792,0.000087495595,0.000024422328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004083003,0.000626196,0.00033614907,0.00043154162,0.0002377071,0.00068047066,0.00037935472,0.0004801949,0.0022252824],"category_scores_gemma":[0.0015211782,0.0003513145,0.000500172,0.0004264627,0.00032719353,0.0013618908,0.00068449025,0.00091969076,0.00096638955],"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":[0.0010811521,0.0005222143,0.014614114,0.00020080364,0.00014210893,0.00012492629,0.0001789174,0.27806187,0.3324255,0.006707554,0.0033879073,0.3625529],"study_design_scores_gemma":[0.000033640397,0.000050110564,0.0075443634,0.000017013284,0.000047457896,0.000055693676,0.000047728365,0.9558269,0.03063609,0.0034591926,0.0022595318,0.000022446082],"about_ca_topic_score_codex":0.0030880016,"about_ca_topic_score_gemma":0.0073352223,"teacher_disagreement_score":0.0030880016,"about_ca_system_score_codex":0.00015591545,"about_ca_system_score_gemma":0.0005666551,"threshold_uncertainty_score":0.007444322},"labels":[],"label_agreement":null},{"id":"W3108249908","doi":"10.1016/j.rse.2020.112206","title":"Assessment of machine learning classifiers for global lake ice cover mapping from MODIS TOA reflectance data","year":2020,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":84,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Communitech","funders":"Natural Sciences and Engineering Research Council of Canada; European Space Agency","keywords":"Cloud cover; Environmental science; Remote sensing; Moderate-resolution imaging spectroradiometer; Solar zenith angle; Land cover; Cryosphere; Climatology; Satellite; Physical geography; Sea ice; Geology; Cloud computing; Geography; Computer science","score_opus":0.042165621247706474,"score_gpt":0.2531099171291437,"score_spread":0.21094429588143723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3108249908","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93938655,0.0040448573,0.045213364,0.0014638073,0.00028894434,0.0002704784,0.0016053306,0.0007080086,0.0070188106],"genre_scores_gemma":[0.97970974,0.00051521545,0.015368652,0.0001759182,0.00011192049,0.000083319996,0.0022917732,0.000050669805,0.001692817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99673706,0.0015469826,0.0002737295,0.0005026658,0.0007523567,0.0001871707],"domain_scores_gemma":[0.9626873,0.029682595,0.0009207535,0.0010297268,0.0052828286,0.00039681338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015714629,0.0012374953,0.00092355936,0.0015509785,0.00072241423,0.0018298626,0.0011872257,0.0022228546,0.0015403633],"category_scores_gemma":[0.029916536,0.00026936588,0.0008032301,0.00077606924,0.0004329921,0.0019618464,0.0009536635,0.0011355516,0.0008278894],"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":[0.0064512184,0.0016107748,0.11197239,0.00063727214,0.0012288511,0.00019433895,0.00026110196,0.45948848,0.0067829927,0.0017250094,0.009938166,0.39970946],"study_design_scores_gemma":[0.00007247519,0.0006586555,0.012550615,0.00006373032,0.00015341665,0.0000317132,0.00011647207,0.9815625,0.0032927452,0.00074134016,0.00073320593,0.000023122702],"about_ca_topic_score_codex":0.00827314,"about_ca_topic_score_gemma":0.0057639806,"teacher_disagreement_score":0.015714629,"about_ca_system_score_codex":0.0014203645,"about_ca_system_score_gemma":0.0010432613,"threshold_uncertainty_score":0.08310789},"labels":[],"label_agreement":null},{"id":"W3120378794","doi":"10.1016/j.rse.2020.112272","title":"SPLITSnow: A spectral light transport model for snow","year":2021,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":15,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Snow; Remote sensing; Environmental science; Context (archaeology); Snowpack; Radiometry; Light scattering; Bidirectional reflectance distribution function; Scattering; Computer science; Meteorology; Optics; Geology; Geography; Reflectivity; Physics","score_opus":0.023142809424966364,"score_gpt":0.2029275500420684,"score_spread":0.17978474061710203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120378794","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.117945984,0.00088459224,0.8359931,0.0009888718,0.00029561206,0.0003065746,0.0032263952,0.0016387721,0.03872003],"genre_scores_gemma":[0.8630991,0.0009870552,0.098036036,0.00035106274,0.00013795201,0.00087285787,0.0031442172,0.0005686135,0.03280307],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986875,0.00002644342,0.000006664344,0.000027019154,0.000042851683,0.00002820431],"domain_scores_gemma":[0.9997764,0.00009280484,0.000027128403,0.000011711927,0.00006671737,0.000025212308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026921462,0.0007863026,0.0006943075,0.00051837857,0.00074377586,0.0010318299,0.0018044696,0.0017813898,0.0048796255],"category_scores_gemma":[0.00081897003,0.00045899517,0.0011790375,0.0005964405,0.00055669266,0.0011553124,0.0010432398,0.0009882578,0.0006953027],"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":[0.000023218543,0.000026658756,0.0003575188,0.000021748243,0.00000883766,0.00004928222,0.000027268836,0.9881891,0.0012248768,0.0070763277,0.0006164456,0.0023787091],"study_design_scores_gemma":[0.000005387885,0.0000034841812,0.000028546796,0.0000017355399,0.0000015945631,0.0000045778525,0.0000038998137,0.9984016,0.00008909027,0.00094003545,0.0005175664,0.000002533051],"about_ca_topic_score_codex":0.026105903,"about_ca_topic_score_gemma":0.0153305065,"teacher_disagreement_score":0.026105903,"about_ca_system_score_codex":0.0012647881,"about_ca_system_score_gemma":0.001555159,"threshold_uncertainty_score":0.051907897},"labels":[],"label_agreement":null},{"id":"W3122663625","doi":"10.1016/j.rse.2020.112275","title":"Air pollution trends measured from Terra: CO and AOD over industrial, fire-prone, and background regions","year":2021,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":180,"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 Toronto; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency; Centre National d’Etudes Spatiales; Jet Propulsion Laboratory; Nuclear Safety and Security Commission; Centre National de la Recherche Scientifique; Belgian Federal Science Policy Office; National Science Foundation; European Space Agency; California Institute of Technology; Langley Research Center; National Aeronautics and Space Administration; National Center for Atmospheric Research; European Organization for the Exploitation of Meteorological Satellites","keywords":"Environmental science; Moderate-resolution imaging spectroradiometer; Trend analysis; Troposphere; Northern Hemisphere; Satellite; Air quality index; Aerosol; Climatology; Southern Hemisphere; Pollution; Spectroradiometer; Atmospheric sciences; Meteorology; Geography; Geology","score_opus":0.03574607700660009,"score_gpt":0.21688760106358268,"score_spread":0.18114152405698258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122663625","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98014235,0.00013681524,0.0002707062,0.00006255453,0.000014774102,0.000015175968,0.016372083,0.000072850205,0.002912751],"genre_scores_gemma":[0.9789382,0.00014269432,0.00092628185,0.00004204766,0.000029792267,0.000036804686,0.018887376,0.0000207439,0.0009761241],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99987423,0.000009074567,0.000018022922,0.000039894032,0.00003274141,0.000025962561],"domain_scores_gemma":[0.99947685,0.00002877521,0.00017522233,0.000034899087,0.00022260105,0.000061632956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021403159,0.0002655947,0.00014438701,0.0012006578,0.00022173412,0.00054301036,0.00022791463,0.00019221668,0.00090519676],"category_scores_gemma":[0.0004307472,0.00007782656,0.00021298035,0.0017654663,0.000095619034,0.00039584283,0.00025335775,0.00018995852,0.00028243716],"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":[0.000053947988,0.00004462748,0.9904819,0.00003966904,0.00006714775,0.000058111527,0.00015562431,0.00064219127,0.0016534514,0.000096790085,0.0013283608,0.005378171],"study_design_scores_gemma":[0.0000021818773,0.000012067876,0.997129,0.0000060830876,0.000015806567,0.0000382035,0.00017076987,0.00063119637,0.0005342878,0.000020169553,0.0014364606,0.0000036567944],"about_ca_topic_score_codex":0.042439327,"about_ca_topic_score_gemma":0.05757035,"teacher_disagreement_score":0.042439327,"about_ca_system_score_codex":0.00038361456,"about_ca_system_score_gemma":0.00032554416,"threshold_uncertainty_score":0.08438462},"labels":[],"label_agreement":null},{"id":"W3124933334","doi":"10.1016/j.rse.2021.112292","title":"Evaluating the temporal accuracy of grassland to cropland change detection using multitemporal image analysis","year":2021,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Grassland; Environmental science; Rangeland; Remote sensing; Climate change; Vegetation (pathology); Change detection; Phenology; Geography; Agroforestry; Ecology","score_opus":0.05840992718751432,"score_gpt":0.3193575086116776,"score_spread":0.2609475814241633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124933334","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98823327,0.00018625289,0.010119752,0.00004843268,0.000024312109,0.000017055516,0.00043060657,0.00015802239,0.00078235974],"genre_scores_gemma":[0.9891775,0.00008482769,0.009737952,0.000016941769,0.000011071611,0.000008090027,0.00063085643,0.00001973297,0.00031297398],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994179,0.00012627464,0.00006764669,0.00015818085,0.0001666423,0.00006329717],"domain_scores_gemma":[0.99567896,0.0024579938,0.000457436,0.00033371884,0.0009640952,0.000107803506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017918054,0.0003372557,0.00025814888,0.0014009544,0.0002089082,0.0008131764,0.0004285149,0.00069355353,0.0006643365],"category_scores_gemma":[0.006311965,0.00017978063,0.00041656912,0.00077409716,0.00021629747,0.0006663123,0.00027666258,0.00022655983,0.0001857652],"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":[0.0034839802,0.0008343395,0.5435345,0.00020389451,0.00088783266,0.00026741406,0.00020400208,0.14480866,0.074251376,0.00041248737,0.0011272756,0.22998428],"study_design_scores_gemma":[0.000043992608,0.0003998262,0.34994084,0.000015814323,0.00024364927,0.00018788688,0.00017777547,0.62862015,0.019514289,0.00019774325,0.0006245784,0.000033392254],"about_ca_topic_score_codex":0.015829524,"about_ca_topic_score_gemma":0.018480308,"teacher_disagreement_score":0.015829524,"about_ca_system_score_codex":0.00043486795,"about_ca_system_score_gemma":0.00038983475,"threshold_uncertainty_score":0.03147477},"labels":[],"label_agreement":null},{"id":"W3124954095","doi":"10.1016/j.rse.2021.112300","title":"Assessment of approaches for monitoring forest structure dynamics using bi-temporal digital aerial photogrammetry point clouds","year":2021,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Sustainable forest management; Photogrammetry; Environmental science; Remote sensing; Forest inventory; Digital elevation model; Forest management; Point cloud; Forest dynamics; Temporal resolution; Physical geography; Geography; Agroforestry; Computer science; Ecology","score_opus":0.024959715929468863,"score_gpt":0.25559699667226765,"score_spread":0.2306372807427988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124954095","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9105558,0.0022728695,0.07666803,0.00042941372,0.000056980734,0.00059416296,0.0022007022,0.000298942,0.00692309],"genre_scores_gemma":[0.94341105,0.00069490453,0.05362718,0.000039004648,0.0000149201815,0.00013995863,0.0011827213,0.000011901248,0.00087833434],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99840957,0.00047161678,0.00010211565,0.00021603086,0.00068986823,0.0001108918],"domain_scores_gemma":[0.996161,0.0018480588,0.00034642115,0.00025614788,0.0012439459,0.00014441926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056100287,0.00078729744,0.0004250434,0.0025627168,0.0005005629,0.0016997205,0.0009553928,0.001127387,0.0010059371],"category_scores_gemma":[0.006181309,0.00023980024,0.0006043377,0.0016037768,0.00031944775,0.002116636,0.001114616,0.00033628757,0.00020553732],"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":[0.0022640596,0.0016028641,0.27654767,0.001260294,0.0012153776,0.00023697302,0.00042658337,0.28818846,0.029300172,0.0029088573,0.0014429664,0.39460573],"study_design_scores_gemma":[0.00015052158,0.0022677917,0.18287899,0.00017184924,0.0006547532,0.00023140109,0.001070122,0.7990804,0.008481928,0.0016958064,0.0032395497,0.000076992255],"about_ca_topic_score_codex":0.018485684,"about_ca_topic_score_gemma":0.03341066,"teacher_disagreement_score":0.018485684,"about_ca_system_score_codex":0.001375357,"about_ca_system_score_gemma":0.0012256317,"threshold_uncertainty_score":0.036756217},"labels":[],"label_agreement":null},{"id":"W3127794790","doi":"10.1016/j.rse.2021.112632","title":"Long time-series NDVI reconstruction in cloud-prone regions via spatio-temporal tensor completion","year":2021,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":170,"is_retracted":false,"has_abstract":false,"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; National Natural Science Foundation of China","keywords":"Remote sensing; Normalized Difference Vegetation Index; Cloud computing; Series (stratigraphy); Computer science; Time series; Environmental science; Geology; Climate change","score_opus":0.01860957977906524,"score_gpt":0.2320983622633231,"score_spread":0.21348878248425784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127794790","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40350226,0.00045329792,0.5922597,0.00054758735,0.00012342067,0.000034789762,0.000709206,0.0009401575,0.0014296615],"genre_scores_gemma":[0.8172853,0.00050321594,0.1777545,0.00005124048,0.00005518306,0.0000249589,0.0013358735,0.00018458739,0.0028051627],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998952,0.000017362303,0.0000069219727,0.000028670072,0.000030680363,0.000021213114],"domain_scores_gemma":[0.99968815,0.00007213527,0.00005773811,0.00005576702,0.000090810805,0.000035457146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038828093,0.00053017336,0.0002890338,0.0004732765,0.00022615948,0.0005857976,0.00047513703,0.00047300544,0.00076298614],"category_scores_gemma":[0.00094867055,0.00022404181,0.00054111495,0.00063323346,0.000315439,0.0008057024,0.0003673508,0.0007289203,0.0003063041],"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":[0.00076032185,0.0003478631,0.015743788,0.00026651233,0.00020942197,0.0006039097,0.0003734287,0.5177689,0.20361736,0.010259105,0.005041903,0.24500759],"study_design_scores_gemma":[0.000005743852,0.000012315078,0.0025310998,0.0000042098127,0.00001151893,0.000036754253,0.000033402437,0.989899,0.005971582,0.00096956355,0.00051353854,0.000011254219],"about_ca_topic_score_codex":0.012463655,"about_ca_topic_score_gemma":0.012881743,"teacher_disagreement_score":0.012463655,"about_ca_system_score_codex":0.00029441377,"about_ca_system_score_gemma":0.0009163212,"threshold_uncertainty_score":0.02478224},"labels":[],"label_agreement":null},{"id":"W3129048830","doi":"10.1016/j.rse.2021.112317","title":"Performance of OLCI Sentinel-3A satellite in the Northeast Pacific coastal waters","year":2021,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":65,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Tula Foundation; University of British Columbia; University of Victoria","funders":"Hakai Institute; Marine Environmental Observation Prediction and Response Network","keywords":"Remote sensing; Satellite; Environmental science; Geology; Oceanography; Astronomy","score_opus":0.008787054693120397,"score_gpt":0.16647196840368153,"score_spread":0.15768491371056115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129048830","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99706644,0.000040492883,0.00022747758,0.0000641966,0.000011887641,0.0000059674053,0.0007134806,0.00006328534,0.0018067509],"genre_scores_gemma":[0.9972198,0.000048108563,0.0007115886,0.00003918602,0.000005640546,0.000007421963,0.0012928095,0.000013056745,0.0006623624],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998041,0.000018346072,0.000010793248,0.00005702608,0.000058251342,0.000051492294],"domain_scores_gemma":[0.99942005,0.000107932145,0.00006022972,0.000037258826,0.00028692425,0.0000876481],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005898808,0.0003294836,0.00027272553,0.0003581016,0.0004750098,0.0005273482,0.00029049022,0.0004752965,0.0007593152],"category_scores_gemma":[0.0010952083,0.00015399554,0.00019160278,0.0005236339,0.00025251275,0.000625758,0.0003900266,0.00022703232,0.00021152021],"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":[0.0013178153,0.00027450165,0.7921996,0.0001580387,0.000163555,0.0003752268,0.0008209921,0.07390965,0.069123015,0.0004886218,0.007693257,0.05347575],"study_design_scores_gemma":[0.00005199611,0.00027287204,0.84465516,0.000021572732,0.00009248135,0.000093665236,0.0008390767,0.14272009,0.008421323,0.00016504344,0.0026283627,0.000038468854],"about_ca_topic_score_codex":0.12916052,"about_ca_topic_score_gemma":0.13504232,"teacher_disagreement_score":0.12916052,"about_ca_system_score_codex":0.0006828175,"about_ca_system_score_gemma":0.0010842279,"threshold_uncertainty_score":0.25681746},"labels":[],"label_agreement":null},{"id":"W3129857656","doi":"10.1016/j.rse.2021.112339","title":"Multimodal deep learning from satellite and street-level imagery for measuring income, overcrowding, and environmental deprivation in urban areas","year":2021,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Impact of Light on Environment and Health","field":"Environmental Science","cited_by":94,"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":"Engineering and Physical Sciences Research Council; Imperial College London; UK Research and Innovation; Wellcome Trust; Medical Research Council; Russian Academy of Medical Sciences","keywords":"Overcrowding; Decile; Satellite imagery; Computer science; Remote sensing; Satellite; Deep learning; Pixel; Artificial intelligence; Environmental science; Geography; Cartography; Statistics; Mathematics","score_opus":0.022135382815023894,"score_gpt":0.22297673259464626,"score_spread":0.20084134977962237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129857656","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86789876,0.0010456747,0.12123061,0.0009953929,0.0001053556,0.000055407,0.0020277796,0.002267079,0.0043739458],"genre_scores_gemma":[0.97847456,0.00014534661,0.018025206,0.00015322966,0.000027833696,0.000025932726,0.0017773611,0.000030712814,0.0013399271],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997702,0.00005340269,0.0000100081315,0.00006726492,0.000035928344,0.00006319081],"domain_scores_gemma":[0.9997081,0.00011095293,0.000046085584,0.000029488894,0.00006922866,0.00003606242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047721612,0.0009011998,0.000499223,0.0007520119,0.00019197528,0.00048406035,0.0007953456,0.00059333676,0.0011194225],"category_scores_gemma":[0.0012208266,0.00025621374,0.00057963934,0.0007662282,0.00028646982,0.0007134298,0.0010030804,0.0007317907,0.00025366835],"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":[0.0004904764,0.00048577122,0.042910498,0.00015004167,0.00024289802,0.00028331298,0.00017208335,0.74754286,0.005813602,0.0010030343,0.006272768,0.1946327],"study_design_scores_gemma":[0.0000053158255,0.000023644794,0.003324883,0.000011535514,0.000014120782,0.00001078925,0.00003535389,0.9949137,0.0008963107,0.00057061063,0.00018771664,0.000005976413],"about_ca_topic_score_codex":0.018453492,"about_ca_topic_score_gemma":0.02150651,"teacher_disagreement_score":0.018453492,"about_ca_system_score_codex":0.0006549871,"about_ca_system_score_gemma":0.0004831946,"threshold_uncertainty_score":0.036692142},"labels":[],"label_agreement":null},{"id":"W3134465096","doi":"10.1016/j.rse.2021.112366","title":"ACIX-Aqua: A global assessment of atmospheric correction methods for Landsat-8 and Sentinel-2 over lakes, rivers, and coastal waters","year":2021,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":336,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada; Université du Québec à Rimouski","funders":"U.S. Geological Survey; European Commission; Sight Research UK; Japan Aerospace Exploration Agency; Natural Environment Research Council; Fundação de Amparo à Pesquisa do Estado de São Paulo; National Aeronautics and Space Administration","keywords":"Environmental science; AERONET; Atmospheric correction; Remote sensing; Range (aeronautics); Climatology; Satellite; Meteorology; Aerosol; Geography; Geology","score_opus":0.008830907115369428,"score_gpt":0.24698508854534068,"score_spread":0.23815418142997125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134465096","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6738771,0.005833883,0.21209778,0.0018032517,0.0005030918,0.0025415837,0.06018967,0.02273402,0.020419562],"genre_scores_gemma":[0.47465542,0.0016614922,0.43761325,0.00056469673,0.000092010116,0.001453847,0.07701968,0.0033882095,0.003551387],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9960581,0.0009799929,0.00024326266,0.00068306795,0.0018276934,0.00020796724],"domain_scores_gemma":[0.9922557,0.0015413547,0.0010917773,0.0014776227,0.0032860432,0.00034758725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017264811,0.0023518126,0.0007835993,0.003022406,0.0009439173,0.0015171763,0.002252593,0.0012256369,0.0016289295],"category_scores_gemma":[0.010469619,0.0006714157,0.0012231021,0.0037773098,0.0006507266,0.0023839728,0.0021402074,0.00086299336,0.00070437544],"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":[0.0022286011,0.0013335217,0.16628195,0.0016545502,0.0028403555,0.00022107731,0.0006693832,0.24032994,0.05310204,0.004192692,0.05369841,0.47344753],"study_design_scores_gemma":[0.0008903555,0.0017619258,0.2899648,0.0005725907,0.00074953894,0.0003432171,0.00092718663,0.56519127,0.047830813,0.0030103968,0.08829344,0.0004644928],"about_ca_topic_score_codex":0.037672512,"about_ca_topic_score_gemma":0.029715518,"teacher_disagreement_score":0.037672512,"about_ca_system_score_codex":0.0015450565,"about_ca_system_score_gemma":0.0025489756,"threshold_uncertainty_score":0.09130615},"labels":[],"label_agreement":null},{"id":"W3134766480","doi":"10.1016/j.rse.2021.112358","title":"Biophysical controls of increased tundra productivity in the western Canadian Arctic","year":2021,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Forest Service; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Arctic Institute of North America; Western Canada Research Grid; Compute Canada; University of Victoria; Government of Canada","keywords":"Tundra; Vegetation (pathology); Arctic vegetation; Arctic; Environmental science; Normalized Difference Vegetation Index; Physical geography; Greening; Climate change; Productivity; Microclimate; Shrub; Shrubland; Taiga; Enhanced vegetation index; Ecology; Ecosystem; Geography; Vegetation Index","score_opus":0.023772423534870905,"score_gpt":0.20928374426685237,"score_spread":0.18551132073198146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134766480","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9984244,0.00019148047,0.00010534786,0.000069919275,0.000002794278,0.000004101072,0.000422523,0.000010066633,0.00076946866],"genre_scores_gemma":[0.99951625,0.00008678514,0.00008486926,0.000010991694,0.000001291723,0.0000017275589,0.00014511852,0.0000021255703,0.00015087421],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997391,0.000026573665,0.0000130260205,0.00006251405,0.00006164807,0.000097207805],"domain_scores_gemma":[0.99940646,0.00006446981,0.0001369376,0.00003254998,0.00023817494,0.00012148161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035938324,0.00021454686,0.00017200792,0.0008896908,0.0009772982,0.00083385664,0.00033708438,0.00016175682,0.00069166115],"category_scores_gemma":[0.0010613186,0.00013651363,0.00024163564,0.0011648906,0.00053891324,0.00021588417,0.00038240675,0.00020326412,0.00005630286],"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":[0.00008388706,0.000026769041,0.98290783,0.000022652259,0.00008883625,0.00011453607,0.0005835769,0.0020865963,0.0054333294,0.00023673497,0.00030536778,0.008109821],"study_design_scores_gemma":[7.572807e-7,0.0000019934992,0.99891806,0.0000024963383,0.000006244487,0.000009192406,0.00020581394,0.00053560873,0.00007733694,0.000016774951,0.00022324683,0.0000025513439],"about_ca_topic_score_codex":0.9763149,"about_ca_topic_score_gemma":0.9881026,"teacher_disagreement_score":0.023685098,"about_ca_system_score_codex":0.0070819724,"about_ca_system_score_gemma":0.006807964,"threshold_uncertainty_score":0.051383555},"labels":[],"label_agreement":null},{"id":"W3138089561","doi":"10.1016/j.rse.2021.112402","title":"When image correlation is needed: Unravelling the complex dynamics of a slow-moving landslide in the tropics with dense radar and optical time series","year":2021,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Landslides and related hazards","field":"Environmental Science","cited_by":68,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada","funders":"Fonds National de la Recherche Luxembourg; California Institute of Technology; Jet Propulsion Laboratory; Belgian Federal Science Policy Office; National Aeronautics and Space Administration","keywords":"Landslide; Geology; Kinematics; Remote sensing; Radar; Geodesy; Geomorphology; Computer science","score_opus":0.006299157607784134,"score_gpt":0.18800824142015424,"score_spread":0.1817090838123701,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138089561","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9091453,0.0023656406,0.078002244,0.0053764195,0.000325886,0.000039324466,0.00050417177,0.00023588674,0.004005077],"genre_scores_gemma":[0.9782095,0.0013505862,0.019169994,0.0003120996,0.0002512507,0.000011668524,0.0002097535,0.000081497,0.00040366378],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99980825,0.000037466925,0.00001313503,0.00004902109,0.00004448255,0.00004775272],"domain_scores_gemma":[0.99866664,0.000601813,0.0003082599,0.00013598232,0.00018742146,0.00009997184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010395225,0.00034456156,0.00042691943,0.00066823664,0.00040118204,0.0020264748,0.00046275245,0.00088148314,0.0006898824],"category_scores_gemma":[0.008842477,0.0003982144,0.00020228735,0.0012553744,0.0008096344,0.003487833,0.0006769145,0.0010531012,0.00016368712],"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":[0.0006901126,0.00039463327,0.40319178,0.00067621755,0.00040914616,0.0021516522,0.0016707632,0.10021521,0.09390964,0.023124766,0.0131206205,0.3604455],"study_design_scores_gemma":[0.000048168356,0.00013058897,0.3066703,0.0001752457,0.00018052277,0.00076417846,0.0027836862,0.6267594,0.014481441,0.039041217,0.008841058,0.00012424885],"about_ca_topic_score_codex":0.0061136154,"about_ca_topic_score_gemma":0.013948666,"teacher_disagreement_score":0.0061136154,"about_ca_system_score_codex":0.00031095775,"about_ca_system_score_gemma":0.0007868939,"threshold_uncertainty_score":0.012156069},"labels":[],"label_agreement":null},{"id":"W3144990628","doi":"10.1016/j.rse.2021.112418","title":"Blinded evaluation of airborne methane source detection using Bridger Photonics LiDAR","year":2021,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":114,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada; BC Oil and Gas Research and Innovation Society","keywords":"Remote sensing; Environmental science; Lidar; Sensitivity (control systems); Wind speed; Methane; Computer science; Meteorology; Physics; Geology; Engineering","score_opus":0.027463152692732876,"score_gpt":0.2521888204440243,"score_spread":0.22472566775129144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3144990628","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9503659,0.00009525961,0.04616444,0.00007233725,0.00004605856,0.00018254113,0.00030788413,0.00034512673,0.0024203733],"genre_scores_gemma":[0.9794391,0.000042839554,0.01925257,0.00008868819,0.0000067750866,0.00012289123,0.00022482678,0.000053417898,0.0007687968],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99758935,0.0005022912,0.00011169017,0.00047029997,0.0011204527,0.00020586622],"domain_scores_gemma":[0.99595463,0.001545584,0.00057042704,0.0005329402,0.0012805583,0.00011579494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028207412,0.0004251611,0.00031748213,0.00043052877,0.00044415123,0.00064045313,0.0007277175,0.00064708426,0.0016740343],"category_scores_gemma":[0.0055787507,0.00021837966,0.0003271555,0.00023895713,0.0006081962,0.00073584524,0.0012497748,0.00039818272,0.00039212703],"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":[0.0027049717,0.0005581695,0.03720086,0.00040786792,0.00011088083,0.0007279535,0.00089527504,0.038265537,0.8552224,0.00161422,0.0009725579,0.06131923],"study_design_scores_gemma":[0.00017608161,0.004458688,0.059723303,0.00007645715,0.0001225565,0.00044302133,0.00078931457,0.16100165,0.7671492,0.0014911621,0.0043742033,0.0001943952],"about_ca_topic_score_codex":0.0012334295,"about_ca_topic_score_gemma":0.0017584095,"teacher_disagreement_score":0.0028207412,"about_ca_system_score_codex":0.0006066545,"about_ca_system_score_gemma":0.0004934321,"threshold_uncertainty_score":0.014917731},"labels":[],"label_agreement":null},{"id":"W3156567400","doi":"10.1016/j.rse.2021.112454","title":"Analysis of coastal wind speed retrieval from CYGNSS mission using artificial neural network","year":2021,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":83,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Fisheries and Oceans Canada","funders":"Natural Science Foundation of Zhejiang Province; National Key Research and Development Program of China; State Key Laboratory of Satellite Ocean Environment Dynamics; Ministerio de Economía y Competitividad; Chinese Academy of Sciences; National Natural Science Foundation of China; Ministry of Science and Technology of the People's Republic of China; Department of Science and Technology of Shandong Province; Ministerio de Ciencia, Innovación y Universidades; National Aeronautics and Space Administration","keywords":"Remote sensing; Artificial neural network; Wind speed; Environmental science; Computer science; Meteorology; Artificial intelligence; Geology; Geography","score_opus":0.023354922258579203,"score_gpt":0.241412451929666,"score_spread":0.2180575296710868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3156567400","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98302215,0.00019766878,0.013948588,0.00011341954,0.0000461332,0.000017973152,0.00035676587,0.00029748626,0.001999606],"genre_scores_gemma":[0.99378854,0.00008363957,0.0045778267,0.000016186614,0.000010789193,0.000008572985,0.00049437577,0.000030940002,0.0009891236],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99993026,0.0000075237813,0.0000039603974,0.000017252767,0.000024884102,0.000016099499],"domain_scores_gemma":[0.9998791,0.000035768455,0.000012210548,0.000009346006,0.00005616065,0.000007440371],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019811807,0.00045126613,0.000230786,0.00042598692,0.00021277799,0.0002943989,0.000245264,0.00033178087,0.0008115774],"category_scores_gemma":[0.00047620165,0.00014899239,0.0004004349,0.0004186835,0.00013023285,0.0003686123,0.0001416008,0.00026735943,0.00016969416],"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":[0.00064125773,0.00020001939,0.046466272,0.00018376758,0.00022965384,0.0006472476,0.00009974971,0.763025,0.091849014,0.0007540142,0.0029240793,0.09297992],"study_design_scores_gemma":[0.000012667446,0.000028533275,0.02213624,0.0000040722684,0.000021168853,0.000022473434,0.000024116063,0.97221035,0.0052177883,0.00008764168,0.00022573174,0.000009303646],"about_ca_topic_score_codex":0.018081611,"about_ca_topic_score_gemma":0.01574209,"teacher_disagreement_score":0.018081611,"about_ca_system_score_codex":0.0003124391,"about_ca_system_score_gemma":0.00040266427,"threshold_uncertainty_score":0.035952747},"labels":[],"label_agreement":null},{"id":"W3172789769","doi":"10.1016/j.rse.2021.112510","title":"Comparing airborne and spaceborne photon-counting LiDAR canopy structural estimates across different boreal forest types","year":2021,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":49,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lidar; Canopy; Remote sensing; Environmental science; Taiga; Satellite; Altimeter; Tree canopy; Elevation (ballistics); Geography; Forestry","score_opus":0.01246657808293295,"score_gpt":0.23925098954053214,"score_spread":0.2267844114575992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172789769","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99889874,0.000044649652,0.00020656445,0.0000031204195,9.506953e-7,0.0000066792404,0.00029758728,0.000007646067,0.0005340863],"genre_scores_gemma":[0.9980946,0.00004443113,0.00083505764,0.0000062841746,0.0000015284386,0.000008175335,0.0008739769,0.000003328695,0.00013257211],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997602,0.000025303267,0.00001581328,0.000075463395,0.0000742814,0.000048871374],"domain_scores_gemma":[0.9995116,0.000085807864,0.00008883465,0.00003621682,0.00023146285,0.000046027515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003533416,0.00028241615,0.00016327621,0.0009996841,0.00030743165,0.00043431367,0.0003382592,0.00014555162,0.00035087985],"category_scores_gemma":[0.0006834131,0.00012473232,0.00015877499,0.0008429017,0.00023697008,0.0003396285,0.00025182703,0.000088777335,0.00006933877],"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":[0.00026932484,0.00006373819,0.96563494,0.000047647274,0.000072448,0.000048778405,0.0006051046,0.0023535837,0.009341312,0.00009726702,0.00020213575,0.02126371],"study_design_scores_gemma":[0.0000052747346,0.000027796255,0.9975198,0.0000038042956,0.00001643883,0.000028413042,0.00033837464,0.0015106058,0.00032706285,0.000010762151,0.00020735858,0.0000042234997],"about_ca_topic_score_codex":0.31916285,"about_ca_topic_score_gemma":0.6588573,"teacher_disagreement_score":0.31916285,"about_ca_system_score_codex":0.0009824132,"about_ca_system_score_gemma":0.0006315705,"threshold_uncertainty_score":0.63461024},"labels":[],"label_agreement":null},{"id":"W3177208529","doi":"10.1016/j.rse.2021.112554","title":"Sentinel-1 soil moisture at 1 km resolution: a validation study","year":2021,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":145,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Remote sensing; Synthetic aperture radar; Environmental science; Mean squared error; Water content; Satellite; Image resolution; Radar; Scale (ratio); Geology; Geography; Computer science; Mathematics; Statistics; Cartography","score_opus":0.01248078922979281,"score_gpt":0.22074418195700402,"score_spread":0.2082633927272112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3177208529","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9959577,0.00006729236,0.0022103891,0.000034158387,0.000012572102,0.000038443784,0.0009985948,0.00008966264,0.0005912398],"genre_scores_gemma":[0.9898519,0.00008032631,0.0043443427,0.000043474192,0.000008196471,0.0000380101,0.0051197354,0.000032345844,0.00048175314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9993135,0.00024227137,0.00004480113,0.00014468188,0.0001867205,0.000068112415],"domain_scores_gemma":[0.99786925,0.00090292055,0.0001793412,0.00052173226,0.0004579725,0.000068708694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034548761,0.0006320698,0.00043924796,0.0003154312,0.00034912492,0.00034544236,0.0010147677,0.0009992422,0.0004550972],"category_scores_gemma":[0.0027542242,0.00023376287,0.0006269789,0.0003968025,0.00060205074,0.00066502445,0.0003635998,0.00041707003,0.00034275927],"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":[0.0054643857,0.008508863,0.24331836,0.0008540741,0.0011391456,0.0011017428,0.0009244996,0.4009476,0.2143079,0.0015920062,0.010899687,0.11094173],"study_design_scores_gemma":[0.0013948537,0.0042230645,0.48697075,0.00007384112,0.0004949189,0.0007400355,0.0004526834,0.4422639,0.058228925,0.000707693,0.0043229493,0.00012636563],"about_ca_topic_score_codex":0.013755563,"about_ca_topic_score_gemma":0.013895752,"teacher_disagreement_score":0.013755563,"about_ca_system_score_codex":0.00043550145,"about_ca_system_score_gemma":0.00056978106,"threshold_uncertainty_score":0.027350962},"labels":[],"label_agreement":null},{"id":"W3184827121","doi":"10.1016/j.rse.2021.112579","title":"Advances in quantifying power plant CO2 emissions with OCO-2","year":2021,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":178,"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; Environment and Climate Change Canada","funders":"Jet Propulsion Laboratory","keywords":"Environmental science; Observatory; Satellite; Remote sensing; Power station; Work (physics); Emission inventory; Power (physics); Meteorology; Air quality index; Geography; Engineering; Physics","score_opus":0.01008266185133719,"score_gpt":0.21813835376485338,"score_spread":0.2080556919135162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184827121","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8231677,0.0036985285,0.14395116,0.00056068326,0.00015299376,0.00015508283,0.0056555304,0.0010496577,0.021608742],"genre_scores_gemma":[0.91759145,0.0009758291,0.077141955,0.00007991681,0.000045369943,0.000087972236,0.0032132738,0.0001417233,0.00072261057],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992287,0.0001238564,0.000042761647,0.00022004137,0.00033700222,0.000047676775],"domain_scores_gemma":[0.99853325,0.0003313359,0.00029834182,0.0003005487,0.00049567915,0.000040860847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012426641,0.0012547552,0.00042340718,0.001541019,0.00032504826,0.0011406905,0.00081123103,0.0004316815,0.00059339165],"category_scores_gemma":[0.0026028499,0.0003189949,0.00053613714,0.0033175554,0.00033389794,0.0012578568,0.0006904484,0.0005454206,0.00019516361],"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":[0.00029283005,0.00027475593,0.53947634,0.0006952207,0.00094247883,0.00016310731,0.00028403534,0.16284874,0.05719588,0.0039696135,0.0023163662,0.23154065],"study_design_scores_gemma":[0.000083304716,0.000311011,0.50474286,0.00027096152,0.000525482,0.00030837243,0.0004942186,0.31739077,0.11134504,0.005829678,0.058426645,0.00027160507],"about_ca_topic_score_codex":0.019594409,"about_ca_topic_score_gemma":0.04430784,"teacher_disagreement_score":0.019594409,"about_ca_system_score_codex":0.0007688895,"about_ca_system_score_gemma":0.0006604147,"threshold_uncertainty_score":0.038960695},"labels":[],"label_agreement":null},{"id":"W3187511516","doi":"10.1016/j.rse.2021.112609","title":"Downscaling of far-red solar-induced chlorophyll fluorescence of different crops from canopy to leaf level using a diurnal data set acquired by the airborne imaging spectrometer HyPlant","year":2021,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":55,"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 Alberta","funders":"Ministerio de Ciencia e Innovación; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Deutsche Forschungsgemeinschaft; European Commission; Bundesministerium für Bildung und Forschung; European Space Agency","keywords":"Canopy; Remote sensing; Environmental science; Chlorophyll fluorescence; Leaf area index; Spectrometer; Vegetation (pathology); Photochemical Reflectance Index; Multispectral image; Atmospheric sciences; Normalized Difference Vegetation Index; Chlorophyll; Agronomy; Botany; Geology; Optics; Physics","score_opus":0.03908038171573906,"score_gpt":0.24299497323114394,"score_spread":0.20391459151540486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3187511516","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99289966,0.00011967496,0.0036649418,0.000018224262,0.000012299371,0.000021678263,0.0020922692,0.00014665093,0.0010247004],"genre_scores_gemma":[0.96876246,0.00015459886,0.02077618,0.00004027233,0.000014891772,0.00006972292,0.009485283,0.00008431495,0.0006123557],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99988246,0.0000065383515,0.0000050448584,0.000044387452,0.000042402287,0.000019178415],"domain_scores_gemma":[0.9998491,0.000011540783,0.000021641506,0.00002508312,0.00007327408,0.0000192195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014516976,0.00037068102,0.00035778593,0.0008414613,0.00029553784,0.0002514372,0.0002517956,0.00025105246,0.0004460115],"category_scores_gemma":[0.00016725772,0.00014076506,0.00042362078,0.0008289346,0.00012016592,0.00026748487,0.0002451242,0.000319008,0.00018143747],"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":[0.00042287557,0.00036828537,0.14611992,0.00030976514,0.00025815942,0.0004861636,0.00074210705,0.007031304,0.7403238,0.0002536911,0.002187341,0.10149658],"study_design_scores_gemma":[0.000016611091,0.00010265863,0.94118226,0.000014314644,0.000058107387,0.00028941885,0.00017261317,0.01368175,0.041608576,0.00007809701,0.0027571265,0.000038432572],"about_ca_topic_score_codex":0.0072823837,"about_ca_topic_score_gemma":0.01752506,"teacher_disagreement_score":0.0072823837,"about_ca_system_score_codex":0.0002500246,"about_ca_system_score_gemma":0.0002794365,"threshold_uncertainty_score":0.014479995},"labels":[],"label_agreement":null},{"id":"W3199112589","doi":"10.1016/j.rse.2021.112688","title":"The Monitoring Nitrous Oxide Sources (MIN2OS) satellite project","year":2021,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Association of Friendship Centres; Agriculture and Agri-Food Canada","funders":"Institut national des sciences de l'Univers; National Oceanic and Atmospheric Administration; Région Occitanie Pyrénées-Méditerranée; Airbus Defense and Space; Centre National de la Recherche Scientifique; Centre National d’Etudes Spatiales; European Space Agency; National Aeronautics and Space Administration","keywords":"Environmental science; Nitrous oxide; Remote sensing; Satellite; Inversion (geology); Atmospheric sciences; Meteorology; Geology; Physics","score_opus":0.010135754509032644,"score_gpt":0.21086923310811917,"score_spread":0.20073347859908652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199112589","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4218167,0.0035238422,0.071756475,0.004623492,0.0008813475,0.0021659683,0.4180101,0.009901274,0.067320876],"genre_scores_gemma":[0.31833947,0.0014420499,0.14202295,0.0009326339,0.0003174701,0.0013447581,0.51788425,0.0008225984,0.016893819],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993031,0.00019215282,0.000016670621,0.00014466824,0.00024877427,0.0000946528],"domain_scores_gemma":[0.999464,0.000046044548,0.0000863365,0.00006107537,0.0002242694,0.00011834577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019592543,0.00093950244,0.00055669475,0.0006007167,0.00034670893,0.0004911207,0.00058135675,0.0004319754,0.0018451631],"category_scores_gemma":[0.00065825245,0.00018351787,0.0002803248,0.00082458975,0.00025576126,0.0006734864,0.0010298559,0.0005545481,0.0008528848],"study_design_candidate":"not_applicable","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":[0.0042395447,0.0009010969,0.14494316,0.0010028916,0.00059332326,0.00037697633,0.0006789581,0.018096909,0.095802814,0.010316979,0.33590984,0.38713756],"study_design_scores_gemma":[0.0017581246,0.0012606548,0.2372063,0.00020488318,0.00033433174,0.00019969734,0.0005775464,0.081038,0.060877584,0.007482408,0.608916,0.00014458338],"about_ca_topic_score_codex":0.01630202,"about_ca_topic_score_gemma":0.01824988,"teacher_disagreement_score":0.01630202,"about_ca_system_score_codex":0.0005519631,"about_ca_system_score_gemma":0.0018144231,"threshold_uncertainty_score":0.032414258},"labels":[],"label_agreement":null},{"id":"W319955476","doi":"10.1016/j.rse.2015.04.017","title":"Reconstructing hydrographic change in Petersen Bay, Ellesmere Island, Canada, inferred from SAR imagery","year":2015,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Carleton University; University of Ottawa","funders":"University of British Columbia; W. Garfield Weston Foundation; Canada Foundation for Innovation; Garfield Weston Foundation; ArcticNet; Royal Geographical Society; University of Ottawa; Natural Sciences and Engineering Research Council of Canada; Royal Canadian Geographical Society; Carleton University","keywords":"Geology; Bay; Oceanography; Sea ice; Shelf ice; Arctic ice pack; Arctic; Satellite imagery; Iceberg; Ice shelf; Hydrography; Antarctic sea ice; Cryosphere","score_opus":0.03496647571985676,"score_gpt":0.20028052756639414,"score_spread":0.16531405184653739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W319955476","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99390924,0.00014575922,0.000286857,0.0001118257,0.0000072049215,0.00001755967,0.003389726,0.00006403325,0.002067808],"genre_scores_gemma":[0.9954181,0.00016346497,0.00087028014,0.000019203648,0.0000026992152,0.000005232436,0.0022180167,0.00001039622,0.0012925417],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99993634,0.0000023544876,0.000003054364,0.000016551876,0.000019222973,0.000022509561],"domain_scores_gemma":[0.99981207,0.000014502511,0.000020355481,0.000008753939,0.000097123615,0.00004722637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011708781,0.0002491484,0.00015633168,0.0012198049,0.0005882703,0.00070721493,0.00048474484,0.00032678794,0.0013201198],"category_scores_gemma":[0.0005533521,0.00020705347,0.00017502699,0.0016291637,0.0003645263,0.00025264904,0.00029117844,0.00028351526,0.00026108848],"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":[0.00044860184,0.0001762397,0.856238,0.00017175991,0.00021717085,0.0007654054,0.0018333956,0.043934427,0.016048027,0.00083758804,0.00776837,0.071561106],"study_design_scores_gemma":[0.000026049922,0.000009599659,0.9732351,0.000031633997,0.00003344028,0.000036794096,0.0011168215,0.02220939,0.0007409688,0.00007390203,0.0024699126,0.000016503474],"about_ca_topic_score_codex":0.98275363,"about_ca_topic_score_gemma":0.99129546,"teacher_disagreement_score":0.017246366,"about_ca_system_score_codex":0.0046456303,"about_ca_system_score_gemma":0.008063582,"threshold_uncertainty_score":0.034695804},"labels":[],"label_agreement":null},{"id":"W3205439147","doi":"10.1016/j.rse.2021.112694","title":"Satellite remote sensing of active fires: History and current status, applications and future requirements","year":2021,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":268,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Forest Service","funders":"National Centre for Earth Observation; Natural Environment Research Council; National Oceanic and Atmospheric Administration; Sight Research UK; Leverhulme Trust; National Science Foundation","keywords":"Remote sensing; Environmental science; Geostationary orbit; Biome; Earth observation; Satellite; Meteorology; Environmental resource management; Geography; Ecosystem; Ecology","score_opus":0.012539124735470345,"score_gpt":0.22644902472354236,"score_spread":0.21390989998807203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205439147","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026745249,0.9359072,0.007089298,0.0143081155,0.0007422028,0.00009642999,0.00076843635,0.00011741916,0.014225704],"genre_scores_gemma":[0.18090367,0.78164655,0.01989867,0.004160099,0.004099131,0.00014578614,0.0020028562,0.000081551356,0.0070617287],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985557,0.00043032225,0.00016716679,0.00030402237,0.00041463447,0.00012822404],"domain_scores_gemma":[0.9826943,0.009087447,0.0011516629,0.00044070935,0.005633722,0.0009921617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011483273,0.0009101563,0.0013548648,0.0016324595,0.0004167032,0.003169739,0.0016142096,0.0015747463,0.0066927364],"category_scores_gemma":[0.0067001465,0.00047184608,0.0008465633,0.0026243464,0.002441485,0.003650566,0.00093457784,0.0016336816,0.0012776235],"study_design_candidate":"not_applicable","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":[0.0008874855,0.00028145828,0.03396613,0.0048913155,0.00017854903,0.00012848635,0.00041711106,0.0018475754,0.007547473,0.0055218204,0.011657081,0.93267554],"study_design_scores_gemma":[0.00021818295,0.0032001892,0.16187848,0.012903282,0.0010844374,0.0032722778,0.004303256,0.011426143,0.009812121,0.021163698,0.7704128,0.00032521164],"about_ca_topic_score_codex":0.0056899525,"about_ca_topic_score_gemma":0.012403921,"teacher_disagreement_score":0.011483273,"about_ca_system_score_codex":0.0014532808,"about_ca_system_score_gemma":0.0029386922,"threshold_uncertainty_score":0.06073004},"labels":[],"label_agreement":null},{"id":"W3206856900","doi":"10.1016/j.rse.2021.112721","title":"Daily estimation of gross primary production under all sky using a light use efficiency model coupled with satellite passive microwave measurements","year":2021,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Environmental science; Primary production; Photosynthetically active radiation; Cloud cover; Remote sensing; Leaf area index; Enhanced vegetation index; Satellite; Atmospheric sciences; Normalized Difference Vegetation Index; Geography; Ecosystem; Cloud computing; Vegetation Index; Ecology; Geology","score_opus":0.02590110857128122,"score_gpt":0.2106934580256989,"score_spread":0.18479234945441766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206856900","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9850032,0.000050681367,0.013269955,0.000043078362,0.000011516218,0.0000123641985,0.00063519104,0.00019378931,0.0007803188],"genre_scores_gemma":[0.99437606,0.000025325684,0.0046814405,0.000006078083,0.0000034693624,0.000010797013,0.0005934146,0.00001813257,0.0002853134],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999039,0.000020350846,0.0000054674088,0.000038550606,0.000017977689,0.000013704253],"domain_scores_gemma":[0.9997464,0.000110330715,0.000022524633,0.000041861567,0.000056789162,0.000022012671],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028949796,0.00041465947,0.00046808663,0.00030167866,0.00027604602,0.00042135973,0.00043641863,0.0005004752,0.0005844441],"category_scores_gemma":[0.0005170215,0.0003959319,0.00063618575,0.0003925455,0.00018393148,0.00046704526,0.00019790379,0.0003153578,0.00022128278],"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":[0.00025772353,0.00019944736,0.050236024,0.00006396852,0.00021999216,0.00007277098,0.000057091693,0.9100114,0.021356113,0.00034738585,0.0005261497,0.016651837],"study_design_scores_gemma":[0.000027075335,0.000040932748,0.04240029,0.0000021484586,0.000029182147,0.000012459447,0.000013461549,0.95435447,0.00275179,0.00016939636,0.00018179211,0.00001696467],"about_ca_topic_score_codex":0.022121178,"about_ca_topic_score_gemma":0.02887635,"teacher_disagreement_score":0.022121178,"about_ca_system_score_codex":0.00049733813,"about_ca_system_score_gemma":0.00062237255,"threshold_uncertainty_score":0.04398489},"labels":[],"label_agreement":null},{"id":"W3207064546","doi":"10.1016/j.rse.2021.112731","title":"Integration of multi-scale remote sensing data for reindeer lichen fractional cover mapping in Eastern Canada","year":2021,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Rangeland and Wildlife Management","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Government of Newfoundland and Labrador; Environment and Climate Change Canada; Natural Resources Canada","funders":"Natural Resources Canada; Government of Canada","keywords":"Lichen; Remote sensing; Range (aeronautics); Scale (ratio); Environmental science; Vegetation (pathology); Ground truth; Physical geography; Geography; Cartography; Ecology; Computer science; Biology","score_opus":0.037606333862005834,"score_gpt":0.24426614172519792,"score_spread":0.2066598078631921,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207064546","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99563605,0.00052112044,0.0003220358,0.00010085034,0.0000109945395,0.00001992894,0.0014731555,0.000032553045,0.0018832191],"genre_scores_gemma":[0.9960743,0.00022647624,0.0011113224,0.000030383962,0.000002862319,0.000010414281,0.0012951511,0.000009408823,0.0012396987],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996101,0.000032549968,0.000023402064,0.00008140609,0.00011459087,0.0001379317],"domain_scores_gemma":[0.9990139,0.000071387156,0.00006828722,0.00003938999,0.0007075294,0.00009954777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000543737,0.00026399182,0.0003014573,0.0021232895,0.0009755721,0.0012712652,0.00047588337,0.00030809152,0.0009097154],"category_scores_gemma":[0.0010718488,0.00019528477,0.00030655533,0.002433208,0.00024599515,0.00042074206,0.0006687543,0.00024875748,0.00016242047],"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":[0.00024819482,0.00014222012,0.9109983,0.00009687403,0.00038102167,0.0002535384,0.0014331568,0.007065987,0.0073017357,0.00031839075,0.002614787,0.06914582],"study_design_scores_gemma":[0.000005941175,0.0000074671125,0.99149066,0.000036021596,0.000068079054,0.000025499983,0.0012324109,0.0047896444,0.00038878253,0.000026596577,0.001914761,0.000014113742],"about_ca_topic_score_codex":0.98688436,"about_ca_topic_score_gemma":0.9959312,"teacher_disagreement_score":0.013115644,"about_ca_system_score_codex":0.008565905,"about_ca_system_score_gemma":0.009971653,"threshold_uncertainty_score":0.06215024},"labels":[],"label_agreement":null},{"id":"W3207302418","doi":"10.1016/j.rse.2021.112732","title":"The influence of forest fire aerosol and air temperature on glacier albedo, western North America","year":2021,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Tula Foundation","keywords":"Albedo (alchemy); Snow; Glacier; Glacier mass balance; Physical geography; Climatology; Precipitation; Environmental science; Cloud albedo; Ablation zone; Atmospheric sciences; Geology; Geography; Cloud cover; Meteorology","score_opus":0.007671872229606102,"score_gpt":0.18252109494841517,"score_spread":0.17484922271880907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207302418","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99922776,0.0001087145,0.000011656707,0.00005570255,0.0000037902516,0.0000010338765,0.00012063059,0.000002984599,0.0004676992],"genre_scores_gemma":[0.9993523,0.00006968343,0.000020901201,0.00002598769,0.0000067789583,0.0000013200054,0.00013683461,0.0000027011365,0.00038352908],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998553,0.000037924783,0.000009275955,0.000034237983,0.000021013699,0.00004227133],"domain_scores_gemma":[0.99922264,0.00030954485,0.00012218911,0.00003927393,0.00013937436,0.00016696063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003987052,0.000178669,0.00017028091,0.00035524773,0.00046006768,0.0007014471,0.0003062647,0.0003972968,0.0018485651],"category_scores_gemma":[0.0009816043,0.00017273339,0.00033917226,0.00033363324,0.0004137742,0.00039227298,0.0003775425,0.00028461724,0.00019507931],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026327692,0.00007719325,0.99194324,0.000021115959,0.0001861082,0.00020904145,0.00025004885,0.0006813485,0.003153638,0.000075314514,0.00030588222,0.0028336616],"study_design_scores_gemma":[0.0000028749641,0.0000063103557,0.9994887,0.0000016659371,0.000017137188,0.000012917448,0.00011522917,0.00020076898,0.00006560034,0.000008693729,0.00007895197,0.0000010338428],"about_ca_topic_score_codex":0.22614473,"about_ca_topic_score_gemma":0.37209517,"teacher_disagreement_score":0.77385527,"about_ca_system_score_codex":0.0010501767,"about_ca_system_score_gemma":0.00075820857,"threshold_uncertainty_score":0.44965684},"labels":[],"label_agreement":null},{"id":"W4205585626","doi":"10.1016/j.rse.2021.112862","title":"Accelerated change in the glaciated environments of western Canada revealed through trend analysis of optical satellite imagery","year":2022,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":36,"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 Northern British Columbia; Ministry of Forests","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Glacier; Geology; Glacier mass balance; Physical geography; Debris; Satellite imagery; Tidewater glacier cycle; Glacier morphology; Rock glacier; Cirque glacier; Satellite; Climatology; Geomorphology; Remote sensing; Cryosphere; Ice stream; Oceanography; Geography; Sea ice; Ice calving","score_opus":0.04472830048001107,"score_gpt":0.23113537299503528,"score_spread":0.1864070725150242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205585626","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9891276,0.0004247179,0.0005781128,0.00013394626,0.000008245004,0.000022060945,0.0058730138,0.00012265594,0.0037096133],"genre_scores_gemma":[0.9916469,0.0005767284,0.0020371804,0.000033581015,0.0000048678444,0.0000110014125,0.003720252,0.000021045065,0.0019484167],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99983203,0.000005602157,0.000009409868,0.00003457047,0.000073570496,0.00004479497],"domain_scores_gemma":[0.9992735,0.000037864746,0.00011988352,0.000020756659,0.00048391032,0.000064207954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018882769,0.00026814186,0.00010076769,0.0030968206,0.00077948417,0.0012537194,0.00027229555,0.00014479776,0.0011526594],"category_scores_gemma":[0.0010509253,0.00014792335,0.00017228532,0.005058222,0.00029012712,0.00037967783,0.0003410469,0.00022121129,0.00015645352],"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":[0.000035648678,0.00001816469,0.9521577,0.00006070837,0.000047074074,0.00008888476,0.0006921619,0.0013784693,0.001853713,0.00020111744,0.0021659953,0.04130039],"study_design_scores_gemma":[0.0000018106996,0.0000029287785,0.9953377,0.000013890871,0.000014441327,0.000022429133,0.0005664992,0.0018076622,0.0002317175,0.000029172395,0.0019662555,0.000005569539],"about_ca_topic_score_codex":0.98848057,"about_ca_topic_score_gemma":0.996015,"teacher_disagreement_score":0.011519432,"about_ca_system_score_codex":0.0085884575,"about_ca_system_score_gemma":0.009612597,"threshold_uncertainty_score":0.062313974},"labels":[],"label_agreement":null},{"id":"W4205698403","doi":"10.1016/j.rse.2021.112845","title":"Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission","year":2022,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":435,"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 Alberta","funders":"Australian Research Council; Natural Environment Research Council; Smithsonian Tropical Research Institute; Smithsonian Conservation Biology Institute; Direktoratet for Utviklingssamarbeid; South African National Parks; Centre National d’Etudes Spatiales; Sight Research UK; Organismo Autónomo de Parques Nacionales; Narodowy Fundusz Ochrony Środowiska i Gospodarki Wodnej; U.S. Forest Service; Agence Nationale de la Recherche; Battelle; Australian Government; Gordon and Betty Moore Foundation; United States Agency for International Development; Natural Sciences and Engineering Research Council of Canada; University of Maryland; Smithsonian Institution; U.S. Department of State; National Aeronautics and Space Administration; Empresa Brasileira de Pesquisa Agropecuária; National Science Foundation","keywords":"Remote sensing; Lidar; Environmental science; Biomass (ecology); Ecosystem; Geography; Ecology; Geology; Oceanography; Biology","score_opus":0.01748460475649456,"score_gpt":0.22681243395780146,"score_spread":0.2093278292013069,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205698403","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4595887,0.0010641745,0.46379504,0.0017607298,0.0002382325,0.00031541273,0.043772455,0.0038309053,0.025634335],"genre_scores_gemma":[0.8239708,0.000699645,0.12817909,0.0003761463,0.000091922666,0.00081387267,0.03402588,0.000694983,0.011147536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983466,0.000044406836,0.000009432443,0.000047379508,0.000040084386,0.000024052844],"domain_scores_gemma":[0.9993498,0.00031891017,0.00006848081,0.00006518986,0.00015840433,0.000039170187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000840008,0.0006035756,0.00037842267,0.00066234136,0.00046698382,0.0006609947,0.0019149493,0.0008714504,0.003939018],"category_scores_gemma":[0.0023538743,0.00040631308,0.0012376179,0.0009981133,0.00019117941,0.0009475031,0.0005841823,0.0012819519,0.0013781388],"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":[0.000029380719,0.000072243856,0.010751685,0.000041836425,0.00006687502,0.00005248645,0.000057319245,0.9579233,0.0004972387,0.010020401,0.0073640756,0.013123197],"study_design_scores_gemma":[0.000009733818,0.0000098745995,0.001790629,0.000012259709,0.000009504146,0.000014012395,0.000022684313,0.9914096,0.00014197329,0.0034862533,0.00308324,0.0000102796475],"about_ca_topic_score_codex":0.04860138,"about_ca_topic_score_gemma":0.045587834,"teacher_disagreement_score":0.04860138,"about_ca_system_score_codex":0.0010828695,"about_ca_system_score_gemma":0.0009899942,"threshold_uncertainty_score":0.09663701},"labels":[],"label_agreement":null},{"id":"W4206510054","doi":"10.1016/j.rse.2022.112896","title":"Non-linearity between gross primary productivity and far-red solar-induced chlorophyll fluorescence emitted from canopies of major biomes","year":2022,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":52,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Primary production; Biome; Canopy; Linearity; Atmospheric sciences; Remote sensing; Environmental science; Linear regression; Normalization (sociology); Mathematics; Statistics; Ecology; Physics; Geology; Ecosystem","score_opus":0.009860633559536643,"score_gpt":0.19168600296757027,"score_spread":0.18182536940803362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206510054","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9978561,0.00007262457,0.0010612718,0.000031448973,0.0000034548752,0.0000044295825,0.00009973088,0.00002217484,0.00084877986],"genre_scores_gemma":[0.99958116,0.000008536511,0.00009432531,0.000015276066,0.0000013692847,0.0000037015425,0.00013340036,0.000006583137,0.00015574931],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99951446,0.0001232351,0.000025097213,0.00020611759,0.00006368635,0.00006744695],"domain_scores_gemma":[0.99605453,0.0029240495,0.00023837906,0.00025755484,0.00034537958,0.00018006722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089711667,0.00023781093,0.0003507556,0.00067938113,0.0003420716,0.0006115005,0.00047637147,0.00040410322,0.0010261085],"category_scores_gemma":[0.0025049807,0.00042689705,0.00049441186,0.00051779934,0.00064896245,0.00080439856,0.0006516932,0.00046106847,0.00030117173],"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":[0.00040783203,0.00005937125,0.9093185,0.00006426227,0.00048710636,0.0001137046,0.00048443445,0.0030284852,0.080512434,0.00030741404,0.00013505328,0.0050813053],"study_design_scores_gemma":[0.0000030198692,0.000017568966,0.9960706,0.0000019143665,0.00002267863,0.00004103289,0.000101418096,0.002841402,0.0007478647,0.00009213367,0.000054504268,0.0000058502005],"about_ca_topic_score_codex":0.010909473,"about_ca_topic_score_gemma":0.014584489,"teacher_disagreement_score":0.010909473,"about_ca_system_score_codex":0.00053935806,"about_ca_system_score_gemma":0.0003770334,"threshold_uncertainty_score":0.021691918},"labels":[],"label_agreement":null},{"id":"W4210502950","doi":"10.1016/j.rse.2022.112919","title":"Evaluating ICESat-2 for monitoring, modeling, and update of large area forest canopy height products","year":2022,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":66,"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":"Natural Resources Canada; Canadian Forest Service; Natural Sciences and Engineering Research Council of Canada; Government of Canada; Compute Canada","keywords":"Taiga; Environmental science; Canopy; Remote sensing; Elevation (ballistics); Terrestrial ecosystem; Scale (ratio); Satellite; Tree canopy; Forest ecology; Boreal; Physical geography; Ecosystem; Geography; Forestry; Ecology; Cartography","score_opus":0.03454087466045587,"score_gpt":0.28487771909533105,"score_spread":0.2503368444348752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210502950","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9678201,0.00045514054,0.013470285,0.00020254971,0.00006676184,0.00029679723,0.011622947,0.0016980966,0.0043672933],"genre_scores_gemma":[0.937438,0.00018143353,0.029808365,0.00010391197,0.000019845038,0.00011365868,0.031443425,0.00017133543,0.00071992096],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99709725,0.00065950025,0.00018097658,0.00052435475,0.0013126314,0.00022524015],"domain_scores_gemma":[0.99208224,0.0018931925,0.00081050687,0.00080496754,0.0039651454,0.00044397396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008956554,0.0008626345,0.00040838594,0.0017781248,0.0005126074,0.0016465567,0.0013467458,0.0006345275,0.0005631466],"category_scores_gemma":[0.01353255,0.0002709905,0.0004752813,0.0019992837,0.00027114403,0.0015346337,0.00066395075,0.00051638373,0.00045203266],"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":[0.0012669568,0.0008254346,0.71550137,0.00024041157,0.0008606095,0.0002456944,0.00053114496,0.16229114,0.010895295,0.0011917006,0.010898398,0.095251895],"study_design_scores_gemma":[0.00011201575,0.00022255354,0.43283334,0.00009180211,0.00014388881,0.000057022702,0.00057031977,0.5537974,0.0070777833,0.00027308843,0.0047536534,0.000067181725],"about_ca_topic_score_codex":0.23634465,"about_ca_topic_score_gemma":0.27716944,"teacher_disagreement_score":0.23634465,"about_ca_system_score_codex":0.002113392,"about_ca_system_score_gemma":0.0021530923,"threshold_uncertainty_score":0.46993792},"labels":[],"label_agreement":null},{"id":"W4210802242","doi":"10.1016/j.rse.2022.112900","title":"Soil moisture retrieval over croplands using dual-pol L-band GRD SAR data","year":2022,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":98,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Ministry of Education, India","keywords":"Remote sensing; Environmental science; Vegetation (pathology); Synthetic aperture radar; Polarimetry; Backscatter (email); Water content; Land cover; Soil science; Geology; Scattering; Computer science; Land use","score_opus":0.024007084126741098,"score_gpt":0.24257493977712066,"score_spread":0.21856785565037956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210802242","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9873083,0.0003209494,0.0081458045,0.000093523355,0.00002520176,0.000016691454,0.0017917705,0.0005966587,0.0017010425],"genre_scores_gemma":[0.98811686,0.00013372482,0.009598054,0.000030264915,0.000013709166,0.000006757943,0.0015133532,0.000028665096,0.00055871793],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99992836,0.0000075228345,0.000004199905,0.000023660037,0.000017130702,0.000019145778],"domain_scores_gemma":[0.99992394,0.00001181474,0.000012168571,0.000011703048,0.000025004458,0.000015330217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014960085,0.0003320036,0.000303847,0.00058280193,0.00013549416,0.00041987238,0.00028468698,0.00031986617,0.00082555565],"category_scores_gemma":[0.00026912612,0.00020145813,0.00020649866,0.000545517,0.00012469759,0.00044034218,0.00019522483,0.00015289178,0.0003972949],"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":[0.0012069028,0.00039573084,0.07097544,0.00037302542,0.00023304173,0.000634168,0.00016123005,0.11672645,0.61886466,0.000624658,0.0044954726,0.18530928],"study_design_scores_gemma":[0.0003231816,0.00014369271,0.21427214,0.000026317546,0.00014660507,0.00018840109,0.00017507133,0.7378034,0.043599937,0.00036967904,0.0028866958,0.00006486238],"about_ca_topic_score_codex":0.011649095,"about_ca_topic_score_gemma":0.01726502,"teacher_disagreement_score":0.011649095,"about_ca_system_score_codex":0.0002694329,"about_ca_system_score_gemma":0.0002955556,"threshold_uncertainty_score":0.023162603},"labels":[],"label_agreement":null},{"id":"W4220886470","doi":"10.1016/j.rse.2022.112961","title":"Canopy spectral reflectance detects oak wilt at the landscape scale using phylogenetic discrimination","year":2022,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":53,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Université de Montréal","funders":"Minnesota Invasive Terrestrial Plants and Pests Center, University of Minnesota; University of Minnesota; National Aeronautics and Space Administration; U.S. Forest Service; National Science Foundation","keywords":"Remote sensing; Canopy; Reflectivity; Scale (ratio); Environmental science; Spectral signature; Geography; Biology; Ecology; Cartography; Optics","score_opus":0.011350564168745629,"score_gpt":0.2143463445502707,"score_spread":0.20299578038152508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220886470","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9952004,0.000055178753,0.0038126046,0.000012509259,0.0000031229633,0.0000033419046,0.000049102142,0.000048931422,0.0008148175],"genre_scores_gemma":[0.99687344,0.00002895011,0.0027744414,0.00001504049,0.0000021768271,0.0000022977565,0.000097599244,0.000012813889,0.0001933798],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998493,0.000024920584,0.000003742052,0.00006853119,0.000028103399,0.00002538635],"domain_scores_gemma":[0.9997044,0.00009419822,0.00007014305,0.000024069355,0.000063344734,0.000043704677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028297325,0.0002278012,0.00024939654,0.00059424876,0.00019527043,0.00044276705,0.00016343946,0.0004146261,0.00074851286],"category_scores_gemma":[0.0004975966,0.00016550973,0.00012848622,0.00033303865,0.00022707021,0.00043662084,0.00020155017,0.00025962593,0.00024347926],"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":[0.0003954482,0.0002288458,0.23894241,0.000042154436,0.00006488436,0.000106173204,0.00018399819,0.001783729,0.72202295,0.00019197322,0.00021994689,0.03581745],"study_design_scores_gemma":[0.000015724645,0.00015646016,0.93909025,0.000006863244,0.00004800869,0.00047377503,0.00021090993,0.03146789,0.027823208,0.0002924714,0.0003943666,0.000019986164],"about_ca_topic_score_codex":0.00170854,"about_ca_topic_score_gemma":0.0037022387,"teacher_disagreement_score":0.00170854,"about_ca_system_score_codex":0.00017769888,"about_ca_system_score_gemma":0.00008540113,"threshold_uncertainty_score":0.0033972263},"labels":[],"label_agreement":null},{"id":"W4280530602","doi":"10.1016/j.rse.2022.113061","title":"Year-round sea ice and snow characterization from combined passive and active microwave observations and radiative transfer modeling","year":2022,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Makivik Corporation; Université du Québec à Rimouski; Takuvik Joint International Laboratory; Center for Northern Studies; Université Laval","funders":"Centre National d’Etudes Spatiales; European Space Agency","keywords":"Scatterometer; Sea ice; Snow; Sea ice thickness; Radiative transfer; Sea ice concentration; Remote sensing; Microwave; Atmospheric radiative transfer codes; Environmental science; Satellite; Radiometer; Cryosphere; Special sensor microwave/imager; Geology; Characterization (materials science); Climatology; Brightness temperature; Oceanography; Physics; Wind speed; Geomorphology; Astronomy; Optics","score_opus":0.014870446784384199,"score_gpt":0.1746038293921589,"score_spread":0.1597333826077747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280530602","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9860495,0.00027728724,0.008215938,0.000095760755,0.000037907666,0.000021043874,0.0030540193,0.0004096246,0.0018389889],"genre_scores_gemma":[0.988736,0.00014102012,0.0057135927,0.000023675631,0.00002152125,0.000019920593,0.004671725,0.00004408653,0.0006284991],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988925,0.00002062156,0.000008636232,0.000043037227,0.000021830994,0.000016647724],"domain_scores_gemma":[0.99985135,0.000037541147,0.00002266638,0.000033550856,0.00003976215,0.000015059949],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035872086,0.00049583905,0.00037249242,0.0007573185,0.0003146167,0.0007480016,0.00046564118,0.00046866926,0.00074403675],"category_scores_gemma":[0.00058037654,0.0003334704,0.0008378461,0.00075675984,0.00013784732,0.0009098697,0.0002808925,0.00029661387,0.00034720148],"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":[0.0006545978,0.00055877975,0.24229024,0.00015727834,0.0009821969,0.00030684037,0.00019631484,0.5867912,0.061777055,0.0007752974,0.0046006464,0.10090956],"study_design_scores_gemma":[0.000066986024,0.00004271943,0.22192053,0.000010225879,0.00019013714,0.000042420837,0.000059424994,0.7697769,0.006053461,0.00040460067,0.0013907725,0.00004185375],"about_ca_topic_score_codex":0.023604458,"about_ca_topic_score_gemma":0.039818615,"teacher_disagreement_score":0.023604458,"about_ca_system_score_codex":0.00038759323,"about_ca_system_score_gemma":0.0006113423,"threshold_uncertainty_score":0.046934128},"labels":[],"label_agreement":null},{"id":"W4282926118","doi":"10.1016/j.rse.2022.113121","title":"Dynamic surface water maps of Canada from 1984 to 2019 Landsat satellite imagery","year":2022,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":57,"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":"Surface water; Remote sensing; Hydrography; Satellite imagery; Satellite; Thematic map; Environmental science; Water resources; Scale (ratio); Geography; Cartography; Hydrology (agriculture); Geology","score_opus":0.0036367117793877084,"score_gpt":0.1774168632071744,"score_spread":0.1737801514277867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282926118","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.107786626,0.0009323569,0.0013210583,0.00044159332,0.000054080174,0.00016088012,0.8648115,0.0005111504,0.023980776],"genre_scores_gemma":[0.32016698,0.001966396,0.009418056,0.00020694891,0.000025981863,0.00024371057,0.6504006,0.00019260676,0.01737874],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9995419,0.0000126330715,0.000020270003,0.000057993813,0.00025564217,0.00011168796],"domain_scores_gemma":[0.9981787,0.000039515846,0.00009076912,0.000045355675,0.001545631,0.000100038946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022297523,0.00056066026,0.00024283685,0.006133634,0.0012417466,0.0011735768,0.00076032605,0.00018258583,0.004631388],"category_scores_gemma":[0.0011673945,0.00020731718,0.00038618638,0.014632105,0.00034660817,0.00042510257,0.00061600737,0.00046682006,0.0011361636],"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":[0.00044064588,0.00012255354,0.28674722,0.0013989923,0.00032970967,0.0006805252,0.0025182723,0.015113324,0.0048118667,0.005527436,0.51489645,0.16741295],"study_design_scores_gemma":[0.000029074965,0.000018142373,0.7148552,0.00023740245,0.000049335653,0.00011387696,0.0021005028,0.006121443,0.0011293135,0.00035517538,0.27491915,0.00007142268],"about_ca_topic_score_codex":0.9952195,"about_ca_topic_score_gemma":0.9977398,"teacher_disagreement_score":0.021098077,"about_ca_system_score_codex":0.021098077,"about_ca_system_score_gemma":0.026924009,"threshold_uncertainty_score":0.15307796},"labels":[],"label_agreement":null},{"id":"W4283641179","doi":"10.1016/j.rse.2022.113129","title":"C- and L-band SAR signatures of Arctic sea ice during freeze-up","year":2022,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":37,"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 Manitoba; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Environment and Climate Change Canada; Mitacs; Marine Environmental Observation Prediction and Response Network; University of Manitoba; Entomological Society of America","keywords":"C band; Remote sensing; Geology; Sea ice; Backscatter (email); L band; Arctic; Open water; Grey level; The arctic; Climatology; Pixel; Artificial intelligence; Computer science; Oceanography","score_opus":0.006044313041025839,"score_gpt":0.17123060947763477,"score_spread":0.16518629643660893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283641179","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99902725,0.000043727458,0.00040291174,0.0000063463303,0.000002886249,0.0000027795538,0.00016032072,0.000015891394,0.0003379631],"genre_scores_gemma":[0.99877816,0.000047823367,0.00062118645,0.000008231294,0.0000023061898,0.000003247631,0.00035860602,0.0000051337206,0.00017540086],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999547,0.000004499321,0.0000025492418,0.000009854919,0.000012237562,0.000016103731],"domain_scores_gemma":[0.99986327,0.00002442906,0.000032814947,0.000007857178,0.00004731066,0.00002432874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014009392,0.00018264625,0.000120150806,0.00053861697,0.0001577965,0.00022323261,0.000101018035,0.00012513701,0.00031400268],"category_scores_gemma":[0.00028319322,0.0000772995,0.00012252016,0.00033996944,0.00015813844,0.00013684336,0.00013257071,0.00012124371,0.0000766444],"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":[0.0005794126,0.00008479578,0.7673384,0.00011927301,0.00008501245,0.00068960607,0.0008278806,0.0060575153,0.18673624,0.0001181921,0.0007080207,0.036655694],"study_design_scores_gemma":[0.0000030245662,0.000033274402,0.9880993,0.0000081755625,0.000015173497,0.000094216666,0.0002224784,0.0052387193,0.006007424,0.000028311051,0.00024291164,0.000007093873],"about_ca_topic_score_codex":0.0121740345,"about_ca_topic_score_gemma":0.027038947,"teacher_disagreement_score":0.0121740345,"about_ca_system_score_codex":0.00013716304,"about_ca_system_score_gemma":0.00014342155,"threshold_uncertainty_score":0.02420634},"labels":[],"label_agreement":null},{"id":"W4285676285","doi":"10.1016/j.rse.2022.113164","title":"Seasonal development and radiative forcing of red snow algal blooms on two glaciers in British Columbia, Canada, summer 2020","year":2022,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Environmental science; Snow; Radiative forcing; Snowmelt; Albedo (alchemy); Bloom; Snowpack; Algal bloom; Glacier; Atmospheric sciences; Forcing (mathematics); Snow line; Climatology; Oceanography; Physical geography; Geology; Climate change; Phytoplankton; Snow cover; Ecology; Geography; Meteorology; Nutrient; Biology","score_opus":0.010693401846792827,"score_gpt":0.17679343989761204,"score_spread":0.16610003805081922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285676285","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9963966,0.00012084576,0.00002246833,0.00012562906,0.000006766448,0.000005418964,0.002030653,0.0000141685705,0.0012774379],"genre_scores_gemma":[0.99601483,0.00013237198,0.000060513114,0.00004780284,0.000002926347,0.000006851559,0.0020785746,0.000004972219,0.0016510928],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99985623,0.000009813912,0.000008449728,0.000025038891,0.000033816512,0.000066591114],"domain_scores_gemma":[0.9993248,0.000056822522,0.000064286374,0.000016379265,0.00034017806,0.00019742257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024189935,0.0003379145,0.0002798991,0.0009901182,0.0018740159,0.0010142191,0.0005527167,0.0005100114,0.0018663025],"category_scores_gemma":[0.0006312841,0.00027105794,0.0002973441,0.0012726214,0.0005226563,0.00023297298,0.00053831673,0.00041797868,0.00024501578],"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":[0.0004557299,0.00010632057,0.9792181,0.000067805224,0.00011909724,0.00033618129,0.0015264035,0.002935685,0.004256668,0.00017095511,0.0044000894,0.0064069666],"study_design_scores_gemma":[0.0000070483343,0.000007440819,0.9976285,0.000008971576,0.000011426171,0.000017802831,0.00086159946,0.0006107893,0.000110271845,0.000010437595,0.0007191375,0.0000064692595],"about_ca_topic_score_codex":0.9876106,"about_ca_topic_score_gemma":0.9957516,"teacher_disagreement_score":0.012389421,"about_ca_system_score_codex":0.0111533655,"about_ca_system_score_gemma":0.008338282,"threshold_uncertainty_score":0.080923736},"labels":[],"label_agreement":null},{"id":"W4292543309","doi":"10.1016/j.rse.2022.113176","title":"Polarimetric decomposition of microwave-band freshwater ice SAR data: Review, analysis, and future directions","year":2022,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Global Water Futures; Natural Sciences and Engineering Research Council of Canada","keywords":"Remote sensing; Polarimetry; Synthetic aperture radar; Microwave; Radar; Sea ice; Sea ice concentration; Environmental science; Decomposition; Scattering; Geology; Sea ice thickness; Computer science; Climatology; Cryosphere; Physics; Ecology; Optics","score_opus":0.009788456554213653,"score_gpt":0.21780644086357015,"score_spread":0.2080179843093565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292543309","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004306934,0.9821416,0.010438552,0.0010561331,0.00039105525,0.00002708547,0.00036428045,0.000055634908,0.0012186888],"genre_scores_gemma":[0.0086134905,0.9793601,0.0097878715,0.00048310388,0.0007385631,0.00002285324,0.00068812864,0.000024378101,0.0002815065],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995454,0.000079936384,0.00006848586,0.00012744633,0.00014709201,0.000031618394],"domain_scores_gemma":[0.9963569,0.0019080836,0.00042365695,0.00017639964,0.0010458914,0.000088989415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032234786,0.0010898126,0.0014744245,0.0024214056,0.00014150226,0.0016995383,0.0012037061,0.0006922477,0.0011041389],"category_scores_gemma":[0.0038268822,0.00043139418,0.00076433894,0.0048451177,0.00081338244,0.0019436186,0.0006916174,0.0011601378,0.0005922469],"study_design_candidate":"not_applicable","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":[0.00009033812,0.0000642868,0.0028154522,0.0066932766,0.00025032804,0.00004792005,0.00006421812,0.0012340081,0.0037438758,0.0020946732,0.0064840466,0.97641754],"study_design_scores_gemma":[0.0001407855,0.00069057825,0.05414475,0.017035337,0.002335373,0.0030021307,0.0011346028,0.016123857,0.015629226,0.025255306,0.8640957,0.00041236432],"about_ca_topic_score_codex":0.0017645361,"about_ca_topic_score_gemma":0.002529341,"teacher_disagreement_score":0.0032234786,"about_ca_system_score_codex":0.00037354857,"about_ca_system_score_gemma":0.0012099601,"threshold_uncertainty_score":0.017047584},"labels":[],"label_agreement":null},{"id":"W4293773804","doi":"10.1016/j.rse.2022.113231","title":"Spaceborne InSAR mapping of landslides and subsidence in rapidly deglaciating terrain, Glacier Bay National Park and Preserve and vicinity, Alaska and British Columbia","year":2022,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":29,"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":"Geology; Glacier; Landslide; Deglaciation; Interferometric synthetic aperture radar; Geomorphology; Glacier mass balance; Physical geography; Outwash plain; Subsidence; Bay; Glacial period; Remote sensing; Oceanography; Synthetic aperture radar; Geography; Structural basin","score_opus":0.0160881025180008,"score_gpt":0.19225715898467238,"score_spread":0.17616905646667158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293773804","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9918147,0.00013961918,0.00046735874,0.000034989716,0.0000058828746,0.000013087101,0.003886584,0.00006237305,0.0035753998],"genre_scores_gemma":[0.9902265,0.00021442948,0.0012332754,0.00001817634,0.0000030326362,0.0000131887455,0.0061611044,0.0000072911134,0.0021229312],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999261,0.000004790178,0.0000037696664,0.000017263621,0.000029525268,0.000018495533],"domain_scores_gemma":[0.99976534,0.000014845507,0.000027543958,0.000014029958,0.00013294166,0.000045315395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001241375,0.00021078254,0.00011598104,0.0009909378,0.0005657117,0.00060745527,0.00017670689,0.00010316083,0.0008153687],"category_scores_gemma":[0.00027298307,0.00011415525,0.000055872188,0.0016160059,0.00016544203,0.00012535209,0.0002279455,0.00014920082,0.00016195931],"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":[0.0001300137,0.000077949284,0.9323084,0.000051443854,0.000054907963,0.0003495401,0.00091634097,0.0061242,0.010308626,0.00025162002,0.007941275,0.041485578],"study_design_scores_gemma":[0.0000060614784,0.0000066488433,0.9896604,0.000014836305,0.000013821757,0.000041668733,0.0009575148,0.0061500994,0.00053427,0.00002845984,0.0025804965,0.000005711696],"about_ca_topic_score_codex":0.75968844,"about_ca_topic_score_gemma":0.91720057,"teacher_disagreement_score":0.24031156,"about_ca_system_score_codex":0.0009990485,"about_ca_system_score_gemma":0.0018926412,"threshold_uncertainty_score":0.48345357},"labels":[],"label_agreement":null},{"id":"W4296159759","doi":"10.1016/j.rse.2022.113268","title":"Harmonizing solar induced fluorescence across spatial scales, instruments, and extraction methods using proximal and airborne remote sensing: A multi-scale study in a soybean field","year":2022,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Institute of Food and Agriculture; European Space Agency; U.S. Department of Agriculture; U.S. Department of Energy","keywords":"Remote sensing; Satellite; Environmental science; Sampling (signal processing); Image resolution; Ground truth; Scale (ratio); Temporal resolution; Geology; Optics; Computer science; Physics; Detector","score_opus":0.03988717502400169,"score_gpt":0.3037225202061395,"score_spread":0.2638353451821378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296159759","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.999006,0.000033140754,0.00074091874,0.000006009603,0.0000014321223,0.000008703131,0.00004834208,0.000011633577,0.00014389511],"genre_scores_gemma":[0.99847955,0.000022575481,0.0012067921,0.0000063750954,0.0000012350532,0.000009382614,0.00012081946,0.000010583196,0.00014269518],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99962115,0.000055474102,0.000013208059,0.0001800454,0.00007279745,0.00005732069],"domain_scores_gemma":[0.9995072,0.00015465911,0.000059860107,0.000057372723,0.00017885881,0.000042099295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080629834,0.00026153418,0.00041337166,0.00049279124,0.0006577474,0.00046254185,0.0004438178,0.0004057136,0.0002573566],"category_scores_gemma":[0.00046041954,0.00021136376,0.00043769745,0.0004643093,0.00041326412,0.00042306288,0.00036787946,0.0002467894,0.00007459477],"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":[0.001691376,0.0013796567,0.31714612,0.00016247098,0.00027750814,0.0003561597,0.0010961856,0.008050913,0.6300782,0.0001872728,0.0002970798,0.03927705],"study_design_scores_gemma":[0.00003672776,0.00041630055,0.9736462,0.000004987761,0.00009069902,0.00010674448,0.00052931596,0.009865357,0.0149019,0.000046747646,0.00033446014,0.000020480558],"about_ca_topic_score_codex":0.029285777,"about_ca_topic_score_gemma":0.03938556,"teacher_disagreement_score":0.029285777,"about_ca_system_score_codex":0.0007667566,"about_ca_system_score_gemma":0.00038343322,"threshold_uncertainty_score":0.05823064},"labels":[],"label_agreement":null},{"id":"W4297103081","doi":"10.1016/j.rse.2022.113284","title":"Assessing a soil-removed semi-empirical model for estimating leaf chlorophyll content","year":2022,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":68,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Leaf area index; Vegetation (pathology); Canopy; Remote sensing; Generality; Environmental science; Empirical modelling; Robustness (evolution); Chlorophyll; Soil science; Computer science; Chemistry; Botany; Geology; Simulation","score_opus":0.05815446451247461,"score_gpt":0.2751296756853252,"score_spread":0.2169752111728506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297103081","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78647333,0.00026663323,0.2111069,0.00028828334,0.00003509813,0.00004669468,0.0002696605,0.0006161656,0.0008972384],"genre_scores_gemma":[0.97240835,0.00005990775,0.026495203,0.00005800476,0.000014040084,0.00003641818,0.00032072692,0.000060574286,0.0005467536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995927,0.00020026266,0.000023308377,0.00008703614,0.00006200103,0.00003472938],"domain_scores_gemma":[0.9941532,0.004853939,0.0002345691,0.0002106634,0.00045637364,0.000091216425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029149142,0.0006381116,0.00057994877,0.0004457794,0.00043318176,0.0009045342,0.0016897991,0.0017076047,0.0005470763],"category_scores_gemma":[0.0076851705,0.00042629646,0.0008092599,0.00041703918,0.00040826376,0.0009652844,0.0006017571,0.0007454779,0.00021769547],"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":[0.00009414806,0.00006001778,0.0037962138,0.000031562337,0.000056679135,0.00002478351,0.000020555406,0.9875728,0.0010877332,0.0003495205,0.00010763874,0.006798465],"study_design_scores_gemma":[0.000006378621,0.000009816974,0.00053227897,0.0000012954963,0.0000058951946,0.0000034434472,0.000003948847,0.99916995,0.00014649858,0.00009944147,0.000017940147,0.0000031482082],"about_ca_topic_score_codex":0.03197076,"about_ca_topic_score_gemma":0.0175022,"teacher_disagreement_score":0.03197076,"about_ca_system_score_codex":0.0013097883,"about_ca_system_score_gemma":0.0013836595,"threshold_uncertainty_score":0.06356937},"labels":[],"label_agreement":null},{"id":"W4307813858","doi":"10.1016/j.rse.2022.113312","title":"Satellite-based daytime urban thermal anisotropy: A comparison of 25 global cities","year":2022,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Fundamental Research Funds for the Central Universities; State Key Laboratory of Remote Sensing Science; Nanjing University; National Natural Science Foundation of China","keywords":"Anisotropy; Zenith; Daytime; Latitude; Environmental science; Solar zenith angle; Nadir; Diurnal temperature variation; Atmospheric sciences; Satellite; Remote sensing; Meteorology; Geography; Geology; Geodesy; Physics; Optics","score_opus":0.013820497954501406,"score_gpt":0.22552081964336387,"score_spread":0.21170032168886246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307813858","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9979512,0.000049475188,0.00011662624,0.000009544867,0.0000031176257,0.0000057641278,0.0010060688,0.0000140444245,0.00084420264],"genre_scores_gemma":[0.99836904,0.00004555582,0.00014705991,0.0000030087638,0.0000029119556,0.00000456446,0.0013073474,0.000007088463,0.00011358644],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997539,0.00004685849,0.00002040718,0.0000610416,0.00005973521,0.000058170535],"domain_scores_gemma":[0.999469,0.000100066376,0.00008794033,0.00008088636,0.00018984392,0.000072299335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042021694,0.00034911395,0.0003225275,0.0014122751,0.00035519508,0.00078100053,0.00021785786,0.00024862753,0.00083799736],"category_scores_gemma":[0.00079483073,0.0001610564,0.0004922539,0.0024752484,0.00029056915,0.0004472099,0.00043960044,0.00014513379,0.00020596695],"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":[0.0016020958,0.00016000657,0.9618014,0.00008232011,0.00038366727,0.00016072138,0.0008616196,0.00814703,0.0049534054,0.00029833268,0.00089161133,0.02065771],"study_design_scores_gemma":[0.00001828589,0.00005968847,0.99597234,0.000004229179,0.0000756951,0.00004776307,0.0007146855,0.0019911,0.0005898838,0.000030456304,0.0004856627,0.000010188244],"about_ca_topic_score_codex":0.06092037,"about_ca_topic_score_gemma":0.13036534,"teacher_disagreement_score":0.06092037,"about_ca_system_score_codex":0.00068645267,"about_ca_system_score_gemma":0.00045028556,"threshold_uncertainty_score":0.12113154},"labels":[],"label_agreement":null},{"id":"W4309030160","doi":"10.1016/j.rse.2022.113335","title":"Paddy rice methane emissions across Monsoon Asia","year":2022,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":70,"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":"College of Engineering, Michigan State University; Division of Chemical, Bioengineering, Environmental, and Transport Systems; HORIZON EUROPE Framework Programme; U.S. Geological Survey; Ministry of Agriculture, Forestry and Fisheries; European Commission; Chinese Academy of Agricultural Sciences; Rural Development Administration; Ministry of Environment; National Natural Science Foundation of China; National Research Foundation; Michigan State University; National Research Foundation of Korea; Gordon and Betty Moore Foundation; National Science Foundation","keywords":"Environmental science; Eddy covariance; Paddy field; Monsoon; Greenhouse gas; Methane; Atmospheric sciences; Climatology; Ecosystem; Agronomy; Ecology","score_opus":0.010074878340658757,"score_gpt":0.2276750739216699,"score_spread":0.21760019558101115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309030160","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99816173,0.000048876882,0.0007044792,0.000029066667,0.0000032923485,0.000005242376,0.00047343495,0.00013102917,0.00044285125],"genre_scores_gemma":[0.9986235,0.000038332342,0.00061785965,0.000011940762,0.0000013565909,0.0000053676476,0.0006001035,0.000011047802,0.00009057051],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9999063,0.000017240971,0.0000063433545,0.00004020074,0.000012582551,0.000017283874],"domain_scores_gemma":[0.9998784,0.000031476848,0.000017162536,0.00002059909,0.000033456385,0.00001880578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003710915,0.00068780524,0.00023973371,0.00033132156,0.00020858197,0.00028727227,0.00044969804,0.00018666487,0.000433541],"category_scores_gemma":[0.00035894205,0.000223089,0.0005056539,0.00048414365,0.0002264266,0.00034652505,0.00037432837,0.00022677942,0.00009450045],"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":[0.0010817188,0.00023206665,0.43355912,0.00017834034,0.00053961773,0.0008594054,0.00031261836,0.47127727,0.052120656,0.00058367767,0.0012815781,0.037974004],"study_design_scores_gemma":[0.000101587255,0.00018195788,0.47717592,0.00001797578,0.00020714708,0.00013172609,0.00020860607,0.5070011,0.013482838,0.00034103243,0.0010998157,0.000050243696],"about_ca_topic_score_codex":0.060973108,"about_ca_topic_score_gemma":0.04687267,"teacher_disagreement_score":0.060973108,"about_ca_system_score_codex":0.00069778535,"about_ca_system_score_gemma":0.00040335947,"threshold_uncertainty_score":0.121236384},"labels":[],"label_agreement":null},{"id":"W4309771280","doi":"10.1016/j.rse.2022.113346","title":"Satellite prediction of coastal hypoxia in the northern Gulf of Mexico","year":2022,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Hypoxia (environmental); Environmental science; Estuary; Ecosystem; Climate change; Satellite; Oceanography; Linear regression; Satellite imagery; Marine ecosystem; Regression analysis; Bloom; Ecology; Oxygen; Geology; Biology","score_opus":0.011015434471462325,"score_gpt":0.16990388710464116,"score_spread":0.15888845263317883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309771280","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9992137,0.000040555173,0.0000622201,0.00006545432,0.000005359619,0.0000016065401,0.00032027718,0.000008146585,0.00028271344],"genre_scores_gemma":[0.99916613,0.000058607337,0.00012361373,0.0000058588753,0.000004116027,0.0000032658288,0.0004504646,0.0000015004106,0.00018642805],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99996233,0.0000059737317,0.0000029584191,0.00001234358,0.0000046394407,0.0000117652135],"domain_scores_gemma":[0.99976736,0.00006518986,0.00006944734,0.00001131489,0.000054369666,0.000032311233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002027618,0.00020774479,0.00016886597,0.000500097,0.00032198953,0.00050402235,0.00026598343,0.0004161119,0.0007099626],"category_scores_gemma":[0.0007514983,0.00012402497,0.00021588478,0.0004526663,0.00016371396,0.00029045646,0.00032163734,0.00023779711,0.000074438656],"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":[0.00019852004,0.000087614324,0.93542993,0.000024510058,0.0000892309,0.00012718701,0.000115751434,0.055473175,0.0010099645,0.00023425152,0.0009522783,0.0062576933],"study_design_scores_gemma":[0.000044537675,0.00003697462,0.91692424,0.00002080608,0.000041412848,0.000020073076,0.00046508794,0.081296995,0.00033077257,0.00009764712,0.00071317115,0.000008236968],"about_ca_topic_score_codex":0.19858892,"about_ca_topic_score_gemma":0.19435357,"teacher_disagreement_score":0.19858892,"about_ca_system_score_codex":0.00086447434,"about_ca_system_score_gemma":0.0006127447,"threshold_uncertainty_score":0.394866},"labels":[],"label_agreement":null},{"id":"W4311973938","doi":"10.1016/j.rse.2022.113415","title":"Coupling ecological concepts with an ocean-colour model: Phytoplankton size structure","year":2022,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","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":"National Centre for Earth Observation; National Research Foundation; Bayworld Centre for Research and Education; Department of Forestry, Fisheries and the Environment; European Commission; National Oceanic and Atmospheric Administration; Sight Research UK; Government of Canada; Natural Environment Research Council; UK Research and Innovation; National Natural Science Foundation of China; Simons Foundation; National Aeronautics and Space Administration","keywords":"Phytoplankton; Environmental science; Sea surface temperature; Oceanography; Satellite; Ocean color; Remote sensing; Climate change; Biomass (ecology); Climatology; Ecology; Geology; Biology; Nutrient; Physics","score_opus":0.009437132271030066,"score_gpt":0.1949633398259609,"score_spread":0.18552620755493082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311973938","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.268016,0.00062917045,0.7161102,0.002112542,0.00020570256,0.00008791352,0.00075329957,0.0005583223,0.011526863],"genre_scores_gemma":[0.9504651,0.00036691612,0.043475967,0.00035228935,0.00008606854,0.0001467174,0.00034881372,0.00018481682,0.004573323],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977094,0.00008377906,0.000012412854,0.0000636311,0.0000339174,0.000035294357],"domain_scores_gemma":[0.99896526,0.0005699278,0.00015995145,0.000059056474,0.00014801486,0.000097764416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008212743,0.00077698013,0.0006777014,0.0005304246,0.00045168053,0.0012990086,0.0017625331,0.0015224788,0.0018028659],"category_scores_gemma":[0.0034794405,0.00050658087,0.0013176999,0.0005877208,0.0009545293,0.001366488,0.0014020082,0.0012581911,0.00031330087],"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":[0.0000096268495,0.000013947121,0.0012636017,0.000013219675,0.00002517533,0.000024387902,0.000025026602,0.98818797,0.00044542004,0.00856952,0.00020746942,0.0012146776],"study_design_scores_gemma":[0.0000032320543,0.0000028897387,0.00020125454,0.000001490168,0.000003641032,0.000003880441,0.000004094394,0.99567455,0.000032216667,0.0039192974,0.000149639,0.0000038483304],"about_ca_topic_score_codex":0.024249112,"about_ca_topic_score_gemma":0.011095634,"teacher_disagreement_score":0.024249112,"about_ca_system_score_codex":0.0016945468,"about_ca_system_score_gemma":0.0012782266,"threshold_uncertainty_score":0.048215926},"labels":[],"label_agreement":null},{"id":"W4317434593","doi":"10.1016/j.rse.2023.113457","title":"Global photosynthetic capacity of C3 biomes retrieved from solar-induced chlorophyll fluorescence and leaf chlorophyll content","year":2023,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Photosynthetically active radiation; Photosynthesis; Biome; Atmospheric sciences; Chlorophyll fluorescence; Remote sensing; Mathematics; Environmental science; Geography; Botany; Physics; Ecosystem; Biology; Ecology","score_opus":0.025180023535474834,"score_gpt":0.20181117389721198,"score_spread":0.17663115036173715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317434593","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99878365,0.00004031907,0.00014851645,0.000012523663,0.0000011669713,0.0000020586751,0.0006072192,0.000019214534,0.0003854215],"genre_scores_gemma":[0.99841034,0.000018624023,0.00014759711,0.000012077854,0.0000012446917,0.0000047485028,0.0013090367,0.00000786977,0.00008842556],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99993026,0.0000069123266,0.000005105173,0.00003063846,0.0000075295166,0.000019565388],"domain_scores_gemma":[0.9998379,0.00004674994,0.000026967635,0.000027142594,0.000032641296,0.00002855756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028492595,0.00032330948,0.00022407598,0.00065848493,0.00025646572,0.00042101886,0.00024149858,0.00036028406,0.0012755704],"category_scores_gemma":[0.00039578116,0.0001139848,0.00048424586,0.0007633385,0.00026417867,0.0005128251,0.00035386172,0.00020572769,0.00026492498],"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":[0.0018287377,0.00013277725,0.6717758,0.00017468708,0.00090454734,0.00024494246,0.00052825996,0.012272426,0.29457933,0.00060010253,0.00071648625,0.016241942],"study_design_scores_gemma":[0.000020425668,0.000027175212,0.98995084,0.000004057922,0.00006966269,0.00004892481,0.00016198095,0.0045633046,0.0047629266,0.00007812148,0.00030041905,0.000012129771],"about_ca_topic_score_codex":0.01620811,"about_ca_topic_score_gemma":0.0102824615,"teacher_disagreement_score":0.01620811,"about_ca_system_score_codex":0.00037593555,"about_ca_system_score_gemma":0.00022096718,"threshold_uncertainty_score":0.032227516},"labels":[],"label_agreement":null},{"id":"W4319298937","doi":"10.1016/j.rse.2023.113494","title":"Dependence of ocean surface filaments on wind speed: An observational study of North Atlantic right whale habitat","year":2023,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bedford Institute of Oceanography; Fisheries and Oceans Canada","funders":"","keywords":"Whale; Right whale; Wind speed; Geology; Synthetic aperture radar; Wind direction; Radar; Wind stress; Remote sensing; Climatology; Oceanography; Geodesy; Environmental science; Computer science","score_opus":0.06847135249242893,"score_gpt":0.27107554151928026,"score_spread":0.20260418902685134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319298937","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99950933,0.000021233402,0.00005145366,0.000010956567,0.0000017172954,0.000001695444,0.00015657731,0.0000014638462,0.00024557518],"genre_scores_gemma":[0.9990478,0.000046187586,0.00011192794,0.000015245072,0.000003933361,0.000005660934,0.0005308779,0.0000029061453,0.00023541755],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999869,0.000035517645,0.0000119772,0.000037901045,0.000018863133,0.000026862795],"domain_scores_gemma":[0.9994332,0.00018695029,0.00014272526,0.00008142056,0.00007116605,0.000084536034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002644803,0.0001517273,0.0001665546,0.00035273962,0.0003497307,0.00031986192,0.00024258252,0.0002832329,0.00084003684],"category_scores_gemma":[0.0008890883,0.0002101877,0.00024439167,0.000598385,0.00028942895,0.00040050398,0.0004633552,0.00026035195,0.00021286056],"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":[0.000044017554,0.000050872277,0.99524105,0.0000076193264,0.000063257314,0.00009325281,0.0005423008,0.000093692106,0.0020569144,0.000021313346,0.00011530515,0.00167042],"study_design_scores_gemma":[6.2426477e-7,0.000008657545,0.9995828,0.0000015021591,0.0000048946818,0.000025380867,0.00018572892,0.000084526844,0.000023902243,0.0000036561116,0.00007687065,0.0000014062538],"about_ca_topic_score_codex":0.057703063,"about_ca_topic_score_gemma":0.16034213,"teacher_disagreement_score":0.057703063,"about_ca_system_score_codex":0.00019612149,"about_ca_system_score_gemma":0.00023623332,"threshold_uncertainty_score":0.11473441},"labels":[],"label_agreement":null},{"id":"W4324373470","doi":"10.1016/j.rse.2023.113529","title":"Estimating and mapping forest age across Canada's forested ecosystems","year":2023,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":81,"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":"Natural Resources Canada; Alliance de recherche numérique du Canada; Canadian Forest Service; Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Disturbance (geology); Remote sensing; Forest ecology; Environmental science; Forest dynamics; Tree canopy; Forest management; Proxy (statistics); Satellite imagery; Forest restoration; Sustainable forest management; Physical geography; Canopy; Geography; Ecosystem; Ecology; Computer science; Agroforestry; Geology","score_opus":0.011713558896371186,"score_gpt":0.21178444372634347,"score_spread":0.2000708848299723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324373470","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95489824,0.0037298538,0.009938871,0.00028680605,0.000025235815,0.00015799064,0.013978038,0.00028647014,0.016698485],"genre_scores_gemma":[0.9721505,0.001929032,0.017026598,0.0000648966,0.000009316391,0.00004884718,0.0042892387,0.000028772356,0.0044528414],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99956745,0.000022382837,0.000020683969,0.00008450869,0.0002035049,0.00010140134],"domain_scores_gemma":[0.99868435,0.00010028797,0.00013134493,0.000046156914,0.00091751665,0.000120294164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057683577,0.00031879058,0.00023572259,0.0036381898,0.0012657028,0.0011814496,0.0005433115,0.00016871613,0.00083380536],"category_scores_gemma":[0.0013568198,0.00013680669,0.00030278513,0.0039315103,0.00029437558,0.0005458466,0.00058583333,0.0002338634,0.0001873862],"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":[0.00007060823,0.000036080386,0.797554,0.000145062,0.00010666802,0.00012430504,0.0015553733,0.0057789823,0.0022905143,0.0011791383,0.0035454715,0.18761373],"study_design_scores_gemma":[0.000003213623,0.000010751233,0.98594034,0.00005366617,0.000028929779,0.000035492558,0.0008480539,0.0057409625,0.00045349178,0.00020045142,0.0066656405,0.000019026988],"about_ca_topic_score_codex":0.98759973,"about_ca_topic_score_gemma":0.99645483,"teacher_disagreement_score":0.0124002695,"about_ca_system_score_codex":0.010392227,"about_ca_system_score_gemma":0.011516527,"threshold_uncertainty_score":0.075401306},"labels":[],"label_agreement":null},{"id":"W4365152881","doi":"10.1016/j.rse.2023.113570","title":"A systematic evaluation of multi-resolution ICESat-2 ATL08 terrain and canopy heights in boreal forests","year":2023,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":64,"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":"Canadian Forest Service; U.S. Forest Service; Nuclear Safety and Security Commission; National Aeronautics and Space Administration","keywords":"Remote sensing; Terrain; Elevation (ballistics); Environmental science; Lidar; Image resolution; Vegetation (pathology); Taiga; Satellite; Canopy; Altimeter; Geology; Geography; Cartography; Computer science","score_opus":0.02576544409067115,"score_gpt":0.26929140238918686,"score_spread":0.2435259582985157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4365152881","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9877101,0.0010999645,0.0055368925,0.00007831034,0.000049721377,0.000041105763,0.002776969,0.0003188707,0.0023881323],"genre_scores_gemma":[0.98696077,0.00023308625,0.008677455,0.000059521873,0.000019267762,0.000022864147,0.0037321872,0.000046257024,0.0002486226],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99883515,0.00017147284,0.00012124231,0.00039955988,0.00040029446,0.0000722878],"domain_scores_gemma":[0.99709153,0.00048861204,0.000595232,0.00045196555,0.0012580962,0.00011460012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024080835,0.00044223428,0.0002679512,0.0010628848,0.00052075175,0.0007079365,0.00064895744,0.00033310594,0.00026355323],"category_scores_gemma":[0.003551733,0.00018500195,0.00038291488,0.0010268375,0.00031058386,0.0010152729,0.00041368822,0.00025619517,0.0001409457],"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":[0.00015264975,0.00012218712,0.90716696,0.00017816106,0.00025112665,0.00011972386,0.0004487212,0.0092972005,0.014906833,0.00028228384,0.0017870985,0.06528714],"study_design_scores_gemma":[0.0000100167235,0.00006550182,0.9815545,0.000042743744,0.000063241925,0.0001475151,0.00026460298,0.011584226,0.0036595445,0.00006979179,0.0025180902,0.000020317351],"about_ca_topic_score_codex":0.036077674,"about_ca_topic_score_gemma":0.09352914,"teacher_disagreement_score":0.036077674,"about_ca_system_score_codex":0.0005054101,"about_ca_system_score_gemma":0.0004894499,"threshold_uncertainty_score":0.07173538},"labels":[],"label_agreement":null},{"id":"W4366609661","doi":"10.1016/j.rse.2023.113580","title":"From spectra to plant functional traits: Transferable multi-trait models from heterogeneous and sparse data","year":2023,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":77,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of Toronto","funders":"","keywords":"Hyperspectral imaging; Plant functional type; Trait; Remote sensing; Partial least squares regression; Biome; Multispectral image; Convolutional neural network; Interpretability; Computer science; Vegetation (pathology); Land cover; Shrubland; Artificial intelligence; Ecology; Biology; Machine learning; Ecosystem; Geography; Land use","score_opus":0.05297961072323462,"score_gpt":0.22375058123760114,"score_spread":0.17077097051436652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366609661","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22287653,0.00082366134,0.7705702,0.0017997043,0.00008606962,0.000110276684,0.0014607575,0.00064537436,0.0016273942],"genre_scores_gemma":[0.952093,0.0005912234,0.040181298,0.00028329037,0.00013182445,0.000258816,0.0011004969,0.0001871239,0.005172937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988809,0.00062677555,0.000040486953,0.00030922802,0.000060079885,0.000082503],"domain_scores_gemma":[0.9853779,0.012284709,0.00083988387,0.0009206088,0.00034661323,0.00023019896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060944846,0.0013212576,0.0018352616,0.0011518553,0.0005271829,0.0015426839,0.0030390944,0.0023991913,0.0025747812],"category_scores_gemma":[0.019842524,0.001297636,0.0020614944,0.0015607094,0.002276751,0.0034207122,0.001669429,0.002797231,0.0005969813],"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":[0.00012113809,0.000054901084,0.002059542,0.000063859276,0.00013580856,0.00010819881,0.000121061414,0.97099215,0.0006165,0.016424745,0.000590305,0.008711821],"study_design_scores_gemma":[0.000011942323,0.000010524781,0.0004104477,0.000005382945,0.000013847578,0.000010797862,0.0000068515646,0.982489,0.000040972438,0.01690946,0.00008079636,0.000009936109],"about_ca_topic_score_codex":0.010228969,"about_ca_topic_score_gemma":0.0067774057,"teacher_disagreement_score":0.010228969,"about_ca_system_score_codex":0.0012981645,"about_ca_system_score_gemma":0.0005762019,"threshold_uncertainty_score":0.032231092},"labels":[],"label_agreement":null},{"id":"W4367185485","doi":"10.1016/j.rse.2023.113600","title":"Validation of Simplified Level 2 Prototype Processor Sentinel-2 fraction of canopy cover, fraction of absorbed photosynthetically active radiation and leaf area index products over North American forests","year":2023,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; McGill University; Natural Resources Canada","funders":"","keywords":"Photosynthetically active radiation; Leaf area index; Remote sensing; Environmental science; Canopy; Imaging spectrometer; Earth observation; Multispectral image; Spectrometer; Satellite; Geography; Ecology","score_opus":0.014860571506283664,"score_gpt":0.23462980947465792,"score_spread":0.21976923796837425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367185485","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9797703,0.000027467262,0.016093226,0.00004193997,0.00002197492,0.00011980331,0.0011873826,0.0014125013,0.001325383],"genre_scores_gemma":[0.9558505,0.000036789857,0.03859141,0.00008442979,0.0000061429046,0.00017934058,0.004397394,0.00017894185,0.0006750359],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934345,0.000121415,0.00004666163,0.0001803736,0.00024566954,0.00006239468],"domain_scores_gemma":[0.99900144,0.00022424664,0.00007644488,0.00015776775,0.00049175846,0.000048398557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001603696,0.00045211174,0.00025145133,0.00035311645,0.0002486285,0.000507588,0.00076234073,0.00031112353,0.0010743589],"category_scores_gemma":[0.0028555726,0.00020863488,0.00029076677,0.00044720457,0.0002470168,0.0008046412,0.00050013396,0.00028155182,0.00049807207],"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":[0.0037091523,0.001677995,0.2522192,0.00052997906,0.00037791577,0.00092812727,0.0014102185,0.22659592,0.26013896,0.0016232074,0.015383781,0.2354055],"study_design_scores_gemma":[0.000483737,0.0017731368,0.1925259,0.000050299466,0.0001183719,0.00036999883,0.00048727606,0.7125895,0.08277664,0.00062840735,0.0081117125,0.00008499712],"about_ca_topic_score_codex":0.010158796,"about_ca_topic_score_gemma":0.0102660535,"teacher_disagreement_score":0.9898412,"about_ca_system_score_codex":0.00040593426,"about_ca_system_score_gemma":0.0005748541,"threshold_uncertainty_score":0.020199358},"labels":[],"label_agreement":null},{"id":"W4379135805","doi":"10.1016/j.rse.2023.113646","title":"Quantification of wetland vegetation communities features with airborne AVIRIS-NG, UAVSAR, and UAV LiDAR data in Peace-Athabasca Delta","year":2023,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Peatlands and Wetlands Ecology","field":"Environmental Science","cited_by":24,"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":"Goddard Space Flight Center; California Institute of Technology; Jet Propulsion Laboratory; National Aeronautics and Space Administration","keywords":"Lidar; Wetland; Environmental science; Remote sensing; Vegetation (pathology); Permafrost; Geography; Geology; Ecology; Oceanography","score_opus":0.025239936979001842,"score_gpt":0.24570605462527545,"score_spread":0.2204661176462736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379135805","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99795747,0.000022929473,0.0006549756,0.000024583505,0.0000023002945,0.000009450402,0.0007548802,0.00007509321,0.0004983571],"genre_scores_gemma":[0.99540126,0.000023507993,0.002912686,0.000008986076,0.0000022266781,0.000011314079,0.0014610393,0.0000070124597,0.00017191378],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998031,0.000022256132,0.000012003145,0.000048811056,0.000067684254,0.000046121142],"domain_scores_gemma":[0.99970585,0.000036118618,0.000042222833,0.000031746877,0.00013181136,0.000052276602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025635032,0.00027162864,0.00012889781,0.0013069578,0.00035761934,0.00044019695,0.00030513303,0.00015744528,0.00031505013],"category_scores_gemma":[0.0005135251,0.00010849869,0.00021229991,0.00094641984,0.0001584619,0.0002492159,0.00033981976,0.00013395873,0.000096073556],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002653763,0.00023954526,0.84692234,0.00009079184,0.00009075289,0.0004961505,0.00075436954,0.026220532,0.04175262,0.00028888803,0.0017642379,0.08111453],"study_design_scores_gemma":[0.000018071323,0.000032234606,0.851673,0.000019558904,0.00003977085,0.000105758736,0.0010268193,0.14001715,0.005745893,0.0000850392,0.001213337,0.000023356073],"about_ca_topic_score_codex":0.20844766,"about_ca_topic_score_gemma":0.29092458,"teacher_disagreement_score":0.7915523,"about_ca_system_score_codex":0.0006786822,"about_ca_system_score_gemma":0.00063494855,"threshold_uncertainty_score":0.4144687},"labels":[],"label_agreement":null},{"id":"W4379644436","doi":"10.1016/j.rse.2023.113661","title":"Multi-grain habitat models that combine satellite sensors with different resolutions explain bird species richness patterns best","year":2023,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Species richness; Normalized Difference Vegetation Index; Remote sensing; Vegetation (pathology); Habitat; Ecology; Environmental science; Geography; Climate change; Biology","score_opus":0.06337076910809564,"score_gpt":0.2402675077400077,"score_spread":0.17689673863191208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379644436","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9503038,0.00062759646,0.043774582,0.00068455865,0.00017503246,0.00004910796,0.0015586851,0.00073309743,0.0020935072],"genre_scores_gemma":[0.99446374,0.000107380605,0.0036407618,0.00014372113,0.000027359749,0.000021651249,0.0007093497,0.00010261447,0.0007834002],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99982834,0.000043216674,0.000010996636,0.00007501962,0.000009934026,0.000032523873],"domain_scores_gemma":[0.9991592,0.00039458193,0.00011572643,0.00017088714,0.00006909194,0.00009050337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080566696,0.00097060093,0.0008476623,0.00057917734,0.0005009099,0.0011601902,0.0009226002,0.0010748919,0.0033935292],"category_scores_gemma":[0.0022884347,0.00058327377,0.0014342996,0.0007685954,0.00038845045,0.002399406,0.0004410105,0.00076700764,0.0011012029],"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":[0.00045351527,0.00028750516,0.14780717,0.00011909025,0.0011867809,0.0001341391,0.00009080046,0.8192507,0.006500796,0.0013924994,0.0032440682,0.019532952],"study_design_scores_gemma":[0.00005297798,0.000036821224,0.030189725,0.000009381853,0.000114088645,0.000047983413,0.00006100485,0.9667153,0.00031233035,0.0020734973,0.0003633182,0.000023617666],"about_ca_topic_score_codex":0.01840555,"about_ca_topic_score_gemma":0.027958674,"teacher_disagreement_score":0.01840555,"about_ca_system_score_codex":0.0007787714,"about_ca_system_score_gemma":0.0005431116,"threshold_uncertainty_score":0.036596835},"labels":[],"label_agreement":null},{"id":"W4383337086","doi":"10.1016/j.rse.2023.113656","title":"Multi-sensor detection of spring breakup phenology of Canada's lakes","year":2023,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Forest Service; Natural Resources Canada; Ste. Anne's Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Phenology; Environmental science; Remote sensing; Satellite imagery; Physical geography; Climatology; Geography; Geology; Ecology","score_opus":0.01087369692888721,"score_gpt":0.18215381069547365,"score_spread":0.17128011376658644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383337086","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9856468,0.00023668843,0.0026481694,0.00007037954,0.000012822287,0.000039911243,0.0077463808,0.00043617777,0.0031627272],"genre_scores_gemma":[0.98240834,0.00013722588,0.0070300633,0.000025449366,0.0000061054484,0.00002210118,0.008998412,0.000020731508,0.0013515567],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99983954,0.0000046339323,0.000004623161,0.00003764593,0.000063514446,0.000049956438],"domain_scores_gemma":[0.9997116,0.000019768788,0.000023949544,0.000012375972,0.00019301575,0.0000391979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015017668,0.00038156845,0.00022046485,0.0013649117,0.0006419769,0.000526454,0.00058417616,0.00022273979,0.0006898804],"category_scores_gemma":[0.00039545714,0.00016107835,0.00018919536,0.0014722323,0.00017435204,0.00025605768,0.0004018513,0.00021156439,0.0001750435],"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":[0.0006067037,0.00041597863,0.6646234,0.00022084516,0.00030611237,0.00034094308,0.0007965506,0.060423296,0.058854584,0.00074199855,0.017128,0.19554149],"study_design_scores_gemma":[0.000030640964,0.000034223103,0.7251973,0.000026200305,0.00006267122,0.000054516786,0.00049987854,0.25546893,0.012901722,0.00015413306,0.00552697,0.000042845637],"about_ca_topic_score_codex":0.88244826,"about_ca_topic_score_gemma":0.9516949,"teacher_disagreement_score":0.117551744,"about_ca_system_score_codex":0.003196221,"about_ca_system_score_gemma":0.003469851,"threshold_uncertainty_score":0.23648804},"labels":[],"label_agreement":null},{"id":"W4385360807","doi":"10.1016/j.rse.2023.113736","title":"Hidden becomes clear: Optical remote sensing of vegetation reveals water table dynamics in northern peatlands","year":2023,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Peatlands and Wetlands Ecology","field":"Environmental Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Carleton University","funders":"Ministère de l’Environnement, de la Protection de la nature et des Parcs; Ympäristöministeriö; H2020 European Research Council; Niemi-säätiö; Koneen Säätiö; Fonds Wetenschappelijk Onderzoek; Ministry of Environment; Academy of Finland; Eesti Teadusagentuur; Vlaamse regering; European Commission; U.S. Department of Energy","keywords":"Peat; Water table; Table (database); Environmental science; Vegetation (pathology); Land cover; Hydrology (agriculture); Physical geography; Land use; Geology; Ecology; Geography; Database; Groundwater; Computer science","score_opus":0.009988906850668833,"score_gpt":0.21889016186421856,"score_spread":0.20890125501354972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385360807","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99676174,0.000116050345,0.0019408882,0.00005062453,0.000012133895,0.0000043281448,0.00027324018,0.000074709475,0.0007663251],"genre_scores_gemma":[0.9974964,0.00004390124,0.0020722495,0.000015123873,0.0000046074997,0.0000026458115,0.00024798088,0.000006036147,0.000111077294],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99988806,0.000021769261,0.0000064476976,0.000032331656,0.00002078712,0.000030626416],"domain_scores_gemma":[0.9998435,0.00004591432,0.000027229837,0.00002158534,0.000039329756,0.000022430566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048215158,0.0002795908,0.00021195892,0.00040516068,0.00019421629,0.00057455286,0.00025382126,0.00027237317,0.00044208506],"category_scores_gemma":[0.0007558427,0.00009956336,0.00037827584,0.00040210612,0.0002072818,0.00073070213,0.0004049954,0.00019039844,0.00008119534],"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":[0.00053156714,0.00018842782,0.78897816,0.00024200353,0.00023894338,0.00045875483,0.00069916144,0.07778295,0.052609846,0.0008283426,0.0010311246,0.0764107],"study_design_scores_gemma":[0.000026034779,0.000108021326,0.66713464,0.00007295168,0.00013551573,0.00024007913,0.0010758532,0.32224396,0.0066299005,0.00068373815,0.0015967557,0.00005259812],"about_ca_topic_score_codex":0.026630491,"about_ca_topic_score_gemma":0.04813952,"teacher_disagreement_score":0.026630491,"about_ca_system_score_codex":0.00025151463,"about_ca_system_score_gemma":0.00046051416,"threshold_uncertainty_score":0.05295098},"labels":[],"label_agreement":null},{"id":"W4385392018","doi":"10.1016/j.rse.2023.113727","title":"A novel semi-empirical model for crop leaf area index retrieval using SAR co- and cross-polarizations","year":2023,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Agriculture and Agri-Food Canada; Natural Resources Canada; University of Toronto","funders":"Canadian Space Agency; National Natural Science Foundation of China; European Space Agency","keywords":"Leaf area index; Remote sensing; Synthetic aperture radar; Environmental science; Vegetation (pathology); Canopy; Backscatter (email); Water content; Growing season; Empirical modelling; Computer science; Geography; Agronomy; Geology","score_opus":0.052172086074466904,"score_gpt":0.3026559379684315,"score_spread":0.2504838518939646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385392018","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009928201,0.00041317154,0.98650336,0.00013489943,0.00004092131,0.000036230696,0.00019289392,0.0003599285,0.0023904452],"genre_scores_gemma":[0.7970822,0.0018111131,0.18001959,0.00022582621,0.00020952683,0.0005936024,0.001238536,0.00028003502,0.018539557],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997584,0.00005000472,0.000014729708,0.00008660739,0.00006386138,0.000026267837],"domain_scores_gemma":[0.99962664,0.00019241236,0.000048847513,0.00002439681,0.00009752825,0.000010225919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006145109,0.0008232939,0.0007868378,0.0005118889,0.00034393388,0.0010217543,0.0021403611,0.0013122469,0.0019658043],"category_scores_gemma":[0.0012240974,0.0006070345,0.00088788616,0.00075663795,0.00048442892,0.0012552674,0.0006565089,0.0010305459,0.0010251293],"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":[0.000025770447,0.000025713493,0.000615085,0.0000677967,0.000032551747,0.000102309554,0.000041532552,0.9726094,0.0020267079,0.006139159,0.00073462795,0.017579451],"study_design_scores_gemma":[0.0000013018018,0.0000027423075,0.000046132885,0.000001372676,0.000002386877,0.000011166767,0.0000013622094,0.99914634,0.00006461304,0.00051634636,0.00020393061,0.0000022660065],"about_ca_topic_score_codex":0.0095019275,"about_ca_topic_score_gemma":0.00694563,"teacher_disagreement_score":0.0095019275,"about_ca_system_score_codex":0.0007002402,"about_ca_system_score_gemma":0.00073784473,"threshold_uncertainty_score":0.018893242},"labels":[],"label_agreement":null},{"id":"W4385437410","doi":"10.1016/j.rse.2023.113725","title":"Melt pond detection on landfast sea ice using dual co-polarized Ku-band backscatter","year":2023,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; European Space Agency","keywords":"Snowmelt; Sea ice; Scatterometer; Backscatter (email); Snow; Sea ice thickness; Geology; Melt pond; Sea ice concentration; Wind speed; Arctic; Arctic ice pack; Remote sensing; Special sensor microwave/imager; Environmental science; Atmospheric sciences; Climatology; Oceanography; Geomorphology; Brightness temperature; Physics; Microwave","score_opus":0.01859329665053337,"score_gpt":0.2209091316058237,"score_spread":0.20231583495529035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385437410","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9920283,0.00007442201,0.006351205,0.000021448179,0.000017281596,0.0000074331606,0.00019644179,0.000119591496,0.0011839299],"genre_scores_gemma":[0.99501085,0.00007637113,0.0039187977,0.000018419543,0.000009242428,0.0000057188126,0.00025336185,0.00000996615,0.0006973428],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99995625,0.0000048737193,0.0000015964994,0.000011409661,0.000012808301,0.00001321362],"domain_scores_gemma":[0.99992824,0.000017085295,0.0000089451205,0.0000061742912,0.000028900495,0.0000106656535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009885525,0.00020930036,0.00019409986,0.0005199126,0.00020502858,0.00027972547,0.00012755394,0.00019385645,0.00069775945],"category_scores_gemma":[0.00013565512,0.000109895744,0.0001718743,0.0003436204,0.00016330705,0.00026833263,0.0002563452,0.00012684017,0.00015254416],"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":[0.0014819088,0.0001274096,0.14085314,0.00011498057,0.000104042745,0.00050560676,0.00038713196,0.005789995,0.74329215,0.0002893841,0.0012955437,0.10575875],"study_design_scores_gemma":[0.00010698026,0.0002585679,0.5756021,0.000041378513,0.00025390493,0.00059852685,0.0007623753,0.18762836,0.23148632,0.0002946566,0.002913617,0.00005307136],"about_ca_topic_score_codex":0.002245548,"about_ca_topic_score_gemma":0.006814141,"teacher_disagreement_score":0.002245548,"about_ca_system_score_codex":0.00007292885,"about_ca_system_score_gemma":0.00013500616,"threshold_uncertainty_score":0.0044649243},"labels":[],"label_agreement":null},{"id":"W4385541303","doi":"10.1016/j.rse.2023.113708","title":"S2MetNet: A novel dataset and deep learning benchmark for methane point source quantification using Sentinel-2 satellite imagery","year":2023,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Remote sensing; Deep learning; Computer science; Methane; Environmental science; Benchmark (surveying); Multispectral image; Satellite; Satellite imagery; Meteorology; Artificial intelligence; Geology","score_opus":0.02151961555101567,"score_gpt":0.24476200177843313,"score_spread":0.22324238622741746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385541303","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2941482,0.002873945,0.02632905,0.0018937817,0.001214035,0.0006882488,0.63114846,0.027072899,0.014631389],"genre_scores_gemma":[0.10442121,0.0004209887,0.02726962,0.00041614016,0.00009246272,0.00041961862,0.8619017,0.00073327194,0.004325081],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916196,0.00011845881,0.000062726685,0.00029661582,0.00023643582,0.00012373787],"domain_scores_gemma":[0.99908864,0.00019293153,0.00008094368,0.00023105025,0.00029995517,0.00010640537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011479871,0.003287544,0.00083930197,0.0023189841,0.0009974464,0.0011111838,0.003696727,0.002827087,0.0037970105],"category_scores_gemma":[0.0032462797,0.0005790137,0.0011646599,0.0024445995,0.00076646806,0.0016283427,0.0017416062,0.001657467,0.002852613],"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":[0.0015838987,0.0014482198,0.015517975,0.0015125361,0.00073175796,0.0004928407,0.00011514489,0.12677987,0.009449871,0.0035092956,0.7224872,0.11637141],"study_design_scores_gemma":[0.0014976779,0.00089693285,0.02723247,0.00031307523,0.00030321034,0.00075843843,0.0004814762,0.7538253,0.027291205,0.011088176,0.17603432,0.00027767735],"about_ca_topic_score_codex":0.058349155,"about_ca_topic_score_gemma":0.09056578,"teacher_disagreement_score":0.058349155,"about_ca_system_score_codex":0.0015204527,"about_ca_system_score_gemma":0.0023577386,"threshold_uncertainty_score":0.11601907},"labels":[],"label_agreement":null},{"id":"W4385657491","doi":"10.1016/j.rse.2023.113747","title":"Wildfire likelihood in Canadian treed peatlands based on remote-sensing time-series of surface conditions","year":2023,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Carleton University","funders":"Canadian Forest Service; Natural Sciences and Engineering Research Council of Canada; Natural Resources Canada; Carleton University","keywords":"Environmental science; Peat; Remote sensing; Boreal; Vegetation (pathology); Multispectral image; Moisture; Meteorology; Geography","score_opus":0.00655859174901989,"score_gpt":0.19986609654856924,"score_spread":0.19330750479954933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385657491","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9982768,0.00009654568,0.00009101738,0.00002771374,0.0000028495535,0.0000037640013,0.0010766792,0.000008434803,0.00041615593],"genre_scores_gemma":[0.9987185,0.000054646287,0.00009161023,0.0000075749817,0.0000015813619,0.0000019264698,0.0008949094,0.0000021887067,0.00022720892],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99969625,0.000029401046,0.000018458724,0.00007300775,0.000074271265,0.00010859642],"domain_scores_gemma":[0.99842864,0.00042690468,0.00029818143,0.00007096837,0.0004579479,0.00031734104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070136855,0.00024101048,0.00019872613,0.0010569275,0.0006218689,0.0009248961,0.0005512281,0.00034835725,0.0013383401],"category_scores_gemma":[0.0022998827,0.00022299669,0.00041820406,0.00082968286,0.0004988565,0.00044712637,0.00037421635,0.00035059365,0.0001398166],"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":[0.000108537395,0.00002072413,0.9934227,0.000009701447,0.00006195006,0.000050183902,0.00009352637,0.0030048024,0.00053053396,0.00008941583,0.00033547808,0.002272381],"study_design_scores_gemma":[0.0000030775352,0.000005926738,0.9949536,0.0000052013584,0.000010549645,0.000025648074,0.00016729272,0.0045547746,0.000085668486,0.000026499303,0.00015457858,0.00000730221],"about_ca_topic_score_codex":0.97033405,"about_ca_topic_score_gemma":0.9879539,"teacher_disagreement_score":0.029665947,"about_ca_system_score_codex":0.005144509,"about_ca_system_score_gemma":0.0041869334,"threshold_uncertainty_score":0.059681237},"labels":[],"label_agreement":null},{"id":"W4388507637","doi":"10.1016/j.rse.2023.113893","title":"Simulation of urban thermal anisotropy at remote sensing pixel scales: Evaluating three schemes using GUTA-T over Toulouse city","year":2023,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Anisotropy; Remote sensing; Radiance; Mean squared error; Parametric statistics; Computer science; Environmental science; Parametrization (atmospheric modeling); Field (mathematics); Algorithm; Meteorology; Geology; Mathematics; Physics; Statistics; Optics; Radiative transfer","score_opus":0.047810307390235,"score_gpt":0.288674591278919,"score_spread":0.24086428388868397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388507637","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99386346,0.00009849071,0.0023108863,0.0002714524,0.00004888098,0.000041943782,0.0005522273,0.00028645308,0.0025262071],"genre_scores_gemma":[0.9963246,0.00004057644,0.0027254482,0.000036261245,0.000009909944,0.000026232121,0.00035495881,0.000031788004,0.00045027936],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966466,0.000109089466,0.000014726515,0.00006122358,0.000037781,0.00011243046],"domain_scores_gemma":[0.9986895,0.00074426475,0.000085217696,0.00010903522,0.0001875241,0.0001844665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087870605,0.0010986024,0.0012192966,0.0006720652,0.0009665332,0.0012448344,0.0017168256,0.0024979895,0.0022502323],"category_scores_gemma":[0.0018938584,0.00063642167,0.0012625271,0.0010432468,0.0012912116,0.00068108836,0.0007948747,0.0011615554,0.00022564534],"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":[0.00014909435,0.00015473655,0.0033412806,0.000022253065,0.000027486994,0.00007038435,0.000025409776,0.99401855,0.0005170089,0.00023032693,0.00019972486,0.0012436361],"study_design_scores_gemma":[0.00010106285,0.000058416266,0.0018914068,0.0000036116658,0.0000139632075,0.0000058826645,0.000043837677,0.9974704,0.00025669424,0.000059425154,0.00008592648,0.000009309852],"about_ca_topic_score_codex":0.15525222,"about_ca_topic_score_gemma":0.08155909,"teacher_disagreement_score":0.15525222,"about_ca_system_score_codex":0.0018291406,"about_ca_system_score_gemma":0.0014643715,"threshold_uncertainty_score":0.3086971},"labels":[],"label_agreement":null},{"id":"W4389469222","doi":"10.1016/j.rse.2023.113932","title":"An AI approach to operationalise global daily PlanetScope satellite imagery for river water masking","year":2023,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":47,"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":"Engineering and Physical Sciences Research Council","keywords":"Remote sensing; Computer science; Convolutional neural network; Environmental science; Satellite imagery; Biome; Artificial neural network; Artificial intelligence; Ecosystem; Geology","score_opus":0.013975450746585006,"score_gpt":0.2538941105342792,"score_spread":0.23991865978769417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389469222","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36297527,0.0009510272,0.55628157,0.0019627174,0.00047033821,0.0010380171,0.023960454,0.021840077,0.030520566],"genre_scores_gemma":[0.60792524,0.00032323305,0.36913878,0.00032100623,0.00007980401,0.0003227688,0.015884455,0.00030601514,0.0056987903],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981505,0.000016431477,0.000014726118,0.000060234448,0.00006311113,0.00003041175],"domain_scores_gemma":[0.9997516,0.000034361965,0.000035906167,0.000046110275,0.00011562356,0.000016463136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034435303,0.0005531608,0.00019571817,0.0012435965,0.00021916103,0.0006060281,0.0005508867,0.0004568001,0.0032235489],"category_scores_gemma":[0.0013268705,0.00021168403,0.000491325,0.0009807602,0.00015083281,0.00082389975,0.0006617965,0.0005648699,0.0014431827],"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":[0.00024126361,0.00026292735,0.047354262,0.0002931673,0.00019596268,0.000270366,0.00033953314,0.08100601,0.078070626,0.0034755117,0.03541371,0.75307655],"study_design_scores_gemma":[0.00003058271,0.000112504975,0.05219695,0.00007683382,0.00005727131,0.00013712991,0.00041612057,0.8873825,0.02806775,0.0035551277,0.027925901,0.000041289706],"about_ca_topic_score_codex":0.02181533,"about_ca_topic_score_gemma":0.031810414,"teacher_disagreement_score":0.02181533,"about_ca_system_score_codex":0.00047139364,"about_ca_system_score_gemma":0.000644702,"threshold_uncertainty_score":0.043376684},"labels":[],"label_agreement":null},{"id":"W4390086207","doi":"10.1016/j.rse.2023.113956","title":"A rapid high-resolution multi-sensory urban flood mapping framework via DEM upscaling","year":2023,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada; University of Waterloo","funders":"Natural Resources Canada; National Natural Science Foundation of China","keywords":"Remote sensing; Flood myth; Digital elevation model; Lidar; Segmentation; Environmental science; Computer science; Geography; Artificial intelligence","score_opus":0.024052790849551847,"score_gpt":0.23360953176569932,"score_spread":0.20955674091614748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390086207","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044038363,0.0002559395,0.9411432,0.00024574384,0.0001580477,0.000150592,0.0032722582,0.006689765,0.004046125],"genre_scores_gemma":[0.44090375,0.0002545473,0.55171084,0.00011680607,0.000073649426,0.00020163906,0.004727521,0.0004120712,0.0015991015],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973994,0.00003505503,0.00001479387,0.00006284106,0.000112344715,0.00003504547],"domain_scores_gemma":[0.999534,0.00008921871,0.000033863147,0.000116426185,0.00019753378,0.000028954248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055830803,0.0007181334,0.0005475012,0.0015139255,0.00033449344,0.0009858584,0.0015857817,0.0005881405,0.0032874488],"category_scores_gemma":[0.0016854785,0.0005056638,0.00076736294,0.0015400449,0.0002804237,0.0013530279,0.0015518094,0.0007066517,0.0010254419],"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":[0.00009278177,0.00023612173,0.0034046145,0.00014810234,0.00016928792,0.00017328693,0.00016599364,0.7085187,0.013513344,0.0057788156,0.008215848,0.25958318],"study_design_scores_gemma":[0.000009175194,0.0000087463595,0.0007174768,0.000006679674,0.000010610989,0.000019524936,0.000024932462,0.9944675,0.0009882082,0.0021059187,0.0016271449,0.000014050012],"about_ca_topic_score_codex":0.017779358,"about_ca_topic_score_gemma":0.028888127,"teacher_disagreement_score":0.017779358,"about_ca_system_score_codex":0.0004606443,"about_ca_system_score_gemma":0.0009386878,"threshold_uncertainty_score":0.035351753},"labels":[],"label_agreement":null},{"id":"W4390569824","doi":"10.1016/j.rse.2023.113981","title":"Modeling transpiration using solar-induced chlorophyll fluorescence and photochemical reflectance index synergistically in a closed-canopy winter wheat ecosystem","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Photochemical Reflectance Index; Transpiration; Vapour Pressure Deficit; Leaf area index; Stomatal conductance; Chlorophyll fluorescence; Eddy covariance; Canopy; Environmental science; Atmospheric sciences; Mean squared error; Remote sensing; Ecosystem; Chlorophyll; Mathematics; Botany; Photosynthesis; Physics; Ecology; Biology; Statistics","score_opus":0.014461220856635185,"score_gpt":0.22416126491016672,"score_spread":0.20970004405353154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390569824","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9984491,0.000023540024,0.001276145,0.000020995956,0.000003631398,0.0000034820484,0.00002074426,0.000025448175,0.00017689315],"genre_scores_gemma":[0.9988451,0.000017071085,0.000938716,0.0000058539385,0.0000016668248,0.000003873755,0.000025050167,0.0000044965745,0.000158157],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999355,0.000012697036,0.000004587676,0.00002643767,0.0000058336805,0.000015006792],"domain_scores_gemma":[0.9998398,0.00008573885,0.000015554622,0.0000097558495,0.000017399594,0.000031699547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023839484,0.00044964065,0.0003596535,0.00017864139,0.0004289931,0.0006973376,0.0005103294,0.0007717105,0.00035522453],"category_scores_gemma":[0.0004385522,0.0003375167,0.0005663532,0.00024122659,0.00028464844,0.0007626437,0.0002919089,0.00034229216,0.000039317605],"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":[0.00020776912,0.00026991984,0.023817291,0.000041087485,0.000114955554,0.00014325003,0.00007797087,0.94331735,0.025076944,0.00026702692,0.000097104356,0.0065693166],"study_design_scores_gemma":[0.00001673256,0.000043056843,0.0057292823,8.870148e-7,0.000018699784,0.000007982394,0.00002132379,0.9932193,0.00084793824,0.00006405015,0.000024935769,0.0000058575592],"about_ca_topic_score_codex":0.031159256,"about_ca_topic_score_gemma":0.030905932,"teacher_disagreement_score":0.031159256,"about_ca_system_score_codex":0.0007823939,"about_ca_system_score_gemma":0.000669974,"threshold_uncertainty_score":0.06195575},"labels":[],"label_agreement":null},{"id":"W4390821331","doi":"10.1016/j.rse.2023.113951","title":"Drivers of deciduous forest near-infrared reflectance: A 3D radiative transfer modeling exercise based on ground lidar","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Japan Aerospace Exploration Agency; Natural Sciences and Engineering Research Council of Canada","keywords":"Canopy; Remote sensing; Environmental science; Albedo (alchemy); Radiative transfer; Atmospheric radiative transfer codes; Lidar; Leaf area index; Tree canopy; Atmospheric sciences; Geology; Geography; Ecology; Optics; Physics","score_opus":0.010195813168930621,"score_gpt":0.2150760187654343,"score_spread":0.2048802055965037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390821331","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99817,0.000024712044,0.00073951937,0.00008325177,0.0000056791423,0.000005828292,0.00045677432,0.000040943873,0.00047318067],"genre_scores_gemma":[0.9987463,0.000024546034,0.0004981506,0.000009470193,0.000004090557,0.0000055290684,0.00037945202,0.000015221282,0.0003173518],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981564,0.000059859114,0.000007501634,0.00005129816,0.00002368297,0.00004196892],"domain_scores_gemma":[0.99951804,0.0002926748,0.000052640156,0.000034039946,0.000056644767,0.000045991128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009121682,0.00048196592,0.00026907813,0.00041461005,0.0005189773,0.000867499,0.0007231675,0.00065749197,0.001852622],"category_scores_gemma":[0.0011721356,0.000614342,0.0011712221,0.0004705072,0.00021100513,0.0010542937,0.0003502898,0.00054990227,0.00031377317],"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":[0.00039138642,0.0004944298,0.43572506,0.00006671295,0.0003462804,0.000493382,0.0005327827,0.53382236,0.00994951,0.004249695,0.0020160456,0.011912392],"study_design_scores_gemma":[0.000045270514,0.00004763364,0.1268444,0.000009475768,0.00008421973,0.000058497255,0.00045562378,0.8692243,0.0012459382,0.0009433741,0.001007726,0.000033601624],"about_ca_topic_score_codex":0.061541956,"about_ca_topic_score_gemma":0.053412404,"teacher_disagreement_score":0.061541956,"about_ca_system_score_codex":0.0008133251,"about_ca_system_score_gemma":0.0009446807,"threshold_uncertainty_score":0.12236744},"labels":[],"label_agreement":null},{"id":"W4391243487","doi":"10.1016/j.rse.2024.114013","title":"Towards a standardized, ground-based network of hyperspectral measurements: Combining time series from autonomous field spectrometers with Sentinel-2","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"European Cooperation in Science and Technology","keywords":"Remote sensing; Hyperspectral imaging; Normalized Difference Vegetation Index; Environmental science; Multispectral image; Spectrometer; Atmospheric correction; Satellite; Vegetation (pathology); Imaging spectrometer; Photochemical Reflectance Index; Enhanced vegetation index; Image resolution; Geology; Vegetation Index; Optics; Climate change; Physics","score_opus":0.009718473820443614,"score_gpt":0.200843966615118,"score_spread":0.1911254927946744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391243487","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13209738,0.0007718771,0.8529692,0.0008955811,0.0004359563,0.0005506032,0.004234569,0.0037831508,0.004261676],"genre_scores_gemma":[0.2450475,0.0005100818,0.7354225,0.00052605127,0.0002502182,0.00046409518,0.015599746,0.0005734244,0.001606327],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9977756,0.00050806947,0.00016956875,0.00061725144,0.00072302396,0.00020639223],"domain_scores_gemma":[0.99371135,0.0003786329,0.0007779652,0.0011217704,0.003567822,0.0004426097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008533265,0.0010989582,0.00088977686,0.0028671012,0.0005796504,0.0023238687,0.0021389034,0.0012098046,0.00074521825],"category_scores_gemma":[0.0052795806,0.00045864965,0.00047952047,0.0030576407,0.0006773566,0.0030878508,0.002841403,0.001521757,0.00096387],"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":[0.0005973336,0.0016067736,0.09331149,0.0005480976,0.0005519941,0.00021160448,0.00083091477,0.04766647,0.27028644,0.012176195,0.022925787,0.54928684],"study_design_scores_gemma":[0.00041791282,0.0015938692,0.24012014,0.00057806296,0.0008429681,0.00053785974,0.002024075,0.45053056,0.14415127,0.026075369,0.13273634,0.0003916504],"about_ca_topic_score_codex":0.009530044,"about_ca_topic_score_gemma":0.015170878,"teacher_disagreement_score":0.009530044,"about_ca_system_score_codex":0.0009872175,"about_ca_system_score_gemma":0.0045103687,"threshold_uncertainty_score":0.045128763},"labels":[],"label_agreement":null},{"id":"W4391628542","doi":"10.1016/j.rse.2024.114025","title":"Simultaneous estimation of leaf directional-hemispherical reflectance and transmittance from multi-angular canopy reflectance","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Young Scientists Fund; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Remote sensing; Canopy; Transmittance; Leaf area index; Hyperspectral imaging; Terrain; Environmental science; Radiative transfer; Reflectivity; Atmospheric radiative transfer codes; Optics; Tree canopy; Materials science; Geography; Physics; Botany","score_opus":0.0077388174235074915,"score_gpt":0.2331754554759079,"score_spread":0.2254366380524004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391628542","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6893146,0.0007928557,0.3038744,0.00008579098,0.000047371417,0.000036699752,0.0009371328,0.0012863808,0.0036247724],"genre_scores_gemma":[0.84908247,0.0005987766,0.14661628,0.000042656284,0.000023038876,0.00003264345,0.0012203929,0.000180944,0.0022027963],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99981827,0.000025105512,0.000005956352,0.00006390396,0.0000653882,0.000021316913],"domain_scores_gemma":[0.9998043,0.000056205525,0.000022203285,0.00003115319,0.00007171876,0.000014468207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025468034,0.00048601654,0.0005329382,0.00051189377,0.00018134217,0.00048011643,0.000258895,0.00030172814,0.0007017332],"category_scores_gemma":[0.00037306707,0.0003189523,0.00046286779,0.00061600324,0.00010742729,0.0007516454,0.00031713457,0.000356503,0.00058313424],"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":[0.00024768108,0.00006487153,0.012782451,0.00016905855,0.000103946164,0.00006240979,0.00010250771,0.0077298474,0.8269485,0.00018598017,0.00054378045,0.15105905],"study_design_scores_gemma":[0.00006279287,0.00012249366,0.20690225,0.00002088638,0.00028949624,0.00057568896,0.00018440124,0.42857185,0.35877916,0.0005909106,0.003788851,0.00011117387],"about_ca_topic_score_codex":0.0026368971,"about_ca_topic_score_gemma":0.008730866,"teacher_disagreement_score":0.0026368971,"about_ca_system_score_codex":0.00013795134,"about_ca_system_score_gemma":0.00032985825,"threshold_uncertainty_score":0.0052431226},"labels":[],"label_agreement":null},{"id":"W4391713552","doi":"10.1016/j.rse.2024.114043","title":"Deriving photosystem-level red chlorophyll fluorescence emission by combining leaf chlorophyll content and canopy far-red solar-induced fluorescence: Possibilities and challenges","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Canopy; Photosystem II; Chlorophyll fluorescence; Far-red; Photosynthesis; Environmental science; Chlorophyll; Remote sensing; Atmospheric sciences; Botany; Physics; Biology; Red light; Geography","score_opus":0.03525740774551223,"score_gpt":0.21779623225916392,"score_spread":0.1825388245136517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391713552","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5363536,0.0079220105,0.44285932,0.0011898614,0.00016843899,0.00017075964,0.0011788501,0.001536425,0.008620833],"genre_scores_gemma":[0.71360046,0.0058105104,0.27609488,0.00064416387,0.000106711756,0.00010889597,0.0011404111,0.00039208846,0.0021019394],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993351,0.0001617735,0.000027895245,0.00020664405,0.00022315675,0.000045405755],"domain_scores_gemma":[0.99937785,0.00028065927,0.00004894679,0.00009757691,0.00016150062,0.000033387092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021906756,0.0010234626,0.0013248256,0.0005483167,0.00035332152,0.00166805,0.0014989097,0.0010396284,0.00062827126],"category_scores_gemma":[0.0018891087,0.0005202727,0.0007146135,0.00094089727,0.00046683094,0.0020615729,0.00085240987,0.0017492924,0.0004895208],"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":[0.00023358225,0.00024967876,0.033639807,0.00074860395,0.00055361836,0.00017771102,0.0002893362,0.011777025,0.73996544,0.0035465953,0.00095074385,0.20786783],"study_design_scores_gemma":[0.00013441569,0.000318356,0.087483734,0.0002632934,0.00071524095,0.00086326053,0.000733287,0.20279585,0.6550187,0.03434918,0.017017314,0.00030741337],"about_ca_topic_score_codex":0.003002501,"about_ca_topic_score_gemma":0.008895983,"teacher_disagreement_score":0.003002501,"about_ca_system_score_codex":0.0006597422,"about_ca_system_score_gemma":0.00086346513,"threshold_uncertainty_score":0.011585534},"labels":[],"label_agreement":null},{"id":"W4391731162","doi":"10.1016/j.rse.2024.114042","title":"Interannual variations and trends of gross primary production and transpiration of four mature deciduous broadleaf forest sites during 2000–2020","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada","funders":"National Science Foundation","keywords":"Deciduous; Primary production; Environmental science; Production (economics); Transpiration; Remote sensing; Forestry; Geography; Agroforestry; Ecology; Botany; Economics; Biology; Ecosystem","score_opus":0.005404874050587417,"score_gpt":0.18273040331000895,"score_spread":0.17732552925942152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391731162","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9981,0.00005063616,0.000073299954,0.000022490132,0.0000044109975,0.0000018963342,0.0013560985,0.0000092700075,0.00038204322],"genre_scores_gemma":[0.99606687,0.000045881436,0.00011595518,0.000014883836,0.000005076881,0.000006991578,0.0033036922,0.0000033404724,0.0004373775],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999232,0.000008819288,0.000009263148,0.000021832526,0.000013358129,0.000023484123],"domain_scores_gemma":[0.99960333,0.00009269413,0.000118877775,0.00002426748,0.000082443,0.000078290526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031161038,0.00020711921,0.00012802992,0.00082408,0.00023210586,0.00041050717,0.00026168337,0.0003898431,0.0009326168],"category_scores_gemma":[0.0004574982,0.00016098194,0.00023053112,0.0007617402,0.00018474122,0.00038163882,0.00037581136,0.00021867114,0.0002518028],"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":[0.00039127772,0.00008206822,0.98759055,0.000030886462,0.00009364417,0.00014320733,0.00034439535,0.000994402,0.0052645975,0.00008884834,0.00066955015,0.004306603],"study_design_scores_gemma":[0.0000025710076,0.000011252062,0.9988605,0.0000019409592,0.00000999197,0.000044219447,0.00008873818,0.00050528906,0.00018504646,0.000010751968,0.00027719565,0.000002454431],"about_ca_topic_score_codex":0.023306612,"about_ca_topic_score_gemma":0.04355832,"teacher_disagreement_score":0.023306612,"about_ca_system_score_codex":0.0003934244,"about_ca_system_score_gemma":0.00021357289,"threshold_uncertainty_score":0.046341896},"labels":[],"label_agreement":null},{"id":"W4391836761","doi":"10.1016/j.rse.2024.114052","title":"Arctic ice-wedge landscape mapping by CNN using a fusion of Radarsat constellation Mission and ArcticDEM","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada; Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland; University of Guelph","funders":"","keywords":"Remote sensing; Synthetic aperture radar; Permafrost; Arctic; Sea ice; Geology; Computer science; Climatology","score_opus":0.03504165931702836,"score_gpt":0.22668486208736896,"score_spread":0.1916432027703406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391836761","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8631576,0.0011272288,0.11296092,0.0003635758,0.00035261904,0.00010118055,0.00661098,0.0028666114,0.012459279],"genre_scores_gemma":[0.9230409,0.00050282583,0.06179431,0.00014106969,0.00013661732,0.000053881326,0.008794807,0.00010277063,0.0054327883],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989617,0.0000056551016,0.0000030971032,0.000038497474,0.000027465978,0.000029119785],"domain_scores_gemma":[0.9999256,0.0000054801762,0.000008120707,0.000011710413,0.00003891807,0.000010200882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015590651,0.0005466239,0.0003200974,0.0013421049,0.00023146275,0.0004071844,0.0003705994,0.00029457154,0.0016395232],"category_scores_gemma":[0.00023135949,0.0002266441,0.00042196005,0.0013905724,0.00010103836,0.000504372,0.0004974448,0.0002496323,0.000655641],"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":[0.00036509163,0.00038715024,0.06841474,0.00016811627,0.00049595913,0.0005785921,0.00023362569,0.1060685,0.09548801,0.0012260247,0.022725563,0.70384854],"study_design_scores_gemma":[0.00002654668,0.000069195805,0.057695314,0.000019947292,0.0001741591,0.00015690568,0.00020793386,0.9232988,0.010804542,0.001056487,0.0064563653,0.00003372386],"about_ca_topic_score_codex":0.024832882,"about_ca_topic_score_gemma":0.05666702,"teacher_disagreement_score":0.024832882,"about_ca_system_score_codex":0.00028040019,"about_ca_system_score_gemma":0.0005477246,"threshold_uncertainty_score":0.049376667},"labels":[],"label_agreement":null},{"id":"W4391868252","doi":"10.1016/j.rse.2024.114032","title":"Benthic habitat sediments mapping in coral reef area using amalgamation of multi-source and multi-modal remote sensing data","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Coral and Marine Ecosystems Studies","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing; Wuhan University; National Natural Science Foundation of China; Ministry of Natural Resources of the People's Republic of China; Ministry of Natural Resources","keywords":"Remote sensing; Multispectral image; Coral reef; Terrain; Bathymetry; Computer science; Environmental science; Data mining; Geology; Cartography; Geography; Oceanography","score_opus":0.0647829771155526,"score_gpt":0.2672115506068876,"score_spread":0.20242857349133503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391868252","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9896891,0.00018349713,0.007579685,0.000048904647,0.000023865197,0.000020176552,0.0011856967,0.00015622943,0.0011128868],"genre_scores_gemma":[0.9833099,0.000104068095,0.014846048,0.000014649742,0.00001160911,0.000019213485,0.0011372182,0.0000109716575,0.0005463738],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99979013,0.000026555288,0.000016376252,0.000071346905,0.000051089213,0.000044577635],"domain_scores_gemma":[0.999841,0.000020165573,0.000024017998,0.000015301472,0.00007354485,0.00002593304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002753508,0.00033144228,0.00019207361,0.0017068865,0.00022283517,0.00044922103,0.00023603046,0.00021997622,0.0007357611],"category_scores_gemma":[0.00043408477,0.00016572545,0.0005111753,0.0014252166,0.00008910913,0.00034280206,0.00044649962,0.00013389565,0.0002011261],"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":[0.00055772794,0.00034643622,0.5250537,0.0003013417,0.00040036248,0.0004407754,0.0006989137,0.027007932,0.12516068,0.0004130406,0.0020023126,0.3176169],"study_design_scores_gemma":[0.000025705614,0.000097132506,0.898365,0.000023656517,0.00015447053,0.00013633746,0.0006544216,0.09400368,0.004742867,0.00016002565,0.0016031382,0.000033430468],"about_ca_topic_score_codex":0.013338345,"about_ca_topic_score_gemma":0.026083747,"teacher_disagreement_score":0.013338345,"about_ca_system_score_codex":0.00018316685,"about_ca_system_score_gemma":0.00026512385,"threshold_uncertainty_score":0.026521444},"labels":[],"label_agreement":null},{"id":"W4392150906","doi":"10.1016/j.rse.2024.114049","title":"Estimating volume of large slow-moving deep-seated landslides in northern Canada from DInSAR-derived 2D and constrained 3D deformation rates","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Landslides and related hazards","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Geological Survey of Canada; Natural Resources Canada","funders":"Natural Resources Canada","keywords":"Landslide; Geology; Deformation (meteorology); Permafrost; Seismology; Geodesy; Geomorphology","score_opus":0.004349715945038936,"score_gpt":0.1935230907745747,"score_spread":0.18917337482953575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392150906","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98608243,0.00035892363,0.003482467,0.000049843515,0.000007841466,0.000033117398,0.0055564465,0.0003369339,0.0040920586],"genre_scores_gemma":[0.98793703,0.00029401836,0.0042482573,0.000016503358,0.000002776983,0.000014428062,0.0058503966,0.000031821583,0.0016047272],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99984694,0.0000026713276,0.000004335521,0.000031509317,0.000062551866,0.000051934923],"domain_scores_gemma":[0.9998054,0.000011507593,0.000019539562,0.000011562398,0.0001182031,0.000033736556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012274456,0.0006745321,0.00024122014,0.0016800667,0.0006363283,0.000839116,0.00043456524,0.00021432871,0.00090318173],"category_scores_gemma":[0.00035600987,0.0002331334,0.0003035259,0.001602227,0.00032326725,0.00021632778,0.00040881912,0.00021589774,0.0002160899],"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":[0.0003620478,0.000119430486,0.76723856,0.00020175596,0.00020739116,0.0008839239,0.0009455328,0.06845385,0.0386494,0.0010162169,0.004969804,0.1169522],"study_design_scores_gemma":[0.000018140852,0.000013839397,0.9052981,0.000034866614,0.000071917064,0.00013384681,0.00082288554,0.084148705,0.0053213737,0.00013790802,0.003948935,0.000049551007],"about_ca_topic_score_codex":0.93306386,"about_ca_topic_score_gemma":0.96560353,"teacher_disagreement_score":0.066936135,"about_ca_system_score_codex":0.003552023,"about_ca_system_score_gemma":0.0058376053,"threshold_uncertainty_score":0.1346606},"labels":[],"label_agreement":null},{"id":"W4392793375","doi":"10.1016/j.rse.2024.114108","title":"Remote sensing of diverse urban environments: From the single city to multiple cities","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Science Foundation","keywords":"Remote sensing; Scope (computer science); Sustainability; Environmental planning; Land cover; Urban heat island; Field (mathematics); Urban planning; Land use; Environmental resource management; Geography; Environmental science; Computer science; Civil engineering; Meteorology; Engineering","score_opus":0.02163821204522545,"score_gpt":0.20845703879454933,"score_spread":0.18681882674932387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392793375","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9658878,0.00437701,0.017155457,0.0010780214,0.00008983542,0.000051070576,0.0012257389,0.00020211004,0.009933069],"genre_scores_gemma":[0.98961484,0.0013554541,0.007704037,0.00011066133,0.00007259971,0.000016962182,0.0005261408,0.000023648681,0.0005756514],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9996599,0.000075206175,0.000011521028,0.0000910559,0.0000978701,0.000064506516],"domain_scores_gemma":[0.9998142,0.000050205865,0.000032823446,0.00003432956,0.00003845935,0.000029961971],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005149792,0.00046827094,0.0004103442,0.0011498262,0.0004396283,0.0011195606,0.0004155724,0.00034540248,0.0007002835],"category_scores_gemma":[0.00049085624,0.00029907323,0.0004006416,0.0020420223,0.00046909766,0.0012833518,0.0014881762,0.00037946648,0.000107142376],"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":[0.0005989174,0.00047228314,0.39459327,0.00048078678,0.0007248408,0.0008591975,0.001605745,0.08290586,0.061069924,0.0040579946,0.008492489,0.44413874],"study_design_scores_gemma":[0.000055360313,0.00012389052,0.88110065,0.00015526346,0.0002893771,0.00060655095,0.0035788468,0.08906753,0.0052330415,0.004689727,0.015000422,0.000099286866],"about_ca_topic_score_codex":0.026467532,"about_ca_topic_score_gemma":0.059219506,"teacher_disagreement_score":0.026467532,"about_ca_system_score_codex":0.00041809105,"about_ca_system_score_gemma":0.0004841665,"threshold_uncertainty_score":0.052626967},"labels":[],"label_agreement":null},{"id":"W4396821727","doi":"10.1016/j.rse.2024.114189","title":"Fractional cover mapping of wildland-urban interface fuels using Landsat, Sentinel 1 and PALSAR imagery","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":false,"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":"","keywords":"Remote sensing; Geology; Satellite imagery; Environmental science; Cover (algebra); Thematic Mapper","score_opus":0.01158692291016584,"score_gpt":0.22270575359640482,"score_spread":0.211118830686239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396821727","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9910748,0.000084045816,0.0032231272,0.000030906995,0.000008898983,0.000018972836,0.0028156927,0.00024383888,0.00249987],"genre_scores_gemma":[0.9917082,0.000058093377,0.0050310283,0.000010075937,0.0000055397386,0.000012275454,0.0020684833,0.000022405751,0.0010838124],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999492,0.0000037369691,0.0000022382258,0.000014597335,0.000013831926,0.000016342847],"domain_scores_gemma":[0.99992955,0.000011620015,0.000012308463,0.000008054201,0.000027002456,0.000011435849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011445543,0.00018100652,0.00014217471,0.001348589,0.00020393326,0.0003919829,0.00015872049,0.00013509733,0.0015086929],"category_scores_gemma":[0.00018064426,0.00010483468,0.00016132921,0.0007576156,0.00006967787,0.00023786422,0.00015267762,0.00008375529,0.00025453008],"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":[0.0008998485,0.0005536747,0.505404,0.00014568861,0.00019645598,0.0005210164,0.00074988167,0.047747888,0.09178626,0.0013343529,0.00781389,0.34284702],"study_design_scores_gemma":[0.000032360902,0.000041613806,0.8627625,0.000016875087,0.000053642725,0.00015719721,0.00041304715,0.12468874,0.007518286,0.00031556096,0.0039781155,0.00002204409],"about_ca_topic_score_codex":0.02440465,"about_ca_topic_score_gemma":0.042036813,"teacher_disagreement_score":0.02440465,"about_ca_system_score_codex":0.00020603591,"about_ca_system_score_gemma":0.00022861369,"threshold_uncertainty_score":0.048525214},"labels":[],"label_agreement":null},{"id":"W4397014467","doi":"10.1016/j.rse.2024.114164","title":"Surface water temperature observations and ice phenology estimations for 1.4 million lakes globally","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; National Oceanic and Atmospheric Administration; Uppsala Universitet; Sveriges Lantbruksuniversitet; Consiglio Nazionale delle Ricerche; European Commission; National Science Foundation; European Space Agency; Nipissing University; American University of Beirut; McGill University; University of Minnesota; National Ocean Service; Natural Environment Research Council; McKnight Foundation; National Park Service; Battelle","keywords":"Remote sensing; Phenology; Environmental science; Surface water; Surface (topology); Climatology; Geology; Meteorology; Geography","score_opus":0.015443463570689967,"score_gpt":0.2096731944430871,"score_spread":0.19422973087239714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4397014467","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31863117,0.00097489596,0.0029998654,0.00022639906,0.000026330701,0.000057532052,0.67287207,0.0007491072,0.0034626524],"genre_scores_gemma":[0.19965647,0.0007244876,0.005641024,0.00008742369,0.000035862256,0.0002521225,0.7916851,0.00007773874,0.001839811],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998011,0.000017450502,0.000025857034,0.00007921939,0.000049852482,0.000026592325],"domain_scores_gemma":[0.99968445,0.0000348266,0.00010052004,0.00004827463,0.00010481498,0.00002715992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020921876,0.00054173317,0.00027241968,0.0010905172,0.00018301846,0.00029177454,0.00028479178,0.00021880817,0.0032051268],"category_scores_gemma":[0.0006644188,0.00027338002,0.00055639265,0.00255866,0.0001328533,0.00046664182,0.0005583719,0.00025189045,0.0017188748],"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":[0.00032306474,0.00015821909,0.6467664,0.0014172561,0.00067050394,0.00026901657,0.0009289293,0.021200288,0.012047778,0.0010753153,0.19676916,0.11837412],"study_design_scores_gemma":[0.0000651541,0.000038899205,0.89315546,0.00012930689,0.0001037235,0.00011359988,0.00034588992,0.010773562,0.0025438252,0.00043120963,0.092258945,0.000040291998],"about_ca_topic_score_codex":0.03237114,"about_ca_topic_score_gemma":0.0452113,"teacher_disagreement_score":0.03237114,"about_ca_system_score_codex":0.00044876218,"about_ca_system_score_gemma":0.0006323707,"threshold_uncertainty_score":0.06436545},"labels":[],"label_agreement":null},{"id":"W4398231446","doi":"10.1016/j.rse.2024.114210","title":"Characterizing satellite-derived freeze/thaw regimes through spatial and temporal clustering for the identification of growing season constraints on vegetation productivity","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":7,"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":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Remote sensing; Vegetation (pathology); Satellite; Productivity; Environmental science; Growing season; Identification (biology); Cluster analysis; Satellite imagery; Computer science; Agronomy; Geography; Ecology; Machine learning","score_opus":0.016896135456189728,"score_gpt":0.2376176454349835,"score_spread":0.22072150997879378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398231446","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99032533,0.00017165154,0.002486508,0.00004282895,0.000006563724,0.000030114623,0.0056198756,0.0001542362,0.0011628473],"genre_scores_gemma":[0.9866654,0.00008786988,0.0040115635,0.000013241871,0.000007264228,0.000019885512,0.008770266,0.00002621743,0.0003980848],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99977773,0.000012936843,0.000012622622,0.000080227044,0.00004541755,0.00007101122],"domain_scores_gemma":[0.99953175,0.00007536668,0.00008395462,0.000046455632,0.00019148873,0.00007100838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003285949,0.0003460437,0.00027760345,0.002284011,0.0006579885,0.0007582295,0.00046782722,0.00022854982,0.00045664614],"category_scores_gemma":[0.0009872072,0.00013940818,0.0004516532,0.0021777402,0.00027618266,0.00022637758,0.0004416447,0.00025134106,0.00014637782],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021516177,0.00012418276,0.8967483,0.00008758622,0.0002470008,0.00019663514,0.0006315685,0.03225113,0.012291772,0.0007663632,0.0037408948,0.052699387],"study_design_scores_gemma":[0.000011327855,0.000019660292,0.8782928,0.000020409028,0.000067457564,0.000047866703,0.0007442977,0.116726644,0.001312309,0.00022724348,0.002499514,0.000030443634],"about_ca_topic_score_codex":0.70960087,"about_ca_topic_score_gemma":0.8134438,"teacher_disagreement_score":0.70960087,"about_ca_system_score_codex":0.001909287,"about_ca_system_score_gemma":0.0026770425,"threshold_uncertainty_score":0.5842187},"labels":[],"label_agreement":null},{"id":"W4399562243","doi":"10.1016/j.rse.2024.114248","title":"Corrigendum to “Characterizing satellite-derived freeze/thaw regimes through spatial and temporal clustering for the identification of growing season constraints on vegetation productivity” [Remote Sensing of Environment Volume 309 (2024) 114210]","year":2024,"lang":"en","type":"erratum","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada; University of British Columbia","funders":"","keywords":"Remote sensing; Vegetation (pathology); Satellite; Environmental science; Productivity; Identification (biology); Volume (thermodynamics); Cluster analysis; Satellite imagery; Computer science; Geology; Ecology; Artificial intelligence; Engineering","score_opus":0.028075358176873854,"score_gpt":0.22437503244309626,"score_spread":0.1962996742662224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399562243","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013040862,0.001204195,0.0010936875,0.041264243,0.9384423,0.00009116439,0.004355795,0.00072054856,0.012697733],"genre_scores_gemma":[0.0038988385,0.004853689,0.0043350803,0.08436419,0.16163531,0.00045202003,0.008741691,0.0018526582,0.72986656],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9967698,0.00046681555,0.00047562562,0.0005552185,0.0014595423,0.00027301032],"domain_scores_gemma":[0.9814232,0.0025073397,0.00060306705,0.0010885354,0.013723014,0.0006547942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026905625,0.002988038,0.002701589,0.00476414,0.0047361804,0.0041795815,0.0038289442,0.0065550795,0.11230513],"category_scores_gemma":[0.02371167,0.0015908177,0.002390324,0.0036621965,0.0016790711,0.0024252268,0.0023432116,0.0075458335,0.09092871],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000986201,0.000005287717,0.000024341392,0.000026570888,0.0000033549484,0.000026493308,0.0000044752887,0.000035065343,0.000031045412,0.00015593793,0.9979572,0.0017202492],"study_design_scores_gemma":[0.000038018185,0.00002154112,0.0023633318,0.0001540446,0.000030063333,0.00008327084,0.00005508072,0.00043196802,0.00025991123,0.0011852093,0.9953251,0.00005259193],"about_ca_topic_score_codex":0.15053105,"about_ca_topic_score_gemma":0.21945755,"teacher_disagreement_score":0.15053105,"about_ca_system_score_codex":0.005091225,"about_ca_system_score_gemma":0.0053963503,"threshold_uncertainty_score":0.3756981},"labels":[],"label_agreement":null},{"id":"W4400229876","doi":"10.1016/j.rse.2024.114283","title":"Influence of temperate forest autumn leaf phenology on segmentation of tree species from UAV imagery using deep learning","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":34,"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 de Sherbrooke; Université de Montréal","funders":"Canada First Research Excellence Fund; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Temperate climate; Temperate forest; Phenology; Temperate rainforest; Deciduous; Segmentation; Evergreen; Convolutional neural network; Tree (set theory); Temperate deciduous forest; Deep learning; Remote sensing; Environmental science; Ecology; Biology; Geography; Artificial intelligence; Computer science; Ecosystem; Mathematics","score_opus":0.010870917915729033,"score_gpt":0.21633361966993628,"score_spread":0.20546270175420725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400229876","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9895746,0.00061989384,0.0061863125,0.000101375765,0.00004124723,0.000021814156,0.0010960811,0.00048018617,0.001878372],"genre_scores_gemma":[0.98733854,0.00018622629,0.0068593617,0.00008892339,0.000011629345,0.000011794279,0.004159205,0.00008661284,0.0012575688],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997061,0.000033829143,0.000012065421,0.00011118839,0.000043610406,0.00009317208],"domain_scores_gemma":[0.99953675,0.00014721073,0.00004040823,0.000034205736,0.00018129189,0.000060180708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006380654,0.0007722773,0.00033382702,0.0008112152,0.00039057742,0.000741337,0.00039783743,0.0004344203,0.00059166324],"category_scores_gemma":[0.0011749777,0.00018051153,0.00045742097,0.00046246138,0.00031530854,0.0004525093,0.0003268509,0.00039088342,0.00036828528],"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":[0.0013282365,0.00026233238,0.33972582,0.00049627793,0.0005192745,0.0006784269,0.0009841571,0.16751607,0.19736786,0.00046534598,0.0078840535,0.2827722],"study_design_scores_gemma":[0.00002199376,0.00013111805,0.41325325,0.00009357556,0.00014852607,0.00019616673,0.0004695995,0.55737674,0.024212532,0.0003465046,0.0036981783,0.000051806466],"about_ca_topic_score_codex":0.20820409,"about_ca_topic_score_gemma":0.31010166,"teacher_disagreement_score":0.20820409,"about_ca_system_score_codex":0.0011702324,"about_ca_system_score_gemma":0.0009464533,"threshold_uncertainty_score":0.41398442},"labels":[],"label_agreement":null},{"id":"W4400613682","doi":"10.1016/j.rse.2024.114290","title":"Deep learning for urban land use category classification: A review and experimental assessment","year":2024,"lang":"en","type":"review","venue":"Remote Sensing of Environment","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":166,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Remote sensing; Land use; Computer science; Environmental science; Artificial intelligence; Geography","score_opus":0.05394558127974756,"score_gpt":0.31015421843692675,"score_spread":0.2562086371571792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400613682","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016125971,0.9866992,0.0067913337,0.00072523736,0.0002672872,0.0000341077,0.00014648074,0.000055018405,0.0036688158],"genre_scores_gemma":[0.009522565,0.9844259,0.0042442633,0.00033064964,0.00017685088,0.000033708413,0.0002452046,0.000013586661,0.0010073334],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996692,0.00007049479,0.000045416517,0.00009011521,0.000100687364,0.000024060222],"domain_scores_gemma":[0.9987853,0.000732367,0.000076780685,0.000043946653,0.00032791437,0.000033653123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015104343,0.0010390719,0.0009824754,0.0026389293,0.00026958555,0.001181184,0.0012307088,0.0008162101,0.0024655529],"category_scores_gemma":[0.0029916512,0.00039542228,0.0009719742,0.004185593,0.000467537,0.001839411,0.0006941384,0.0011129879,0.0011294123],"study_design_candidate":"not_applicable","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":[0.000043101696,0.000077997385,0.0010062084,0.011353917,0.00016687067,0.000041971114,0.00006468054,0.0023325735,0.0005714928,0.0048555913,0.012554577,0.9669311],"study_design_scores_gemma":[0.000034462213,0.0004584435,0.0060695335,0.016145077,0.0012153059,0.0007638481,0.00036461142,0.01341447,0.0040253503,0.012622451,0.9447652,0.00012126349],"about_ca_topic_score_codex":0.0044535017,"about_ca_topic_score_gemma":0.0046818955,"teacher_disagreement_score":0.0044535017,"about_ca_system_score_codex":0.00071401644,"about_ca_system_score_gemma":0.0017398197,"threshold_uncertainty_score":0.008855164},"labels":[],"label_agreement":null},{"id":"W4400893487","doi":"10.1016/j.rse.2024.114313","title":"River ice breakup classification using dual- (HH&amp;HV) or compact-polarization RADARSAT Constellation Mission data","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Remote sensing; Constellation; Breakup; Environmental science; Synthetic aperture radar; Geology; Physics; Astronomy","score_opus":0.1265338173823618,"score_gpt":0.2831649032955866,"score_spread":0.15663108591322478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400893487","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98704684,0.00009982379,0.002380496,0.000058214486,0.000036803194,0.00006718419,0.005017955,0.0003217613,0.0049709366],"genre_scores_gemma":[0.98017234,0.00010263713,0.0029334791,0.000030083058,0.000017414259,0.000028145007,0.013462294,0.000041136667,0.003212477],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998708,0.000008901411,0.000012174738,0.00003106791,0.000024151543,0.0000528732],"domain_scores_gemma":[0.99972385,0.0000323808,0.000053666816,0.000036605197,0.00009696652,0.00005650351],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002896672,0.00029698655,0.00022708884,0.0019259995,0.00038190198,0.0007635953,0.00026800716,0.0003139009,0.0023421405],"category_scores_gemma":[0.0004187819,0.00008383743,0.0003546559,0.0006672728,0.00018492363,0.00043613647,0.00036277177,0.00027237448,0.0009776717],"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":[0.0009857182,0.00039853872,0.7514839,0.00014360556,0.00020340123,0.0010501579,0.00051650766,0.016658336,0.05419107,0.0005929111,0.010089542,0.16368622],"study_design_scores_gemma":[0.00006456088,0.00012833501,0.88922226,0.000060225928,0.00014235282,0.00019444765,0.0012138494,0.08352666,0.013504522,0.00025303304,0.011652494,0.000037148908],"about_ca_topic_score_codex":0.019961154,"about_ca_topic_score_gemma":0.027538845,"teacher_disagreement_score":0.019961154,"about_ca_system_score_codex":0.00028638105,"about_ca_system_score_gemma":0.00036262584,"threshold_uncertainty_score":0.0396899},"labels":[],"label_agreement":null},{"id":"W4401409096","doi":"10.1016/j.rse.2024.114338","title":"Improved global estimation of seasonal variations in C3 photosynthetic capacity based on eco-evolutionary optimality hypotheses and remote sensing","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Joint Forces Staff College","keywords":"Remote sensing; Environmental science; Estimation; Photosynthetic capacity; Computer science; Photosynthesis; Geology; Biology; Botany","score_opus":0.010923637026634419,"score_gpt":0.2177421176471556,"score_spread":0.20681848062052116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401409096","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92773706,0.0002258234,0.06980186,0.00005287023,0.000007755047,0.000012186611,0.0003475523,0.000110120425,0.001704755],"genre_scores_gemma":[0.9839909,0.00003488711,0.015494941,0.000011077879,0.000006232092,0.000007737449,0.00028399494,0.000030417214,0.0001397658],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998586,0.00003638743,0.0000063464895,0.00007214978,0.000013765968,0.0000128379515],"domain_scores_gemma":[0.9995739,0.00024382441,0.000051778035,0.000055102082,0.000057401892,0.000017928685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061726035,0.0004897089,0.0004707672,0.0008090378,0.00025367682,0.0004324072,0.0004985841,0.00042600822,0.0006405254],"category_scores_gemma":[0.0012915259,0.00034309732,0.00041409186,0.00081965985,0.00026289822,0.00081917143,0.0004954233,0.00030527147,0.00011101822],"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":[0.0007011426,0.00010739088,0.25060678,0.00018239682,0.0005404896,0.00017559191,0.0002135514,0.521713,0.116544336,0.0025568202,0.0005083336,0.106150135],"study_design_scores_gemma":[0.00002873679,0.000044007702,0.25277647,0.000009150748,0.000073495,0.000058161568,0.000044302215,0.74035496,0.0043571307,0.0019799767,0.00023804614,0.000035604757],"about_ca_topic_score_codex":0.0066533233,"about_ca_topic_score_gemma":0.010543597,"teacher_disagreement_score":0.0066533233,"about_ca_system_score_codex":0.00032005014,"about_ca_system_score_gemma":0.00028002297,"threshold_uncertainty_score":0.013229191},"labels":[],"label_agreement":null},{"id":"W4401837913","doi":"10.1016/j.rse.2024.114376","title":"Increase in gross primary production of boreal forests balanced out by increase in ecosystem respiration","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Fire effects on ecosystems","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":"Université de Montréal; Université du Québec à Montréal; Dalhousie University; Environment and Climate Change Canada","funders":"Horizon 2020 Framework Programme; Eesti Teadusagentuur; Helsingin Yliopisto","keywords":"Primary production; Taiga; Environmental science; Ecosystem respiration; Remote sensing; Ecosystem; Primary (astronomy); Production (economics); Boreal; Respiration; Ecology; Geography; Forestry; Biology; Economics; Botany","score_opus":0.006177140592023387,"score_gpt":0.20906941415793173,"score_spread":0.20289227356590833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401837913","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99656624,0.00037153144,0.0010346043,0.00003944411,0.000010740702,0.000005877999,0.00048315863,0.000047572234,0.0014408978],"genre_scores_gemma":[0.998917,0.00013663396,0.00041652741,0.000019554138,0.000010301714,0.000005023834,0.0003193039,0.0000036364552,0.00017209932],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99993324,0.0000077594295,0.0000056795952,0.00002733803,0.000015997417,0.000010012856],"domain_scores_gemma":[0.99973553,0.000038651964,0.00012631899,0.000022947623,0.000041482064,0.00003501201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025818727,0.00029207594,0.00015108917,0.00025271988,0.00018401023,0.00035762464,0.0001094255,0.00016319186,0.0007188192],"category_scores_gemma":[0.00044753082,0.00012786362,0.00017004061,0.00023343346,0.00017723405,0.0005234712,0.00014623551,0.0001782662,0.00013566663],"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":[0.00020128126,0.000075096345,0.8831442,0.00012883646,0.00013525621,0.00013026406,0.00014851234,0.0031293323,0.081108026,0.00023720326,0.00058982964,0.030972093],"study_design_scores_gemma":[0.0000012262716,0.00001773252,0.99841154,0.0000014244154,0.000007094581,0.00006039303,0.000018147393,0.0006088931,0.0005715038,0.000041429816,0.00025877514,0.0000018310675],"about_ca_topic_score_codex":0.0048985207,"about_ca_topic_score_gemma":0.010065308,"teacher_disagreement_score":0.0048985207,"about_ca_system_score_codex":0.00020415407,"about_ca_system_score_gemma":0.0001648149,"threshold_uncertainty_score":0.009739995},"labels":[],"label_agreement":null},{"id":"W4401869427","doi":"10.1016/j.rse.2024.114371","title":"Bridging spatio-temporal discontinuities in global soil moisture mapping by coupling physics in deep learning","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":73,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Agricultural Research Service; National Natural Science Foundation of China; U.S. Department of Agriculture; Ministry of Natural Resources of the People's Republic of China; Ministry of Natural Resources; Jianghan University","keywords":"Classification of discontinuities; Bridging (networking); Remote sensing; Environmental science; Coupling (piping); Moisture; Computer science; Physics; Meteorology; Geography; Materials science; Mathematics","score_opus":0.009565133093651125,"score_gpt":0.21827959400342445,"score_spread":0.20871446090977333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401869427","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6064316,0.00061795604,0.38965455,0.0006583483,0.00009629465,0.000014032552,0.00016445875,0.00047488543,0.0018878903],"genre_scores_gemma":[0.9874849,0.0000972394,0.011759159,0.00003897152,0.000018652434,0.0000069570747,0.000077029435,0.000025338944,0.0004918749],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989784,0.000022906652,0.000005079066,0.000034247998,0.00001764941,0.000022175902],"domain_scores_gemma":[0.999374,0.00040169773,0.000079652935,0.000058197078,0.000048796155,0.000037619106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004906808,0.0003500566,0.00030747504,0.0004531019,0.00021755915,0.00061624637,0.00072156725,0.00076432625,0.000852721],"category_scores_gemma":[0.0024551337,0.00033283044,0.0003116273,0.00044364814,0.0005679774,0.0015671037,0.0011806508,0.0012558792,0.0001027288],"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":[0.00019726284,0.00014972955,0.007963487,0.00007631561,0.0000794055,0.0001089073,0.00010554863,0.9095045,0.009621427,0.013393381,0.00077139545,0.05802861],"study_design_scores_gemma":[0.0000031775253,0.0000069454713,0.0006146497,0.0000024972458,0.0000034946581,0.000003373911,0.0000069875864,0.9951574,0.00043776084,0.0036756406,0.00008530567,0.0000027319168],"about_ca_topic_score_codex":0.006276677,"about_ca_topic_score_gemma":0.006225197,"teacher_disagreement_score":0.006276677,"about_ca_system_score_codex":0.0004960518,"about_ca_system_score_gemma":0.0005215719,"threshold_uncertainty_score":0.012480259},"labels":[],"label_agreement":null},{"id":"W4402227887","doi":"10.1016/j.rse.2024.114377","title":"New insights into distinguishing temperate deciduous swamps from upland forests and shrublands with SAR","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Landslides and related hazards","field":"Environmental Science","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":"Carleton University; Environment and Climate Change Canada","funders":"","keywords":"Shrubland; Swamp; Remote sensing; Deciduous; Temperate rainforest; Temperate deciduous forest; Temperate forest; Environmental science; Temperate climate; Geography; Agroforestry; Ecology; Ecosystem","score_opus":0.0052669359542648906,"score_gpt":0.19485324227243558,"score_spread":0.1895863063181707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402227887","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9833211,0.0017403021,0.008986258,0.00045582786,0.000021383248,0.000022918375,0.00028605096,0.000056989702,0.0051092426],"genre_scores_gemma":[0.98729277,0.0016939924,0.00903672,0.00020374663,0.000055010933,0.000009870482,0.00034889914,0.000014382423,0.0013446251],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998869,0.000023796432,0.000010772566,0.000036427904,0.000019211853,0.000022991253],"domain_scores_gemma":[0.99959546,0.0001635631,0.00011603649,0.000029474044,0.00006298851,0.00003245693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060015044,0.00038452368,0.00019555775,0.0007659745,0.00013756476,0.00066814275,0.00017751343,0.00021414609,0.00096347474],"category_scores_gemma":[0.000723072,0.00017969239,0.00024367434,0.0006904747,0.00035090113,0.00086449087,0.00023480627,0.00027030875,0.00014758972],"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":[0.00020803577,0.00016515817,0.6857657,0.00027944794,0.00013697748,0.0011763537,0.0015708749,0.0029834723,0.15618812,0.0025566285,0.0008891201,0.1480801],"study_design_scores_gemma":[0.0000044724297,0.00007523081,0.98187363,0.00004611376,0.000044985736,0.00055371114,0.0012642734,0.0082570845,0.0042689336,0.0014751817,0.002121316,0.000015093115],"about_ca_topic_score_codex":0.0021326898,"about_ca_topic_score_gemma":0.0071010366,"teacher_disagreement_score":0.0021326898,"about_ca_system_score_codex":0.00014114664,"about_ca_system_score_gemma":0.00019247418,"threshold_uncertainty_score":0.0042405725},"labels":[],"label_agreement":null},{"id":"W4402448810","doi":"10.1016/j.rse.2024.114412","title":"Land surface temperature retrieval from SDGSAT-1 thermal infrared spectrometer images: Algorithm and validation","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Key Research and Development Program of China Stem Cell and Translational Research; National Outstanding Youth Science Fund Project of National Natural Science Foundation of China","keywords":"Remote sensing; Thermal infrared; Infrared; Spectrometer; Imaging spectrometer; Environmental science; Thermal; Computer science; Algorithm; Meteorology; Optics; Geology; Physics","score_opus":0.006447802898701269,"score_gpt":0.19696925783828353,"score_spread":0.19052145493958228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402448810","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74881196,0.00039398036,0.24021572,0.00016337703,0.00009639862,0.00030848733,0.0021005522,0.0052951304,0.0026143517],"genre_scores_gemma":[0.71947443,0.00016717402,0.27259547,0.00006316049,0.000014208069,0.00019350699,0.0051515694,0.00022398941,0.0021164552],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997805,0.000028070928,0.000016039423,0.00007221821,0.00007718111,0.000025969164],"domain_scores_gemma":[0.99973625,0.000045597586,0.000019782177,0.00003934148,0.00015105716,0.0000080536665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081692176,0.00062971807,0.00043960378,0.0006050798,0.00043414978,0.00050733885,0.00079622254,0.0007238883,0.0008777793],"category_scores_gemma":[0.0009968451,0.00032842517,0.0004946098,0.00056389434,0.00025922744,0.0006591562,0.00043576412,0.0004604024,0.00081315235],"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":[0.00061454705,0.0006553752,0.034328334,0.00023491798,0.00029133397,0.00009674255,0.000120489014,0.38903946,0.1307684,0.0011685477,0.0050946837,0.4375872],"study_design_scores_gemma":[0.00007801988,0.000044341294,0.014403387,0.000008031289,0.000037116224,0.00002560027,0.00002094147,0.9593573,0.02491053,0.00020133733,0.0008961849,0.000017208167],"about_ca_topic_score_codex":0.017183648,"about_ca_topic_score_gemma":0.018061578,"teacher_disagreement_score":0.017183648,"about_ca_system_score_codex":0.0005876068,"about_ca_system_score_gemma":0.0010166338,"threshold_uncertainty_score":0.03416729},"labels":[],"label_agreement":null},{"id":"W4402461578","doi":"10.1016/j.rse.2024.114402","title":"DRMAT: A multivariate algorithm for detecting breakpoints in multispectral time series","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"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":"Ohio Agricultural Research and Development Center, Ohio State University; National Institute of Food and Agriculture; Lowe Syndrome Trust; Ohio Department of Higher Education; U.S. Geological Survey; U.S. Department of Agriculture; National Aeronautics and Space Administration","keywords":"Multispectral image; Remote sensing; Series (stratigraphy); Multivariate statistics; Algorithm; Computer science; Multispectral pattern recognition; Time series; Data mining; Geology; Machine learning","score_opus":0.009213438682920523,"score_gpt":0.21802927998188393,"score_spread":0.2088158412989634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402461578","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008171114,0.00013940866,0.9860124,0.00006433223,0.000052378196,0.000042913227,0.00036018944,0.0048260996,0.00033115598],"genre_scores_gemma":[0.07580027,0.00015318076,0.9188705,0.00009597132,0.0000927439,0.00017533811,0.0014655994,0.00058623275,0.0027601372],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994311,0.00009249767,0.000040939653,0.00016649565,0.00020959131,0.000059385675],"domain_scores_gemma":[0.9993943,0.00017798056,0.0001105624,0.00011858007,0.00015829541,0.00004023817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010008157,0.0011430782,0.0008818196,0.0024056288,0.00057986676,0.00093658664,0.0014910664,0.0010811914,0.0057497313],"category_scores_gemma":[0.0024400486,0.000443855,0.0011417413,0.0015124766,0.00038293787,0.001364106,0.0013334468,0.0015138584,0.0027734493],"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":[0.00037562303,0.00014896676,0.0021035976,0.000096627904,0.0001331027,0.00006339951,0.000051854935,0.030052237,0.025354428,0.0027827576,0.007659575,0.93117785],"study_design_scores_gemma":[0.00006678573,0.0001682797,0.0054690107,0.000018135876,0.00006537336,0.00020418063,0.000043348824,0.95786214,0.01935189,0.0041376697,0.012558672,0.000054510958],"about_ca_topic_score_codex":0.0031090956,"about_ca_topic_score_gemma":0.0049850666,"teacher_disagreement_score":0.0057497313,"about_ca_system_score_codex":0.0003761741,"about_ca_system_score_gemma":0.00070385705,"threshold_uncertainty_score":0.019234776},"labels":[],"label_agreement":null},{"id":"W4402553752","doi":"10.1016/j.rse.2024.114424","title":"A new dataset of leaf optical traits to include biophysical parameters in addition to spectral and biochemical assessment","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","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":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Remote sensing; Environmental science; Computer science; Geology","score_opus":0.009446720836308008,"score_gpt":0.2475030691798993,"score_spread":0.2380563483435913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402553752","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07466564,0.00081482943,0.021578183,0.00031910365,0.00019439292,0.0002753776,0.89153427,0.0050866436,0.0055315085],"genre_scores_gemma":[0.051438585,0.00022506423,0.02749583,0.00026951876,0.000042599917,0.0005592108,0.91802937,0.00024621986,0.0016935627],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99905354,0.00006762332,0.00009580133,0.00043573603,0.00025483465,0.0000925063],"domain_scores_gemma":[0.99790764,0.000321369,0.00023448422,0.00063421694,0.00076342135,0.00013899834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079818437,0.001352414,0.0011900096,0.0028061448,0.0006903809,0.0016442616,0.0014097113,0.0015655265,0.0040891413],"category_scores_gemma":[0.0021767356,0.00042012925,0.0017303135,0.0035522238,0.00033921382,0.0015071356,0.0016051763,0.0018555147,0.005533632],"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":[0.0009934454,0.002116605,0.22017168,0.0045011584,0.0010147857,0.00090756133,0.0006947143,0.025229318,0.085288756,0.003989011,0.41264012,0.24245283],"study_design_scores_gemma":[0.00023385978,0.00030508803,0.38000882,0.00047113153,0.00026769386,0.0012620392,0.0006579804,0.043463774,0.01937401,0.004194125,0.54946846,0.00029301806],"about_ca_topic_score_codex":0.008441818,"about_ca_topic_score_gemma":0.01883109,"teacher_disagreement_score":0.008441818,"about_ca_system_score_codex":0.00091701065,"about_ca_system_score_gemma":0.00092702854,"threshold_uncertainty_score":0.016785324},"labels":[],"label_agreement":null},{"id":"W4402991917","doi":"10.1016/j.rse.2024.114435","title":"Deployment-invariant probability of detection characterization for aerial LiDAR methane detection","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Atmospheric and Environmental Gas Dynamics","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":"Carleton University","funders":"","keywords":"Remote sensing; Lidar; Software deployment; Computer science; Environmental science; Aerial survey; Methane; Artificial intelligence; Geology","score_opus":0.010247468675773157,"score_gpt":0.2063985495043602,"score_spread":0.19615108082858704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402991917","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08782774,0.00013091174,0.90892833,0.00014578391,0.000023688026,0.000068903515,0.0002553086,0.0004283238,0.0021909953],"genre_scores_gemma":[0.9629844,0.00016846685,0.034307167,0.000078081874,0.000021693599,0.000098555276,0.000530547,0.00010159797,0.0017095092],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9982986,0.00038799815,0.00006709753,0.00042982798,0.0005919403,0.00022454155],"domain_scores_gemma":[0.9923029,0.004314298,0.0015023609,0.000844717,0.0008813023,0.00015450382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025274158,0.00086422765,0.00052638754,0.0011360061,0.00030729218,0.0007447231,0.0014329173,0.00071328983,0.00091179245],"category_scores_gemma":[0.012039955,0.00046075697,0.0007966643,0.0008232716,0.0009901815,0.0018024062,0.0010499016,0.0012096207,0.00022095453],"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":[0.00008533175,0.00007158458,0.012241192,0.00006794325,0.000050183182,0.00013291507,0.00008990423,0.9486965,0.0060805744,0.012760956,0.00074967224,0.018973175],"study_design_scores_gemma":[0.0000024335804,0.000018901814,0.0016863447,0.0000027054164,0.000004733997,0.00004280215,0.000010748248,0.99574006,0.0008886498,0.0013935994,0.00020020474,0.00000879109],"about_ca_topic_score_codex":0.0070847254,"about_ca_topic_score_gemma":0.004391411,"teacher_disagreement_score":0.0070847254,"about_ca_system_score_codex":0.001716385,"about_ca_system_score_gemma":0.00082176004,"threshold_uncertainty_score":0.014086962},"labels":[],"label_agreement":null},{"id":"W4403033077","doi":"10.1016/j.rse.2024.114433","title":"Sensor-generic adjacency-effect correction for remote sensing of coastal and inland waters","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada; Agriculture and Agri-Food Canada; University of Ottawa","funders":"Agriculture and Agri-Food Canada; Canadian Space Agency","keywords":"Remote sensing; Adjacency list; Computer science; Environmental science; Geology; Algorithm","score_opus":0.009690979084411635,"score_gpt":0.19073093047587625,"score_spread":0.18103995139146462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403033077","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050417338,0.000169667,0.8971915,0.00016396739,0.00021292256,0.00021887611,0.006404012,0.040348373,0.0048732394],"genre_scores_gemma":[0.17852195,0.00012637782,0.8020695,0.00022519659,0.00003719433,0.0003964627,0.011090191,0.0030792546,0.0044538337],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994698,0.00006799729,0.00002524232,0.00013702596,0.00025091678,0.000049013328],"domain_scores_gemma":[0.99949837,0.0000742356,0.00006065935,0.0001567308,0.00018858472,0.000021371883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007487172,0.00076217117,0.000351388,0.0007224225,0.00038601574,0.00055155996,0.0013512559,0.0005432988,0.0060044434],"category_scores_gemma":[0.0023201671,0.00036977642,0.00074483873,0.0011456432,0.00024458516,0.0011129874,0.0011185148,0.0010057972,0.0027377869],"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":[0.00037651535,0.00044378612,0.019685812,0.00083218864,0.00049411174,0.00027454708,0.0006588122,0.119168244,0.2059602,0.013309668,0.083135895,0.5556601],"study_design_scores_gemma":[0.000099863006,0.00012752079,0.030615788,0.0000632608,0.00009177334,0.00036070417,0.00020221228,0.7682818,0.11094517,0.011030618,0.07800437,0.00017686858],"about_ca_topic_score_codex":0.007399119,"about_ca_topic_score_gemma":0.019234343,"teacher_disagreement_score":0.007399119,"about_ca_system_score_codex":0.0004347105,"about_ca_system_score_gemma":0.0010283197,"threshold_uncertainty_score":0.020086884},"labels":[],"label_agreement":null},{"id":"W4403148167","doi":"10.1016/j.rse.2024.114456","title":"Individual tree species classification using low-density airborne multispectral LiDAR data via attribute-aware cross-branch transformer","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Calgary; University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Remote sensing; Lidar; Multispectral image; Environmental science; Multispectral pattern recognition; Computer science; Geography","score_opus":0.052470678480278005,"score_gpt":0.2896602424469025,"score_spread":0.23718956396662452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403148167","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27499366,0.00021984697,0.7183797,0.00008123168,0.00007089472,0.00007502578,0.00049594213,0.0022082229,0.003475424],"genre_scores_gemma":[0.90367925,0.00014575165,0.09262936,0.000044207092,0.000025542193,0.000040469837,0.0011969914,0.00008736434,0.0021511165],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99978787,0.00001925902,0.00001425145,0.000058824502,0.00008319098,0.000036520436],"domain_scores_gemma":[0.9997125,0.00004928865,0.000024811632,0.000043676446,0.00014062511,0.000029102277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036870298,0.00036046995,0.0005504799,0.0013027863,0.0002792474,0.00068211206,0.00056158553,0.0002676139,0.0016912597],"category_scores_gemma":[0.00063747365,0.00015206159,0.00049007824,0.0010445287,0.00017841703,0.0011269496,0.0006734581,0.00036824917,0.0009300094],"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":[0.0006673735,0.00036480976,0.013099652,0.0001005122,0.00009160856,0.000188887,0.00015944258,0.042414572,0.119990245,0.0036233496,0.0030842177,0.8162154],"study_design_scores_gemma":[0.00002115076,0.000103293285,0.007549204,0.000008085595,0.000049383307,0.00018305637,0.00012211267,0.9619918,0.024257395,0.003798714,0.0018963896,0.000019482708],"about_ca_topic_score_codex":0.0018940638,"about_ca_topic_score_gemma":0.0026072059,"teacher_disagreement_score":0.0018940638,"about_ca_system_score_codex":0.00026454817,"about_ca_system_score_gemma":0.00051261665,"threshold_uncertainty_score":0.005657792},"labels":[],"label_agreement":null},{"id":"W4403322972","doi":"10.1016/j.rse.2024.114380","title":"Monitoring road development in Congo Basin forests with multi-sensor satellite imagery and deep learning","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":23,"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":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Remote sensing; Satellite imagery; Satellite; Deep learning; Environmental science; Geology; Computer science; Artificial intelligence","score_opus":0.009373440258822556,"score_gpt":0.2096020283063776,"score_spread":0.20022858804755506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403322972","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96743786,0.0013711295,0.020617662,0.00085205917,0.00009391046,0.00004071618,0.005564372,0.001089466,0.0029329015],"genre_scores_gemma":[0.9835714,0.00028415394,0.01146964,0.000059568152,0.00003187988,0.000019312038,0.0038933323,0.000020129506,0.0006504981],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997638,0.00005069112,0.000012470097,0.00007224579,0.00003729977,0.00006343748],"domain_scores_gemma":[0.9997837,0.00004096011,0.000050482606,0.000030547846,0.00006417577,0.000030103343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003082058,0.0007795815,0.00022753682,0.0016144457,0.00026086802,0.0005303809,0.00049807277,0.00047361138,0.0004780787],"category_scores_gemma":[0.0005704092,0.0002104197,0.0004397258,0.00091097003,0.00024103439,0.000763068,0.0005085117,0.00047753783,0.00019919794],"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":[0.00031885298,0.00065917236,0.3042464,0.00031104835,0.00051486306,0.0008315846,0.0003257543,0.44205776,0.02088545,0.0010249029,0.009549263,0.21927501],"study_design_scores_gemma":[0.000013238634,0.00004512971,0.10379633,0.000049819064,0.00007429209,0.000089323345,0.00028146553,0.88819724,0.004898041,0.00051723584,0.0020059026,0.00003204821],"about_ca_topic_score_codex":0.05007092,"about_ca_topic_score_gemma":0.094177306,"teacher_disagreement_score":0.05007092,"about_ca_system_score_codex":0.00065989036,"about_ca_system_score_gemma":0.00041776913,"threshold_uncertainty_score":0.09955895},"labels":[],"label_agreement":null},{"id":"W4403628939","doi":"10.1016/j.rse.2024.114480","title":"4D imaging of the volcano feeding system beneath the urban area of the Campi Flegrei caldera","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":20,"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","funders":"Agenzia Spaziale Italiana; NOAA Center for Earth System Sciences and Remote Sensing Technologies; Agencia Estatal de Investigación; National Science Foundation","keywords":"Caldera; Volcano; Geology; Remote sensing; Seismology","score_opus":0.005274963515016382,"score_gpt":0.1732527068509768,"score_spread":0.1679777433359604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403628939","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95424753,0.00043446373,0.028272923,0.00044318428,0.000047296548,0.00008989785,0.0043025063,0.00075131987,0.011410851],"genre_scores_gemma":[0.9375135,0.0003079824,0.057320137,0.000099817946,0.00005242988,0.00003733974,0.0026867446,0.00007106824,0.001910949],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99991286,0.0000067212986,0.0000023049952,0.000023793398,0.00003167022,0.00002261263],"domain_scores_gemma":[0.9999436,0.000009106685,0.000011948807,0.000009465986,0.000015087849,0.0000107422875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001055538,0.000335067,0.00017919105,0.0016300573,0.0001870547,0.00044923427,0.00030288906,0.0004699252,0.0012289762],"category_scores_gemma":[0.00018482508,0.0002482625,0.0002457347,0.00079471833,0.00016913767,0.00021882089,0.0003443028,0.00027082418,0.00026967938],"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":[0.0002951454,0.00053469185,0.17211531,0.00038611854,0.00024279815,0.0022116578,0.0015274845,0.12468636,0.43743998,0.00206559,0.007296174,0.2511987],"study_design_scores_gemma":[0.000057018056,0.00014458755,0.6391558,0.00005861324,0.00011402997,0.0009882896,0.00071421865,0.32788092,0.017978394,0.0007904089,0.01204681,0.00007091311],"about_ca_topic_score_codex":0.010237918,"about_ca_topic_score_gemma":0.03543887,"teacher_disagreement_score":0.010237918,"about_ca_system_score_codex":0.00032359746,"about_ca_system_score_gemma":0.00037789706,"threshold_uncertainty_score":0.020356655},"labels":[],"label_agreement":null},{"id":"W4403932412","doi":"10.1016/j.rse.2024.114478","title":"Grounding-line retreat of Milne Glacier, Ellesmere Island, Canada over 1966–2023 from satellite, airborne, and ground radar data","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":4,"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; Environment and Climate Change Canada; Natural Resources Canada; Carleton University","funders":"","keywords":"Remote sensing; Glacier; Satellite; Geology; Radar; Meteorology; Geomorphology; Geography; Telecommunications; Engineering","score_opus":0.02388528363045787,"score_gpt":0.21166386954626742,"score_spread":0.18777858591580956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403932412","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9793976,0.0005560959,0.00015267648,0.00010393793,0.000010884362,0.000024049234,0.017135637,0.00002690601,0.0025921448],"genre_scores_gemma":[0.9779122,0.0005569018,0.00038807854,0.00004829708,0.000009337296,0.000016299497,0.019311788,0.000007033409,0.0017500021],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999811,0.000005309385,0.000011134751,0.00004525972,0.00008131676,0.00004610853],"domain_scores_gemma":[0.9991353,0.00002331863,0.00016093966,0.00002472684,0.00052070036,0.00013505128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024416437,0.00038114985,0.00019087522,0.0016629971,0.00077223865,0.0007987748,0.00043737277,0.00021610061,0.000781254],"category_scores_gemma":[0.0006165769,0.00010521364,0.00028191577,0.002509597,0.00029203083,0.0003205665,0.00038571394,0.0003021818,0.00018226649],"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":[0.00009188786,0.000028652968,0.9845775,0.00004436559,0.000090481815,0.00017434274,0.00039982915,0.0017834336,0.0007618443,0.00010243013,0.0024284087,0.00951674],"study_design_scores_gemma":[0.0000021681287,0.0000043660116,0.99660397,0.0000127480625,0.000010798298,0.000027832675,0.00023392099,0.00084273575,0.00014250336,0.000007259458,0.0021068556,0.0000048078373],"about_ca_topic_score_codex":0.9741901,"about_ca_topic_score_gemma":0.9907079,"teacher_disagreement_score":0.025809884,"about_ca_system_score_codex":0.008084345,"about_ca_system_score_gemma":0.0058507174,"threshold_uncertainty_score":0.058656275},"labels":[],"label_agreement":null},{"id":"W4404281046","doi":"10.1016/j.rse.2024.114494","title":"Earth's record-high greenness and its attributions in 2020","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Global Energy and Sustainability Research","field":"Energy","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"Oak Ridge National Laboratory; Southern Research Station","keywords":"Remote sensing; Earth (classical element); Earth observation; Attribution; Environmental science; Astrobiology; Geology; Satellite; Astronomy; Psychology","score_opus":0.013364009172670263,"score_gpt":0.2432067429818175,"score_spread":0.22984273380914721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404281046","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72089666,0.009879862,0.0068181157,0.038874924,0.004822496,0.000047338723,0.10645073,0.000901655,0.11130821],"genre_scores_gemma":[0.9682671,0.002251492,0.0016768129,0.0010784788,0.00049173844,0.000025594225,0.018642282,0.00017180931,0.007394728],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996512,0.000034781926,0.000019537947,0.00006004244,0.00013928236,0.00009512283],"domain_scores_gemma":[0.9985978,0.00013786601,0.0002527993,0.00013006033,0.00066874444,0.00021264362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008493844,0.0002846859,0.00015555385,0.0013485876,0.00055807823,0.0015839541,0.00038591312,0.00068787707,0.003984951],"category_scores_gemma":[0.003272723,0.00012289504,0.00033471006,0.003470209,0.0004991595,0.0019596927,0.0014851049,0.0012443839,0.0009835407],"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":[0.0011588412,0.00018919303,0.3207682,0.001125561,0.00042365573,0.0004858573,0.0022031814,0.011348072,0.0065929866,0.12183332,0.26734808,0.26652306],"study_design_scores_gemma":[0.000019503414,0.00008665138,0.5922432,0.0002555993,0.00006961034,0.00015822244,0.0023893975,0.004748815,0.0023498712,0.020466825,0.377132,0.00008025132],"about_ca_topic_score_codex":0.040085383,"about_ca_topic_score_gemma":0.040670466,"teacher_disagreement_score":0.040085383,"about_ca_system_score_codex":0.0017453433,"about_ca_system_score_gemma":0.0010582871,"threshold_uncertainty_score":0.079704106},"labels":[],"label_agreement":null},{"id":"W4404388705","doi":"10.1016/j.rse.2024.114499","title":"Evaluating the wilderness status of long-distance trails in the United States - Exploring the potential of SDGSAT-1 glimmer imager data","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Diverse Aspects of Tourism Research","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"U.S. Geological Survey; Nanjing University; National Natural Science Foundation of China; U.S. Department of Transportation; Ministry of Education; Ministry of Education of the People's Republic of China; Commission for Environmental Cooperation; National Aeronautics and Space Administration; Foundation for Appalachian Ohio","keywords":"Wilderness; Remote sensing; Geography; Environmental science; Ecology","score_opus":0.1660070414758068,"score_gpt":0.3943684450275603,"score_spread":0.2283614035517535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404388705","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9981325,0.000047649686,0.00015703199,0.00005506725,0.0000033026486,0.000011912217,0.0006671324,0.000009566935,0.0009156648],"genre_scores_gemma":[0.9971009,0.00007985931,0.0010761254,0.000023664861,0.0000030103313,0.000006977244,0.0013350707,0.0000037769466,0.00037079054],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996182,0.00010829103,0.000032829124,0.00005747367,0.00011243584,0.00007076114],"domain_scores_gemma":[0.99889946,0.00022376257,0.0002031235,0.00009060833,0.00038905782,0.00019392793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010846546,0.00019309972,0.00019932484,0.0013188752,0.00044384124,0.0011877335,0.0003594245,0.00034775003,0.00062281196],"category_scores_gemma":[0.0018367916,0.00012027115,0.00021703992,0.0015619731,0.00031531815,0.0009473225,0.0005926264,0.00020473733,0.00014322832],"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":[0.00008880963,0.00014408628,0.9800827,0.00002105639,0.00006446937,0.000080882455,0.0002474982,0.0024927738,0.0010319086,0.00012313732,0.000499233,0.015123519],"study_design_scores_gemma":[0.000009963897,0.00010972404,0.98359203,0.000022654283,0.000042532072,0.00003563339,0.002622869,0.011988528,0.00043757487,0.00012032477,0.0010080123,0.000010165807],"about_ca_topic_score_codex":0.14951783,"about_ca_topic_score_gemma":0.47037703,"teacher_disagreement_score":0.14951783,"about_ca_system_score_codex":0.000866885,"about_ca_system_score_gemma":0.00090030645,"threshold_uncertainty_score":0.2972951},"labels":[],"label_agreement":null},{"id":"W4404580476","doi":"10.1016/j.rse.2024.114508","title":"Genetic Algorithm for Atmospheric Correction (GAAC) of water bodies impacted by adjacency effects","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Adjacency list; Remote sensing; Atmospheric correction; Algorithm; Computer science; Genetic algorithm; Environmental science; Reflectivity; Geology; Physics; Optics; Machine learning","score_opus":0.004039850082665216,"score_gpt":0.17260362001744792,"score_spread":0.1685637699347827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404580476","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.084410354,0.00032725878,0.91067797,0.00022187551,0.00015975,0.00008614661,0.000124356,0.0010803068,0.0029120755],"genre_scores_gemma":[0.54123336,0.00013619455,0.45452935,0.00015478734,0.000073179006,0.00012717157,0.00028067204,0.00015197977,0.0033133172],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975675,0.000059557733,0.000010599441,0.000063116175,0.000057121793,0.000052865358],"domain_scores_gemma":[0.99930096,0.0003695101,0.000053454547,0.000040751565,0.00021084781,0.000024475235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062288437,0.00057408126,0.0007116304,0.0009292566,0.0007011333,0.00064800656,0.0012132602,0.0013608367,0.0021485607],"category_scores_gemma":[0.0025775444,0.00029835216,0.0008593586,0.00095599965,0.00044109437,0.00038636493,0.00068242225,0.00081745675,0.00023196844],"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":[0.000074889715,0.000030460647,0.0014980208,0.000027046259,0.000051476392,0.00006832292,0.000045027107,0.8983623,0.0013069696,0.001986103,0.0011144545,0.09543491],"study_design_scores_gemma":[0.000010942858,0.000012123631,0.0002544173,0.0000034723987,0.000010230173,0.000015067971,0.0000071963086,0.9986205,0.0003436798,0.0005058289,0.00021362839,0.0000029908492],"about_ca_topic_score_codex":0.04718378,"about_ca_topic_score_gemma":0.034902558,"teacher_disagreement_score":0.04718378,"about_ca_system_score_codex":0.000635266,"about_ca_system_score_gemma":0.002374242,"threshold_uncertainty_score":0.09381831},"labels":[],"label_agreement":null},{"id":"W4404641473","doi":"10.1016/j.rse.2024.114512","title":"Seasonal vegetation dynamics for phenotyping using multispectral drone imagery: Genetic differentiation, climate adaptation, and hybridization in a common-garden trial of interior spruce (Picea engelmannii × glauca)","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Natural Resources and Forestry; Government of British Columbia; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multispectral image; Remote sensing; Vegetation (pathology); Adaptation (eye); Picea engelmannii; Drone; Environmental science; Geography; Ecology; Physical geography; Biology; Botany","score_opus":0.01142062090619115,"score_gpt":0.2300508838244813,"score_spread":0.21863026291829016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404641473","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9997631,0.0000060022676,0.000098153425,0.0000037562882,0.0000017178853,0.000010420311,0.00006518132,0.0000034808434,0.000048269452],"genre_scores_gemma":[0.99820614,0.000014782489,0.0009074465,0.000022020467,0.0000025759061,0.000052459978,0.0003414753,0.000016435446,0.00043665172],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9994697,0.00017759723,0.000028178456,0.00017810114,0.00006509915,0.000081314734],"domain_scores_gemma":[0.9989127,0.0003997144,0.00012540072,0.00010271478,0.000088523135,0.00037100582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009419063,0.00044951183,0.00046448546,0.0006600844,0.0007562029,0.0004215964,0.00064936857,0.0005087626,0.00058095873],"category_scores_gemma":[0.0007542137,0.00018530205,0.00050690334,0.00046559813,0.00052881346,0.00029981523,0.00049999903,0.00075016456,0.00012261985],"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":[0.011248167,0.013101256,0.14418302,0.00006555234,0.00037912364,0.0007421904,0.0029031143,0.002173479,0.8133884,0.00024646413,0.0002968375,0.01127241],"study_design_scores_gemma":[0.00028109382,0.00713003,0.95377886,0.000009314068,0.00018463527,0.00037134407,0.0017134838,0.006527325,0.029231707,0.00008679294,0.00063253957,0.000052922962],"about_ca_topic_score_codex":0.017100852,"about_ca_topic_score_gemma":0.046364173,"teacher_disagreement_score":0.017100852,"about_ca_system_score_codex":0.00087313727,"about_ca_system_score_gemma":0.00043356494,"threshold_uncertainty_score":0.034002602},"labels":[],"label_agreement":null},{"id":"W4405125875","doi":"10.1016/j.rse.2024.114545","title":"LiDAR-derived Lorenz-entropy metric for vertical structural complexity: A comparative study of tropical dry and moist forests","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Forest ecology and management","field":"Environmental Science","cited_by":7,"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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Sveriges Lantbruksuniversitet; University of Alberta","keywords":"Lidar; Remote sensing; Metric (unit); Tropical forest; Entropy (arrow of time); Environmental science; Geography; Mathematics; Physics; Ecology; Biology","score_opus":0.029621047700115824,"score_gpt":0.27470356728573725,"score_spread":0.24508251958562144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405125875","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9964809,0.00020061793,0.0022198746,0.00001943902,0.0000031560785,0.000009804195,0.00013275459,0.000008533375,0.0009249081],"genre_scores_gemma":[0.9989906,0.000066887376,0.0007419383,0.000003910594,0.000007136606,0.000003849727,0.00012726108,0.0000028691172,0.00005552419],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99979,0.000042157702,0.000017905664,0.000043934844,0.00006529712,0.000040723167],"domain_scores_gemma":[0.9994336,0.00023146662,0.00011981311,0.00003484998,0.00010984754,0.00007041179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008049852,0.0001996229,0.00021074055,0.0031326134,0.00036490092,0.0006562149,0.0001872562,0.00017690987,0.0004232817],"category_scores_gemma":[0.0015767337,0.000094476396,0.00030389568,0.001490148,0.0004003776,0.001304944,0.00047708696,0.00016767113,0.00006452547],"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":[0.00023481926,0.0000900135,0.9108273,0.00011516119,0.00013913536,0.000379535,0.0010948735,0.019086666,0.009368138,0.0023368064,0.00036730844,0.05596031],"study_design_scores_gemma":[0.0000045605175,0.00006552041,0.9482515,0.000014647504,0.000028255488,0.00023387402,0.000821439,0.048277743,0.00090190285,0.0007954807,0.0005806984,0.00002436133],"about_ca_topic_score_codex":0.0046475204,"about_ca_topic_score_gemma":0.0041267034,"teacher_disagreement_score":0.0046475204,"about_ca_system_score_codex":0.00037449456,"about_ca_system_score_gemma":0.00018045164,"threshold_uncertainty_score":0.009240925},"labels":[],"label_agreement":null},{"id":"W4405266709","doi":"10.1016/j.rse.2024.114568","title":"Long-term prediction of Arctic sea ice concentrations using deep learning: Effects of surface temperature, radiation, and wind conditions","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Korea Institute of Marine Science and Technology promotion; Korea Polar Research Institute; Ministry of Oceans and Fisheries","keywords":"Remote sensing; Term (time); Environmental science; Arctic; Sea ice; Sea surface temperature; Climatology; Meteorology; Radiation; Wind speed; Sea ice concentration; Arctic ice pack; Atmospheric sciences; Geology; Oceanography; Sea ice thickness; Geography","score_opus":0.006281783786645682,"score_gpt":0.19892562287098448,"score_spread":0.1926438390843388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405266709","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9878747,0.00048828515,0.009804,0.0003540909,0.00012589016,0.0000050990284,0.000580914,0.0001985701,0.0005684158],"genre_scores_gemma":[0.99648273,0.00009959923,0.0020127178,0.000038308943,0.000027939408,0.0000039604406,0.000868194,0.000015243826,0.00045126723],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998994,0.000015883848,0.000006908719,0.000037089285,0.000010971929,0.000029748695],"domain_scores_gemma":[0.9993849,0.00028932028,0.000063729305,0.000042406213,0.00015324826,0.00006636963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006352067,0.0007651118,0.0004897831,0.00029548063,0.00036910884,0.0005747739,0.00050158816,0.0006982736,0.00047500187],"category_scores_gemma":[0.0013796563,0.00038719014,0.00050918874,0.0003026365,0.00027869843,0.0007760456,0.00041694916,0.0011998026,0.00023671064],"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":[0.000449926,0.00044026467,0.054390814,0.00004211334,0.0002215621,0.00009881117,0.000043878477,0.89835376,0.006207161,0.00027509104,0.0020561595,0.037420552],"study_design_scores_gemma":[0.00000572167,0.000013025361,0.005027369,0.0000030383358,0.000010574002,0.000004000587,0.0000062940053,0.9939428,0.00081156084,0.0001175815,0.000053095624,0.000004897552],"about_ca_topic_score_codex":0.034342866,"about_ca_topic_score_gemma":0.04183289,"teacher_disagreement_score":0.034342866,"about_ca_system_score_codex":0.00062424055,"about_ca_system_score_gemma":0.00095096097,"threshold_uncertainty_score":0.06828594},"labels":[],"label_agreement":null},{"id":"W4405660110","doi":"10.1016/j.rse.2024.114577","title":"Ground surface displacement measurement from SAR imagery using deep learning","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","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":"Defense Acquisition Program Administration; Institute of Civil-Military Technology Cooperation; Ministry of Trade, Industry and Energy; Southern Methodist University; Nvidia","keywords":"Remote sensing; Geology; Synthetic aperture radar; Displacement (psychology); Surface (topology)","score_opus":0.015351239572801139,"score_gpt":0.21335200559577888,"score_spread":0.19800076602297775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405660110","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50953895,0.00052225357,0.4790759,0.00039761476,0.00013521442,0.000053558844,0.0010688402,0.0035566727,0.0056509627],"genre_scores_gemma":[0.942767,0.00013373476,0.054707777,0.000060087972,0.000017541583,0.000017098078,0.00080727955,0.00003279919,0.0014566531],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99990857,0.000009882717,0.000004633739,0.00003073088,0.00003160124,0.00001454763],"domain_scores_gemma":[0.9998989,0.000019856696,0.000020578716,0.000016364918,0.00003583964,0.000008388704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015979396,0.00052829174,0.00020998198,0.00040092607,0.000109234505,0.0002791601,0.0004117829,0.00031554248,0.0009317518],"category_scores_gemma":[0.00049645174,0.00018002835,0.00024933045,0.0004814948,0.00016727447,0.0005426128,0.00037794097,0.00048090695,0.000366422],"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":[0.00013071187,0.00014325665,0.016823865,0.00009281562,0.00009365132,0.00013515183,0.000061464554,0.609809,0.046868876,0.0013777259,0.0029900833,0.3214734],"study_design_scores_gemma":[0.0000032741666,0.00001905432,0.0035896166,0.0000040767327,0.0000056447607,0.0000136297485,0.000009829561,0.9899995,0.0054964568,0.00044869288,0.00040340534,0.0000069337248],"about_ca_topic_score_codex":0.006008213,"about_ca_topic_score_gemma":0.011299516,"teacher_disagreement_score":0.006008213,"about_ca_system_score_codex":0.00031473307,"about_ca_system_score_gemma":0.00039058452,"threshold_uncertainty_score":0.011946499},"labels":[],"label_agreement":null},{"id":"W4405779151","doi":"10.1016/j.rse.2024.114581","title":"The impact of leaf-wood separation algorithms on aboveground biomass estimation from terrestrial laser scanning","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":23,"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 Lethbridge","funders":"European Metrology Programme for Innovation and Research; National Key Research and Development Program of China; China Scholarship Council; National Natural Science Foundation of China; National Centre for Earth Observation; Natural Environment Research Council; European Commission; Sight Research UK","keywords":"Remote sensing; Biomass (ecology); Environmental science; Laser scanning; Lidar; Separation (statistics); Algorithm; Computer science; Laser; Geology; Optics; Agronomy; Machine learning; Physics; Biology","score_opus":0.015749517355695197,"score_gpt":0.2888623133072052,"score_spread":0.27311279595151,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405779151","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93168175,0.0028308898,0.05762146,0.00021104614,0.00012510965,0.00007800781,0.0021245214,0.0022859913,0.0030411563],"genre_scores_gemma":[0.8812049,0.0006956767,0.10547782,0.0002196344,0.000041000305,0.0000815453,0.011181471,0.00038234406,0.0007156499],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9976787,0.00049615296,0.00017410777,0.00067561277,0.0007931927,0.00018233065],"domain_scores_gemma":[0.99647313,0.0017611705,0.000363601,0.0005213681,0.000771867,0.00010879823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004264608,0.0012805394,0.0007033404,0.0018741891,0.00069463573,0.0017440858,0.0011590784,0.0009856997,0.00051972334],"category_scores_gemma":[0.008148827,0.00031179402,0.0010955392,0.0017965629,0.00050238316,0.001600488,0.0011543069,0.0008583729,0.0007048687],"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":[0.0009903118,0.0004089354,0.26611662,0.0007069645,0.0010615649,0.00022479042,0.00046942028,0.2061547,0.041276608,0.0013989204,0.005642985,0.47554818],"study_design_scores_gemma":[0.000072454306,0.00029996556,0.20943648,0.00016661017,0.0002395334,0.00028592217,0.00051131006,0.7543792,0.026802186,0.0014181448,0.006279329,0.00010883901],"about_ca_topic_score_codex":0.01360552,"about_ca_topic_score_gemma":0.020473054,"teacher_disagreement_score":0.01360552,"about_ca_system_score_codex":0.00073359837,"about_ca_system_score_gemma":0.0008187742,"threshold_uncertainty_score":0.0270527},"labels":[],"label_agreement":null},{"id":"W4406727353","doi":"10.1016/j.rse.2025.114610","title":"Combining Landsat 5 TM and UAV images to estimate river discharge with limited ground-based flow velocity and water level observations","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Higher Education Discipline Innovation Project; National Natural Science Foundation of China","keywords":"Remote sensing; Environmental science; Geology; Flow (mathematics); Physics","score_opus":0.01824930974507708,"score_gpt":0.23720240926118163,"score_spread":0.21895309951610456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406727353","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9196653,0.00065607234,0.072197616,0.00011561572,0.0001342274,0.000094647425,0.0026185357,0.0014777185,0.0030403468],"genre_scores_gemma":[0.92648906,0.0002602844,0.06834683,0.00006740755,0.000043940425,0.00005121957,0.0034685116,0.00006539585,0.0012073119],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997744,0.000024987872,0.00001622697,0.000072550974,0.00007008576,0.000041737327],"domain_scores_gemma":[0.9997868,0.000032143405,0.00003968314,0.000030787676,0.000090322246,0.000020236781],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003040733,0.00076240033,0.00059065217,0.0019079862,0.00025171135,0.000672126,0.00032560277,0.00042012642,0.00066605455],"category_scores_gemma":[0.00049897656,0.00033385213,0.00058745145,0.0017127277,0.00010353582,0.0009755986,0.00036454384,0.00026148235,0.0004599517],"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":[0.000455453,0.0006455239,0.22418056,0.0003010606,0.00064721785,0.00048478492,0.00024774042,0.07711824,0.16769029,0.0004025967,0.0069659194,0.52086055],"study_design_scores_gemma":[0.00006780719,0.00021389256,0.3376219,0.00004691776,0.00037713436,0.00020545593,0.0003169868,0.64009845,0.017371157,0.00050276663,0.0030907975,0.00008676534],"about_ca_topic_score_codex":0.01427343,"about_ca_topic_score_gemma":0.027413964,"teacher_disagreement_score":0.01427343,"about_ca_system_score_codex":0.00028770426,"about_ca_system_score_gemma":0.0005246821,"threshold_uncertainty_score":0.028380692},"labels":[],"label_agreement":null},{"id":"W4406787388","doi":"10.1016/j.rse.2025.114607","title":"Enhanced sea ice classification for ICESat-2 using combined unsupervised and supervised machine learning","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Fundamental Research Funds for the Central Universities; China Scholarship Council; National Natural Science Foundation of China; European Space Agency","keywords":"Remote sensing; Sea ice; Geology; Computer science; Environmental science; Climatology","score_opus":0.01812853470977914,"score_gpt":0.22183757860747425,"score_spread":0.20370904389769512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406787388","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6788784,0.00066865294,0.3016107,0.00032689213,0.000336578,0.00017942028,0.0032691727,0.008219944,0.0065101534],"genre_scores_gemma":[0.8170115,0.00012909311,0.16992591,0.000131771,0.000117426105,0.00012100823,0.0068415576,0.00024381335,0.005477935],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973625,0.000052969546,0.000015574517,0.000064317384,0.000090670736,0.000040147544],"domain_scores_gemma":[0.9995658,0.000102888145,0.000030200463,0.00006719155,0.00021444018,0.000019526855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048896,0.0005754533,0.0004752206,0.000837177,0.00028139938,0.0005014758,0.0004605675,0.0003847466,0.0013562351],"category_scores_gemma":[0.0007506119,0.00019349635,0.00057547825,0.00047297773,0.00012755358,0.00072159886,0.0005477019,0.00048943795,0.00080235803],"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":[0.0007054508,0.0010812782,0.02416209,0.00015979074,0.0003666052,0.00014564216,0.00014260157,0.09483717,0.08981337,0.0006640769,0.01435503,0.77356696],"study_design_scores_gemma":[0.000038094175,0.00007164031,0.018950015,0.000007154537,0.000049310078,0.00003404016,0.000045242654,0.96332014,0.015004022,0.00042375753,0.0020352867,0.000021298765],"about_ca_topic_score_codex":0.007552343,"about_ca_topic_score_gemma":0.021827128,"teacher_disagreement_score":0.007552343,"about_ca_system_score_codex":0.00021898287,"about_ca_system_score_gemma":0.00068840303,"threshold_uncertainty_score":0.015016794},"labels":[],"label_agreement":null},{"id":"W4407069353","doi":"10.1016/j.rse.2025.114618","title":"Individual tree crown delineation in high resolution aerial RGB imagery using StarDist-based model","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":31,"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 New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Remote sensing; Crown (dentistry); Aerial survey; Aerial photos; Tree (set theory); RGB color model; Aerial imagery; Artificial intelligence; Geology; Environmental science; Computer science; Mathematics","score_opus":0.01796765337428841,"score_gpt":0.24425928121091892,"score_spread":0.2262916278366305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407069353","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2123673,0.0006735983,0.77982235,0.00018061633,0.000060854887,0.000081700346,0.00068086776,0.002515436,0.0036173635],"genre_scores_gemma":[0.7537298,0.00033349803,0.24080302,0.00014887542,0.000032556403,0.000057836438,0.0014995289,0.00022431526,0.0031706025],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983203,0.00001957571,0.000008535519,0.00005708628,0.00005249184,0.00003023433],"domain_scores_gemma":[0.9998142,0.00004056097,0.000027133101,0.000034855013,0.00006504965,0.000018141443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003142237,0.00051605946,0.00042505556,0.00079759094,0.00019359472,0.00077993626,0.0008759638,0.00053366664,0.0009845814],"category_scores_gemma":[0.00059639924,0.00025725283,0.00074756367,0.0005244201,0.0002830901,0.0006972794,0.0006052958,0.0004968212,0.0005330771],"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":[0.00029410675,0.00008339384,0.009249125,0.00013104887,0.000095089,0.00023415567,0.00020520216,0.6429605,0.028825806,0.0039784582,0.0039862017,0.30995694],"study_design_scores_gemma":[0.0000027038893,0.000010136138,0.0011534599,0.000005682433,0.000007227514,0.000056254747,0.000011105999,0.99521893,0.0023144628,0.0005829753,0.0006298866,0.00000722462],"about_ca_topic_score_codex":0.007020945,"about_ca_topic_score_gemma":0.0132252555,"teacher_disagreement_score":0.007020945,"about_ca_system_score_codex":0.0005173533,"about_ca_system_score_gemma":0.00060867256,"threshold_uncertainty_score":0.013960183},"labels":[],"label_agreement":null},{"id":"W4407098467","doi":"10.1016/j.rse.2024.114586","title":"Estimating global transpiration from TROPOMI SIF with angular normalization and separation for sunlit and shaded leaves","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Normalization (sociology); Remote sensing; Transpiration; Environmental science; Geology; Chemistry; Photosynthesis","score_opus":0.005081502256545575,"score_gpt":0.21438808587152766,"score_spread":0.20930658361498208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407098467","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84035933,0.00029458886,0.14985907,0.000057772137,0.000035761786,0.00003299948,0.0031388574,0.0028973431,0.0033243604],"genre_scores_gemma":[0.8755915,0.0001288351,0.117545694,0.000027829832,0.000013045221,0.00004686515,0.0048838053,0.0004978838,0.0012644989],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9999037,0.000008896476,0.0000041318635,0.0000423538,0.000021607279,0.000019307043],"domain_scores_gemma":[0.9998989,0.000020496025,0.000008665636,0.000020607666,0.00004376048,0.000007533662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002304988,0.00048949046,0.00033749823,0.0006660958,0.00027661404,0.00034784354,0.00028694022,0.0002861057,0.0012936397],"category_scores_gemma":[0.0005064735,0.00021882089,0.00050828076,0.0009147606,0.00009710559,0.00047559539,0.00015841825,0.00032343587,0.0006773691],"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":[0.00036564504,0.0001424933,0.09929111,0.00017872681,0.00028357183,0.00012741578,0.00027743037,0.054330043,0.5064927,0.0006873806,0.0030600212,0.33476344],"study_design_scores_gemma":[0.000046619603,0.000062818784,0.5371742,0.000020377729,0.00013732744,0.00022398932,0.0001526282,0.35880402,0.097885124,0.0006111407,0.0048029153,0.0000789329],"about_ca_topic_score_codex":0.015978595,"about_ca_topic_score_gemma":0.037459724,"teacher_disagreement_score":0.015978595,"about_ca_system_score_codex":0.00028503925,"about_ca_system_score_gemma":0.0004664822,"threshold_uncertainty_score":0.031771183},"labels":[],"label_agreement":null},{"id":"W4407270339","doi":"10.1016/j.rse.2025.114635","title":"Spatiotemporal evolution characteristics of ground deformation in the Beijing Plain from 1992 to 2023 derived from a novel multi-sensor InSAR fusion method","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Space Agency; National Natural Science Foundation of China; European Space Agency; National Aeronautics and Space Administration","keywords":"Remote sensing; Beijing; Interferometric synthetic aperture radar; Environmental science; Fusion; Sensor fusion; Geology; Synthetic aperture radar; Geography; China; Computer science; Artificial intelligence","score_opus":0.013259580695989561,"score_gpt":0.23692771132056428,"score_spread":0.22366813062457472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407270339","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99737847,0.0000643623,0.0002502311,0.000036256282,0.0000062986846,0.000004623099,0.001698333,0.000022735212,0.00053868594],"genre_scores_gemma":[0.9959566,0.00004560038,0.00022888074,0.0000062934073,0.0000046305604,0.0000052108835,0.0033746026,0.0000029598953,0.00037517294],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99990773,0.000006282521,0.000008314127,0.000030509418,0.00002302671,0.000024155202],"domain_scores_gemma":[0.999811,0.000017477441,0.00006501421,0.000016681495,0.000059937607,0.000029971361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023208947,0.00031426712,0.00018007349,0.001018892,0.00018981512,0.00034094675,0.00023268393,0.00035869644,0.00076767383],"category_scores_gemma":[0.00034903042,0.00014356988,0.00028709485,0.0014168412,0.00017098834,0.00033260966,0.00021275849,0.00017864667,0.00019692667],"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":[0.0007373245,0.00019387665,0.90607274,0.00009941566,0.00027955882,0.00097011204,0.00035586738,0.04037809,0.017753245,0.00052032736,0.0031869507,0.029452477],"study_design_scores_gemma":[0.0000056152135,0.000019976887,0.986771,0.0000035540977,0.000027735665,0.000057754678,0.00007401575,0.011868145,0.0005359454,0.000026864926,0.0006001154,0.000009214347],"about_ca_topic_score_codex":0.045422047,"about_ca_topic_score_gemma":0.062434718,"teacher_disagreement_score":0.045422047,"about_ca_system_score_codex":0.0006674627,"about_ca_system_score_gemma":0.00038648045,"threshold_uncertainty_score":0.09031534},"labels":[],"label_agreement":null},{"id":"W4407568394","doi":"10.1016/j.rse.2025.114649","title":"Identification of geothermal anomalies from Landsat derived land surface temperature, Mount Meager volcanic complex, British Columbia, Canada","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Commission Géologique du Canada; Office of Energy Research and Development; Natural Resources Canada; U.S. Geological Survey","keywords":"Mount; Remote sensing; Volcano; Geothermal gradient; Geology; Identification (biology); Seismology; Geophysics","score_opus":0.011536739181926532,"score_gpt":0.18639438534667388,"score_spread":0.17485764616474736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407568394","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94935143,0.0012204399,0.00079663657,0.000763604,0.00003925534,0.00014595606,0.018215302,0.00012590086,0.029341511],"genre_scores_gemma":[0.96960384,0.0006260019,0.0020080472,0.000104553386,0.000007415809,0.000046124405,0.0070341327,0.00003720841,0.020532757],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99976903,0.000016298907,0.000014457532,0.000040973966,0.00009227921,0.00006708848],"domain_scores_gemma":[0.9986135,0.00005747858,0.00005030258,0.00002778502,0.0011054005,0.0001454532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028090083,0.00030223053,0.00020072435,0.0020969005,0.0027830626,0.0012342945,0.00079577655,0.00030654893,0.0038272385],"category_scores_gemma":[0.001211299,0.00021327296,0.00012990132,0.0028337138,0.00029951896,0.0002541101,0.00044848528,0.0005126736,0.0006612792],"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":[0.00022527699,0.00014598366,0.87600535,0.00013193808,0.000098240314,0.00036329875,0.0012682634,0.002824541,0.0065899314,0.0010227727,0.0273808,0.08394356],"study_design_scores_gemma":[0.000017125323,0.000009532515,0.9827793,0.00007123578,0.00002693927,0.000052028816,0.0017209764,0.0033601301,0.0011146914,0.00008316354,0.010745727,0.000019139672],"about_ca_topic_score_codex":0.99735427,"about_ca_topic_score_gemma":0.9994929,"teacher_disagreement_score":0.017460357,"about_ca_system_score_codex":0.017460357,"about_ca_system_score_gemma":0.022781735,"threshold_uncertainty_score":0.12668437},"labels":[],"label_agreement":null},{"id":"W4407959141","doi":"10.1016/j.rse.2025.114667","title":"A novel GSM and fluorescence coupled full-spectral chlorophyll <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si92.svg\"> <mml:mi mathvariant=\"bold-italic\">a</mml:mi> </mml:math> algorithm for waters with high CDM content","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Water Quality Monitoring and Analysis","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":"Bedford Institute of Oceanography; Université Laval; Dillon Consulting","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Sichuan Province; National Aeronautics and Space Administration; California Institute of Technology; ArcticNet; Université Laval; Japan Aerospace Exploration Agency; Centre National d’Etudes Spatiales; Canada Excellence Research Chairs, Government of Canada","keywords":"Computer science","score_opus":0.016269079663387167,"score_gpt":0.21558856567468718,"score_spread":0.1993194860113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407959141","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033799414,0.00014645234,0.9614762,0.00014766643,0.00007603726,0.000069134,0.00012316197,0.0031354155,0.0010264042],"genre_scores_gemma":[0.095890716,0.00008011875,0.8996679,0.00017943514,0.000042823987,0.00011281068,0.0005984966,0.00022063576,0.0032071364],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996815,0.00004662492,0.000021799726,0.000096310374,0.000117138145,0.000036613983],"domain_scores_gemma":[0.9995283,0.00015119177,0.000047635614,0.00006529435,0.0001712425,0.000036381585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008204588,0.0009814351,0.0005704598,0.0008038388,0.00048616962,0.0007771079,0.0016506615,0.0015201494,0.002023475],"category_scores_gemma":[0.0017112585,0.00032401393,0.000775121,0.00085722737,0.00040230894,0.0009470288,0.0010916948,0.00091568875,0.0013150689],"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":[0.00039039753,0.00023457537,0.004227484,0.00012313062,0.0001812582,0.00012483477,0.00014309675,0.18573564,0.060786985,0.005783998,0.005513516,0.7367551],"study_design_scores_gemma":[0.000032000014,0.000069617716,0.0007763821,0.0000062382683,0.00001607232,0.00009244801,0.000020988602,0.9785452,0.015981311,0.0014180811,0.0030196188,0.000022031745],"about_ca_topic_score_codex":0.0063100876,"about_ca_topic_score_gemma":0.008687559,"teacher_disagreement_score":0.0063100876,"about_ca_system_score_codex":0.0007511439,"about_ca_system_score_gemma":0.0017916774,"threshold_uncertainty_score":0.012546718},"labels":[],"label_agreement":null},{"id":"W4408477250","doi":"10.1016/j.rse.2025.114705","title":"Urban thermal anisotropies by local climate zones: An assessment using multi-angle land surface temperatures from ECOSTRESS","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Urban Heat Island Mitigation","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":false,"ca_institutions":"Western University","funders":"Hong Kong Polytechnic University","keywords":"Remote sensing; Environmental science; Thermal; Surface (topology); Anisotropy; Meteorology; Geology; Geography; Optics; Physics; Geometry","score_opus":0.010314885893304723,"score_gpt":0.2569532845812584,"score_spread":0.24663839868795365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408477250","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9870003,0.00013049453,0.0110666035,0.00005186216,0.000011743657,0.000027628972,0.00077179354,0.00019945043,0.00074002985],"genre_scores_gemma":[0.9927955,0.0000726393,0.0062064743,0.000008274735,0.000009378684,0.000011522641,0.00077451556,0.000027682692,0.00009395062],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99973637,0.000063116335,0.000015862748,0.00007826265,0.00006986742,0.000036497317],"domain_scores_gemma":[0.99952865,0.00011826678,0.00009237236,0.00008755419,0.00012766241,0.00004558986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079123175,0.00053883163,0.00028247864,0.0013200721,0.00020915731,0.0005772444,0.00033602875,0.00033699247,0.00034542358],"category_scores_gemma":[0.0011167076,0.0002784055,0.0007005649,0.0013014755,0.00030806835,0.0006200496,0.0004395866,0.00023553701,0.00011723148],"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":[0.00045489788,0.00016872636,0.56873876,0.00016896549,0.00039534518,0.00046101035,0.0005437155,0.3388852,0.031213637,0.00082206086,0.0009985763,0.057149116],"study_design_scores_gemma":[0.000030014711,0.000049539296,0.4103664,0.000018831011,0.00012237848,0.00010118698,0.0003391194,0.580811,0.0068033966,0.00026740957,0.0010290771,0.00006173574],"about_ca_topic_score_codex":0.015204298,"about_ca_topic_score_gemma":0.026299644,"teacher_disagreement_score":0.015204298,"about_ca_system_score_codex":0.00029193118,"about_ca_system_score_gemma":0.00031467967,"threshold_uncertainty_score":0.030231595},"labels":[],"label_agreement":null},{"id":"W4408802068","doi":"10.1016/j.rse.2025.114715","title":"Quantified positive radiative forcing at a greening Canadian boreal-Arctic transition over the last four decades","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université du Québec à Trois-Rivières; Makivik Corporation; Université Laval","funders":"","keywords":"Greening; Radiative forcing; Environmental science; Remote sensing; Boreal; Forcing (mathematics); Arctic; Taiga; Climatology; Radiative transfer; Atmospheric sciences; Geography; Meteorology; Physics; Ecology; Geology; Biology; Forestry","score_opus":0.02229385879898384,"score_gpt":0.2209105351963395,"score_spread":0.19861667639735564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408802068","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9927085,0.0002697542,0.00014909965,0.00014319614,0.000009202391,0.000011722217,0.004288399,0.000020987705,0.0023991398],"genre_scores_gemma":[0.9978416,0.00010189582,0.00015242657,0.000032049094,0.0000032318992,0.0000052256228,0.0011844841,0.000003209851,0.0006759406],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998234,0.00001037868,0.0000074116933,0.00004123547,0.000060785012,0.000056807352],"domain_scores_gemma":[0.99951434,0.00002312864,0.00006745736,0.000014466343,0.0003102636,0.00007037942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025520148,0.0002402268,0.00015359359,0.0006920498,0.0012239581,0.00083468814,0.00033052656,0.00027613502,0.0010136208],"category_scores_gemma":[0.0005451524,0.00010612434,0.00022349478,0.0011807481,0.00032447392,0.00022452851,0.00032852317,0.00023930828,0.00010035134],"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":[0.000104465784,0.000025073223,0.98187846,0.000043813507,0.00008305839,0.000096004456,0.0005200966,0.002531305,0.0031897128,0.00017495362,0.0016373366,0.009715813],"study_design_scores_gemma":[0.000001442693,0.0000036628646,0.99807394,0.000004131353,0.000008777684,0.000010745028,0.00021351452,0.0005433579,0.00010757708,0.0000063843854,0.0010212323,0.0000052510663],"about_ca_topic_score_codex":0.9921881,"about_ca_topic_score_gemma":0.99685436,"teacher_disagreement_score":0.012239914,"about_ca_system_score_codex":0.012239914,"about_ca_system_score_gemma":0.007970213,"threshold_uncertainty_score":0.088807225},"labels":[],"label_agreement":null},{"id":"W4409124806","doi":"10.1016/j.rse.2025.114717","title":"Next generation Arctic vegetation maps: Aboveground plant biomass and woody dominance mapped at 30 m resolution across the tundra biome","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; Nova Scotia Community College; Carleton University","funders":"H2020 Fast Track to Innovation; Biological and Environmental Research; Division of Environmental Biology; Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Academy of Finland; Office of Science; Otto A. Malm Lahjoitusrahasto; Nordenskiöld-samfundet; ArcticNet; U.S. Department of Energy; Vetenskapsrådet; Queen's University; European Commission; National Research Council; Office of Polar Programs; Université Laval; Polar Knowledge Canada; Societas pro Fauna et Flora Fennica; Ministère des Forêts, de la Faune et des Parcs; Danmarks Frie Forskningsfond; National Science Foundation; National Aeronautics and Space Administration; Google; FP7 Ideas: European Research Council; Natural Resources Canada","keywords":"Tundra; Biome; Remote sensing; Dominance (genetics); Environmental science; Vegetation (pathology); Biomass (ecology); Arctic; Arctic vegetation; Physical geography; Ecology; Geography; Ecosystem; Biology","score_opus":0.03958461956686757,"score_gpt":0.23901019279237185,"score_spread":0.19942557322550428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409124806","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7540898,0.0007896941,0.05669854,0.00040401693,0.00011662917,0.00010018238,0.17412539,0.0050840895,0.008591646],"genre_scores_gemma":[0.8238689,0.00035570495,0.0706399,0.0000831144,0.000041980933,0.00011876002,0.102879204,0.0002626412,0.001749934],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99988127,0.000022506927,0.0000048616557,0.00004044881,0.000028457229,0.00002234799],"domain_scores_gemma":[0.9996749,0.000047306614,0.000037056,0.0000430015,0.00016039789,0.000037353657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044828994,0.00044675975,0.0001954023,0.00095588306,0.00021745545,0.0005386324,0.00030599968,0.00030884615,0.0020378463],"category_scores_gemma":[0.00091061986,0.000220684,0.00052834715,0.0011985581,0.000120640325,0.00031235424,0.0003415035,0.0003767634,0.0006652492],"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":[0.0005783604,0.0003960862,0.24543424,0.00035646313,0.0006576572,0.00055275415,0.00064114033,0.53661966,0.025842927,0.0038186717,0.044813514,0.1402885],"study_design_scores_gemma":[0.00008384762,0.000040859853,0.3639761,0.00009195292,0.00010385326,0.00014887504,0.0003325441,0.5871724,0.006747782,0.0028140452,0.03839461,0.00009309114],"about_ca_topic_score_codex":0.08793116,"about_ca_topic_score_gemma":0.12425671,"teacher_disagreement_score":0.08793116,"about_ca_system_score_codex":0.0004576096,"about_ca_system_score_gemma":0.0006789018,"threshold_uncertainty_score":0.17483866},"labels":[],"label_agreement":null},{"id":"W4409180776","doi":"10.1016/j.rse.2025.114713","title":"Net primary production in the Labrador Sea between 2014 and 2022 derived from ocean colour remote sensing based on ecological regimes","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","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":"Dalhousie University; Bedford Institute of Oceanography; Fisheries and Oceans Canada","funders":"Fisheries and Oceans Canada; Canada Research Chairs","keywords":"Remote sensing; Environmental science; Primary (astronomy); Oceanography; Primary production; Geography; Geology; Ecology; Ecosystem; Biology; Physics","score_opus":0.009121928675435617,"score_gpt":0.1859435636781179,"score_spread":0.17682163500268228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409180776","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98574257,0.00028264584,0.00027230044,0.00006250355,0.000019293337,0.000006373612,0.012878157,0.000068993926,0.0006670598],"genre_scores_gemma":[0.97947615,0.000116267074,0.00024618057,0.000033163076,0.000014317462,0.000012162074,0.019742738,0.000013655318,0.00034529166],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997334,0.000041542033,0.00002989114,0.00010105092,0.000043966305,0.00005015864],"domain_scores_gemma":[0.99939466,0.0000725751,0.00024141598,0.000052033258,0.00017114842,0.000068162146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006776384,0.00074053067,0.00033891515,0.0014448563,0.0002194793,0.00083215354,0.0004439593,0.00030166714,0.00084446574],"category_scores_gemma":[0.00090494147,0.00022986303,0.001113689,0.0014880451,0.00025368916,0.0005209876,0.00050777936,0.00026609274,0.0005306248],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017383423,0.00003134519,0.98956853,0.000050331862,0.00029146648,0.00007592516,0.00003493795,0.0034566978,0.0008080181,0.00007162618,0.0011786714,0.004258551],"study_design_scores_gemma":[0.000005505995,0.000016236661,0.99602306,0.0000096509075,0.00004226284,0.00003170673,0.000059143,0.0029548327,0.00025830037,0.000021929309,0.0005693704,0.000007962732],"about_ca_topic_score_codex":0.0986058,"about_ca_topic_score_gemma":0.106919885,"teacher_disagreement_score":0.9013942,"about_ca_system_score_codex":0.0013731492,"about_ca_system_score_gemma":0.0006532108,"threshold_uncertainty_score":0.1960637},"labels":[],"label_agreement":null},{"id":"W4410419894","doi":"10.1016/j.rse.2025.114797","title":"GROUNDED EO: Data-driven Sentinel-2 LAI and FAPAR retrieval using Gaussian processes trained with extensive fiducial reference measurements","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","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":"Natural Resources Canada","funders":"Natural Resources Canada; Freistaat Sachsen; European Research Council; National Science Foundation; Vetenskapsrådet; H2020 European Research Council; European Space Agency; European Commission; Battelle","keywords":"Remote sensing; Fiducial marker; Computer science; Gaussian; Gaussian process; Artificial intelligence; Geology; Physics","score_opus":0.050660985544835224,"score_gpt":0.2642968541334695,"score_spread":0.21363586858863426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410419894","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26965183,0.00021076108,0.7204367,0.00012505666,0.000057544465,0.0001169288,0.0015303985,0.0057854136,0.0020853155],"genre_scores_gemma":[0.77836335,0.00012524423,0.21390663,0.00010776492,0.000028434695,0.000091155875,0.0054846304,0.00019835298,0.0016945073],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981993,0.000023184148,0.000007850777,0.00006128059,0.00006175538,0.000026072206],"domain_scores_gemma":[0.999772,0.00005386846,0.000030168403,0.0000562825,0.00007210177,0.00001553748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006385551,0.0005148035,0.00027764766,0.00047238634,0.00015963268,0.00041238358,0.0011166321,0.00043599054,0.0006932093],"category_scores_gemma":[0.0013868001,0.00021408954,0.00035800668,0.000616737,0.00023723391,0.00070595136,0.0007647903,0.00052739517,0.00045034155],"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":[0.0005849273,0.0004836572,0.018930223,0.00014512416,0.00013195822,0.0002773634,0.00016513099,0.5255037,0.07235536,0.005461096,0.0064540077,0.36950737],"study_design_scores_gemma":[0.00002064885,0.00003464346,0.00271055,0.0000030060073,0.0000063193456,0.000027886541,0.000011940007,0.9884679,0.0070089516,0.00084717496,0.0008483412,0.000012619572],"about_ca_topic_score_codex":0.01291489,"about_ca_topic_score_gemma":0.012318015,"teacher_disagreement_score":0.01291489,"about_ca_system_score_codex":0.0003688016,"about_ca_system_score_gemma":0.0006493027,"threshold_uncertainty_score":0.02567947},"labels":[],"label_agreement":null},{"id":"W4410765560","doi":"10.1016/j.rse.2025.114829","title":"Observing carbon monoxide and volatile organic compounds from Canadian wildfires in 2023 from FengYun-3E/HIRAS-II in a dawn-dusk sun-synchronous orbit","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto; Environment and Climate Change Canada","funders":"National Meteorological Satellite Center; Chinese Medical Association; China Meteorological Administration; National Natural Science Foundation of China; Belgian Federal Science Policy Office; Fonds De La Recherche Scientifique - FNRS; National Key Research and Development Program of China; Peking University; Endocrine Society of Australia","keywords":"Dusk; Carbon monoxide; Environmental science; Remote sensing; Meteorology; Orbit (dynamics); Atmospheric sciences; Physics; Geography; Chemistry; Astronomy; Aerospace engineering; Catalysis","score_opus":0.004849851968030134,"score_gpt":0.17786819330217105,"score_spread":0.1730183413341409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410765560","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99402845,0.00012862454,0.00033232378,0.00008602088,0.000029283381,0.000032572156,0.0029531652,0.00013130222,0.002278402],"genre_scores_gemma":[0.9910652,0.000094685216,0.0016371892,0.00007653932,0.000014155731,0.000022659744,0.005404678,0.000026138876,0.0016587619],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997514,0.000005741326,0.0000037996517,0.000047819223,0.00009700258,0.00009425944],"domain_scores_gemma":[0.99976176,0.000007865053,0.000014545911,0.000012766777,0.000106832136,0.00009627367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031603564,0.00083153136,0.00060862035,0.0009179198,0.0021228166,0.00055921555,0.0008168922,0.00071085664,0.0011148312],"category_scores_gemma":[0.00021841796,0.00038983734,0.000585723,0.001172479,0.00050162594,0.0003538494,0.00062883645,0.00081214716,0.00024330917],"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":[0.0031962092,0.0008989631,0.6427153,0.00025359367,0.0008101612,0.0019229084,0.0016012978,0.020174712,0.28245616,0.00044334045,0.013524106,0.032003194],"study_design_scores_gemma":[0.00009818805,0.00007669958,0.9809074,0.00000965981,0.00009413871,0.00007567497,0.0003554251,0.008897977,0.006749769,0.00003338126,0.0026621034,0.0000395937],"about_ca_topic_score_codex":0.8407746,"about_ca_topic_score_gemma":0.94348276,"teacher_disagreement_score":0.1592254,"about_ca_system_score_codex":0.0036207186,"about_ca_system_score_gemma":0.006269653,"threshold_uncertainty_score":0.3203262},"labels":[],"label_agreement":null},{"id":"W4411051150","doi":"10.1016/j.rse.2025.114815","title":"Mapping of sea ice in 1975 and 1976 using the NIMBUS-6 Scanning Microwave Spectrometer (SCAMS)","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","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 Manitoba","funders":"","keywords":"Remote sensing; Microwave; Spectrometer; Environmental science; Sea ice; Imaging spectrometer; Geology; Geography; Meteorology; Optics; Physics; Computer science; Telecommunications","score_opus":0.011393836400607892,"score_gpt":0.202714079391553,"score_spread":0.1913202429909451,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411051150","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9792662,0.00029860475,0.0014030824,0.000053270163,0.000037818787,0.000043572683,0.012241772,0.0001666072,0.0064889626],"genre_scores_gemma":[0.9606161,0.00038404926,0.008778317,0.000037006514,0.00009705925,0.00007025925,0.027373943,0.00003627808,0.0026068655],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99990606,0.0000070933393,0.0000055002847,0.000022064572,0.000042537966,0.000016739386],"domain_scores_gemma":[0.9998646,0.00000790505,0.000029828394,0.000012974639,0.000056886565,0.000027827526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016548322,0.0002746654,0.0001549175,0.0018854144,0.0002868352,0.0002172652,0.00023162301,0.00016098807,0.00079529855],"category_scores_gemma":[0.00027276063,0.000075936325,0.000119045835,0.0013875953,0.00014737569,0.00016566178,0.0001981485,0.00014868556,0.0002606746],"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":[0.0009868896,0.00024386453,0.7381387,0.00023893415,0.00020556457,0.0013708232,0.00096524635,0.011520005,0.066287704,0.001255607,0.017154938,0.16163176],"study_design_scores_gemma":[0.000018036704,0.000036072703,0.9832698,0.000010115165,0.000014270534,0.00007524214,0.000117115,0.0028233277,0.003749244,0.000074416064,0.00980354,0.000008863531],"about_ca_topic_score_codex":0.045081902,"about_ca_topic_score_gemma":0.11429035,"teacher_disagreement_score":0.045081902,"about_ca_system_score_codex":0.00063624093,"about_ca_system_score_gemma":0.000410117,"threshold_uncertainty_score":0.08963901},"labels":[],"label_agreement":null},{"id":"W4411078801","doi":"10.1016/j.rse.2025.114842","title":"Hierarchy features attention network for tiny ship detection from SDGSAT-1 thermal infrared images","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Infrared Target Detection Methodologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Changchun Science and Technology Planning Project; Shanghai Rising-Star Program; Chinese Academy of Sciences; National Natural Science Foundation of China; Canadian Anesthesiologists' Society","keywords":"Remote sensing; Infrared; Thermal infrared; Hierarchy; Computer science; Thermal; Artificial intelligence; Environmental science; Computer vision; Geology; Meteorology; Optics; Physics","score_opus":0.013746481346863003,"score_gpt":0.23051010422139542,"score_spread":0.2167636228745324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411078801","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3812481,0.0016988515,0.59342873,0.00066853454,0.0005273437,0.000321613,0.0019783142,0.009859649,0.010268803],"genre_scores_gemma":[0.93007493,0.00026101913,0.05989594,0.00019963352,0.000105485895,0.000109075336,0.0020344383,0.000091287126,0.007228207],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983156,0.000013742418,0.0000068753357,0.000053858883,0.000047231213,0.000046758745],"domain_scores_gemma":[0.9998404,0.00003539777,0.000013073315,0.000018980609,0.000072900686,0.000019111314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027191598,0.000559239,0.00050002785,0.00073423446,0.0003908294,0.000315739,0.00081697555,0.0003927707,0.002513182],"category_scores_gemma":[0.0006423784,0.00017957832,0.00031609286,0.00050552737,0.00014733915,0.0005126567,0.0007328064,0.0004571409,0.000496004],"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":[0.0008075023,0.00042062407,0.0060006334,0.00012389415,0.000113078124,0.00020814798,0.000105488085,0.05606829,0.045922067,0.001718992,0.020561393,0.8679499],"study_design_scores_gemma":[0.000014055697,0.00010872143,0.0038188645,0.0000069780635,0.000033431104,0.000043296124,0.00003483008,0.98349124,0.009906259,0.001123774,0.0014072806,0.000011239679],"about_ca_topic_score_codex":0.017654566,"about_ca_topic_score_gemma":0.026619777,"teacher_disagreement_score":0.017654566,"about_ca_system_score_codex":0.0006794575,"about_ca_system_score_gemma":0.00059377804,"threshold_uncertainty_score":0.03510362},"labels":[],"label_agreement":null},{"id":"W4411256249","doi":"10.1016/j.rse.2025.114863","title":"Comparison of snowmelt timing estimates from Sentinel-1 SAR and surface observations in British Columbia, Canada","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":1,"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 Northern British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Snowmelt; Remote sensing; Environmental science; Geology; Climatology; Meteorology; Snow; Geography","score_opus":0.024834103686207396,"score_gpt":0.2226311080827629,"score_spread":0.1977970043965555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411256249","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98363113,0.0004682191,0.00081210025,0.00020667448,0.000019409039,0.000042767395,0.007963802,0.00009284454,0.0067629376],"genre_scores_gemma":[0.99130225,0.00028570212,0.0010383601,0.000062229396,0.0000039397764,0.000021481183,0.0047839917,0.000027962025,0.0024740766],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99956924,0.00003500238,0.000028395521,0.00011253343,0.00014279697,0.00011207953],"domain_scores_gemma":[0.99790764,0.00014054081,0.00010234581,0.00005173305,0.001667767,0.00013003257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052204117,0.00035965248,0.00025446698,0.0018529518,0.0013531052,0.0015243196,0.00068700116,0.0002531583,0.0016129913],"category_scores_gemma":[0.0019546454,0.0002207246,0.00021267433,0.003195361,0.00031817786,0.00032200335,0.00042145996,0.00030654017,0.00027166697],"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":[0.0002581388,0.000059746464,0.9312988,0.00015885357,0.00018172139,0.00025546472,0.0014325196,0.009037279,0.005111395,0.00041122062,0.0071090446,0.044685777],"study_design_scores_gemma":[0.000015949787,0.000009869533,0.9823743,0.00007945007,0.000042163112,0.000030319927,0.0015622603,0.011211658,0.0007406843,0.000047876267,0.0038533611,0.000032212694],"about_ca_topic_score_codex":0.9969104,"about_ca_topic_score_gemma":0.998801,"teacher_disagreement_score":0.01504205,"about_ca_system_score_codex":0.01504205,"about_ca_system_score_gemma":0.016377164,"threshold_uncertainty_score":0.10913825},"labels":[],"label_agreement":null},{"id":"W4412067699","doi":"10.1016/j.rse.2025.114883","title":"Seasonal dynamics of a coupled hillslope — river system in the Arctic revealed by semi-automated satellite image analysis","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Climate change and permafrost","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":"National Aeronautics and Space Administration","keywords":"Remote sensing; Satellite; Arctic; Satellite image; Satellite imagery; Geology; The arctic; Environmental science; Oceanography","score_opus":0.008511704258481665,"score_gpt":0.2105655873654852,"score_spread":0.20205388310700353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412067699","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.997486,0.00004225619,0.0012901005,0.000021014406,0.000003004062,0.000013228453,0.00045679163,0.000079282814,0.00060832285],"genre_scores_gemma":[0.9961423,0.00003784475,0.003205626,0.0000086062655,0.0000023525415,0.000009189884,0.0004212711,0.00000902445,0.00016380342],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998841,0.000015108606,0.0000061083474,0.000030922365,0.000038239486,0.000025540248],"domain_scores_gemma":[0.99969125,0.000044384295,0.0000684023,0.000020240383,0.00013645821,0.000039239894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032429423,0.00016515865,0.00019069567,0.00087735034,0.00037928636,0.00052035804,0.0002628531,0.00015726671,0.0003329269],"category_scores_gemma":[0.00060171547,0.00015271256,0.00016844609,0.0009615239,0.00022221627,0.00014242562,0.00023841119,0.00010544727,0.000067727095],"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":[0.00018035095,0.00008980031,0.8996303,0.00007841372,0.0002094691,0.00025080715,0.0008523804,0.024438888,0.03182648,0.00034878953,0.0011652181,0.040929113],"study_design_scores_gemma":[0.000005205931,0.000011906009,0.93417805,0.0000075593566,0.00002480945,0.000030428322,0.00024227497,0.06400326,0.0008794053,0.00007917641,0.0005253305,0.000012586685],"about_ca_topic_score_codex":0.36234677,"about_ca_topic_score_gemma":0.54049754,"teacher_disagreement_score":0.36234677,"about_ca_system_score_codex":0.00084388774,"about_ca_system_score_gemma":0.0011355841,"threshold_uncertainty_score":0.7204753},"labels":[],"label_agreement":null},{"id":"W4412564072","doi":"10.1016/j.rse.2025.114920","title":"Analytical modeling and correction of the ocean colour bidirectional reflectance across water types","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Marine and coastal ecosystems","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":"European Organization for the Exploitation of Meteorological Satellites; National Oceanic and Atmospheric Administration; Toronto Arts Council; European Commission; University of Miami","keywords":"Remote sensing; Reflectivity; Environmental science; Geology; Optics","score_opus":0.010071457272654334,"score_gpt":0.2134344801318208,"score_spread":0.20336302285916646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412564072","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06369912,0.00218394,0.92602086,0.0004715008,0.00021814484,0.00009034969,0.0012900229,0.0021185232,0.003907507],"genre_scores_gemma":[0.58213705,0.0037047472,0.39888015,0.00027377246,0.00024854866,0.0002469378,0.0044129095,0.0009426401,0.009153271],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996458,0.000055879653,0.00002040739,0.00012573667,0.000111014066,0.000041060288],"domain_scores_gemma":[0.99966145,0.00009834011,0.000041160198,0.000051247658,0.00013683258,0.000011027111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010033065,0.0010582897,0.0005207706,0.001267859,0.00032994538,0.0010709863,0.0013789436,0.00089685176,0.0011070321],"category_scores_gemma":[0.0018174257,0.00038049906,0.0017594418,0.0013249632,0.0003893384,0.0011399221,0.0007694407,0.0009867046,0.0011415237],"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":[0.000096308206,0.00011147916,0.00916912,0.0006135624,0.00024955723,0.00022955114,0.00031833866,0.58849585,0.031559642,0.009244125,0.00798903,0.35192338],"study_design_scores_gemma":[0.000009526983,0.000014607406,0.0029930894,0.00003103876,0.00003426649,0.00007092122,0.000038241073,0.9852196,0.0040672603,0.0020692523,0.005424138,0.000028009747],"about_ca_topic_score_codex":0.015292088,"about_ca_topic_score_gemma":0.009711141,"teacher_disagreement_score":0.015292088,"about_ca_system_score_codex":0.00064276974,"about_ca_system_score_gemma":0.0011357237,"threshold_uncertainty_score":0.030406117},"labels":[],"label_agreement":null},{"id":"W4413098971","doi":"10.1016/j.rse.2025.114907","title":"Methodological considerations for studying spectral-plant diversity relationships","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Ecology and Vegetation Dynamics Studies","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":true,"ca_institutions":"McGill University; University of British Columbia; National Research Council Canada; Université de Sherbrooke; Université du Québec à Montréal; Université de Montréal; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Council","keywords":"Remote sensing; Diversity (politics); Plant diversity; Computer science; Environmental science; Geography; Ecology; Biodiversity; Biology; Sociology","score_opus":0.16644922479152688,"score_gpt":0.3050696279380518,"score_spread":0.13862040314652493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413098971","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07616642,0.0031345617,0.8999975,0.004214992,0.0015950263,0.0045613516,0.0016150519,0.000327136,0.008388029],"genre_scores_gemma":[0.23508911,0.001141678,0.7461522,0.0024742575,0.0005513546,0.012142169,0.0008279493,0.00039717712,0.0012240394],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.78092414,0.1654225,0.019228747,0.011125917,0.022264183,0.0010345364],"domain_scores_gemma":[0.6438587,0.24499501,0.02523753,0.050470117,0.03394505,0.0014936697],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.20543782,0.0015904179,0.0015129225,0.003028123,0.0033846453,0.005537139,0.0043864995,0.0018444634,0.0017748813],"category_scores_gemma":[0.40697598,0.0009387996,0.0020673631,0.006409276,0.005730665,0.0031783625,0.0050851465,0.0038999727,0.0006347954],"study_design_candidate":"not_applicable","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":[0.0023901272,0.0012171236,0.2906674,0.012374329,0.007242571,0.0021978223,0.030923905,0.025399871,0.07187203,0.1431584,0.014466795,0.39808968],"study_design_scores_gemma":[0.0006329779,0.0032199274,0.36286995,0.0054823025,0.003186184,0.0020449113,0.016717575,0.05107452,0.044303916,0.34299332,0.16652185,0.00095266313],"about_ca_topic_score_codex":0.01001661,"about_ca_topic_score_gemma":0.01935345,"teacher_disagreement_score":0.20543782,"about_ca_system_score_codex":0.0020600313,"about_ca_system_score_gemma":0.0039885086,"threshold_uncertainty_score":0.97983664},"labels":[],"label_agreement":null},{"id":"W4413454206","doi":"10.1016/j.rse.2025.114962","title":"RSPECT: A PROSPECT-based model incorporating the real structure of rice leaves","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Leaf Properties and Growth Measurement","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Liaoning Revitalization Talents Program; Institut National de la Recherche Agronomique; Department of Education of Liaoning Province; National Natural Science Foundation of China; European Commission; University of Saskatchewan","keywords":"Remote sensing; Environmental science; Computer science; Geology","score_opus":0.016629459622569634,"score_gpt":0.19751328582867633,"score_spread":0.1808838262061067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413454206","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2325637,0.00042000532,0.741652,0.0008736016,0.00023208882,0.0001344006,0.0035290637,0.0065573445,0.0140377395],"genre_scores_gemma":[0.9292299,0.00017375866,0.06248693,0.00016690236,0.000056035955,0.00014960175,0.0012647253,0.0003340789,0.0061380337],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99992096,0.000021272048,0.0000036618123,0.00002543436,0.000016862467,0.000011840287],"domain_scores_gemma":[0.9997174,0.00013066953,0.000024652167,0.000029019553,0.00006758841,0.000030625746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033907575,0.0006090321,0.000639646,0.00029886715,0.00028113928,0.0006719076,0.0019234437,0.0013992977,0.0035336795],"category_scores_gemma":[0.0012261326,0.00049041543,0.00061813224,0.000411449,0.0003527566,0.0009901918,0.0005489243,0.0007462565,0.0007782065],"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":[0.00002394489,0.000010575763,0.0003079246,0.000011726951,0.000012231673,0.00001897641,0.0000036457786,0.9957288,0.000250057,0.0005765009,0.00036952962,0.0026860735],"study_design_scores_gemma":[0.000004340192,0.0000044460453,0.000048105434,8.9472036e-7,0.0000021517185,0.000003942984,5.8617064e-7,0.999435,0.00006246674,0.00030261016,0.0001326889,0.0000028442532],"about_ca_topic_score_codex":0.015831446,"about_ca_topic_score_gemma":0.010093535,"teacher_disagreement_score":0.015831446,"about_ca_system_score_codex":0.000514684,"about_ca_system_score_gemma":0.0010345505,"threshold_uncertainty_score":0.031478584},"labels":[],"label_agreement":null},{"id":"W4413463179","doi":"10.1016/j.rse.2025.114987","title":"A more precise retrieval of sun-induced chlorophyll fluorescence from satellite data using artificial neural networks","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Water Quality Monitoring and Analysis","field":"Environmental Science","cited_by":7,"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":"European Research Council; Office of Science; Horizon 2020 Framework Programme; National Natural Science Foundation of China; European Commission; U.S. Department of Energy","keywords":"Remote sensing; Satellite; Artificial neural network; Chlorophyll fluorescence; Fluorescence; Computer science; Environmental science; Chlorophyll a; Artificial intelligence; Geology; Physics; Optics; Astronomy; Botany; Biology","score_opus":0.05386053647804312,"score_gpt":0.2859945825167152,"score_spread":0.2321340460386721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413463179","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5461045,0.0010135715,0.44835502,0.00029306408,0.0000971343,0.00003093681,0.00056353473,0.0009021819,0.0026400909],"genre_scores_gemma":[0.9132878,0.0003007841,0.08493806,0.00006296558,0.000029915447,0.000026751844,0.0005263838,0.000025726335,0.0008016577],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998745,0.000017712026,0.000010788802,0.00004886438,0.000036142734,0.000011927056],"domain_scores_gemma":[0.9998735,0.000039445244,0.000025583831,0.000016975351,0.000040134524,0.0000042183806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036326598,0.00054361735,0.0003549576,0.0004239664,0.00015567314,0.00041682954,0.00027261453,0.0005567637,0.00031523276],"category_scores_gemma":[0.00073458266,0.00019776192,0.00041209257,0.00071020453,0.00011708357,0.00076429674,0.00023245577,0.00047272936,0.0001331427],"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":[0.00006552682,0.00008460035,0.007018457,0.00008293793,0.00007723885,0.00009517409,0.00004145839,0.8534086,0.04156522,0.0008959189,0.00053153594,0.09613329],"study_design_scores_gemma":[0.000002964973,0.0000061275405,0.001991023,0.000003870253,0.0000049538353,0.0000048126863,0.0000047844596,0.9947049,0.0028429467,0.00024247573,0.00018407426,0.0000069970965],"about_ca_topic_score_codex":0.010379831,"about_ca_topic_score_gemma":0.009487895,"teacher_disagreement_score":0.010379831,"about_ca_system_score_codex":0.0004717103,"about_ca_system_score_gemma":0.00034578206,"threshold_uncertainty_score":0.020638824},"labels":[],"label_agreement":null},{"id":"W4413851003","doi":"10.1016/j.rse.2025.114998","title":"Mitigating the black-soil problem in the reflectance-to-fluorescence (R2F) relationship: A soil-adjusted reflectance-based approach for downscaling SIF","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China; Deutsche Forschungsgemeinschaft","keywords":"Reflectivity; Downscaling; Remote sensing; Environmental science; Soil science; Geology; Optics; Physics; Climate change","score_opus":0.019533545010497463,"score_gpt":0.24818219284021317,"score_spread":0.22864864782971572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413851003","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14231706,0.00056450244,0.8496202,0.0003597552,0.00016643354,0.000100222045,0.00078833,0.003721665,0.002361893],"genre_scores_gemma":[0.30736408,0.0003931086,0.68788064,0.00017810572,0.00008159767,0.00009375046,0.0013739464,0.0008865638,0.001748226],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996069,0.000055698867,0.000026628011,0.0001315646,0.00012004562,0.00005926903],"domain_scores_gemma":[0.9994061,0.000121519566,0.00008609627,0.00010446032,0.00026238366,0.00001942691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001248393,0.0012064483,0.0007510941,0.0015588157,0.00058577105,0.00096292276,0.0012721883,0.0010042551,0.0018291144],"category_scores_gemma":[0.003194133,0.00056159677,0.00095840596,0.001572695,0.00034096657,0.0014096851,0.0008074209,0.001613957,0.00084067945],"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":[0.00027342615,0.00055378355,0.017126633,0.00036177685,0.0004343146,0.0001856099,0.00040018017,0.21269803,0.25668278,0.0047105844,0.007800036,0.4987729],"study_design_scores_gemma":[0.000034346962,0.0000534287,0.014282726,0.000022380966,0.000098659264,0.00006868138,0.00008409355,0.94695485,0.030665096,0.0017393748,0.005923925,0.00007247547],"about_ca_topic_score_codex":0.01863831,"about_ca_topic_score_gemma":0.037240274,"teacher_disagreement_score":0.01863831,"about_ca_system_score_codex":0.00053298596,"about_ca_system_score_gemma":0.0016041251,"threshold_uncertainty_score":0.037059665},"labels":[],"label_agreement":null},{"id":"W4415313268","doi":"10.1016/j.rse.2025.115087","title":"The next Landsat: Mission turning point?","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Rangeland and Wildlife 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":false,"ca_institutions":"Canadian Forest Service; Natural Resources Canada","funders":"","keywords":"Earth observation; Context (archaeology); Satellite; Earth observation satellite; Successor cardinal; Thermal infrared; Spectral bands; Radiometry","score_opus":0.010374722577548196,"score_gpt":0.2173151928024852,"score_spread":0.206940470224937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415313268","genre_codex":"other","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032475576,0.006463919,0.036231264,0.11928193,0.015220865,0.00059344486,0.040547866,0.021254277,0.72793096],"genre_scores_gemma":[0.23052105,0.008792986,0.13406314,0.046763986,0.0059122485,0.00045417502,0.12750268,0.009402149,0.4365877],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994733,0.00007743013,0.000016031865,0.00007446333,0.00023482928,0.00012393281],"domain_scores_gemma":[0.9991211,0.000036761565,0.000041044645,0.00009517708,0.0003451077,0.00036062617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016637627,0.0003200358,0.00014909616,0.000480725,0.0008169533,0.0023368697,0.00070837035,0.000948925,0.036803197],"category_scores_gemma":[0.001356438,0.00014210898,0.00018172435,0.00069055986,0.00045274096,0.0034190805,0.0011551396,0.0013339637,0.02433906],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000101072386,0.000045453824,0.0020966516,0.00010281631,0.0000045915576,0.00010171464,0.00022628294,0.00013965824,0.0025666722,0.017669247,0.8804232,0.096522555],"study_design_scores_gemma":[0.000007923609,0.000025005835,0.0019737133,0.000049759263,0.0000018001195,0.00006115622,0.0001662018,0.00020203479,0.00072331395,0.0020028588,0.99477416,0.000012042087],"about_ca_topic_score_codex":0.008784263,"about_ca_topic_score_gemma":0.014225529,"teacher_disagreement_score":0.036803197,"about_ca_system_score_codex":0.0009680239,"about_ca_system_score_gemma":0.001483762,"threshold_uncertainty_score":0.12311894},"labels":[],"label_agreement":null},{"id":"W4415427159","doi":"10.1016/j.rse.2025.115089","title":"A dual-band parametric method for angular normalization of land surface thermal radiation for single-angle thermal infrared image","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Natural Science Foundation of China","keywords":"Normalization (sociology); Radiative transfer; Radiation; Thermal; Zenith; Thermal radiation; Radiometer; Parametric statistics; Infrared; Brightness temperature","score_opus":0.01023744616845573,"score_gpt":0.24016897866004236,"score_spread":0.22993153249158663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415427159","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006281119,0.00010961332,0.99149114,0.00002744337,0.000043199852,0.000034439097,0.00013134019,0.0006608512,0.0012209377],"genre_scores_gemma":[0.083449125,0.00032644064,0.910667,0.000045000266,0.000041501833,0.00018914136,0.00085058925,0.00044567243,0.003985575],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947137,0.000086392814,0.00001978986,0.0001300349,0.00023229094,0.000060105995],"domain_scores_gemma":[0.99955934,0.00006885342,0.0000380029,0.0001144469,0.0001995013,0.000019802495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005680042,0.0007695778,0.00047648567,0.0009295639,0.00048324687,0.0008671981,0.001146101,0.00046167936,0.0048257266],"category_scores_gemma":[0.0012732345,0.0004478095,0.0007960621,0.0014634932,0.0003952832,0.0010662061,0.0010383555,0.0011847673,0.0025766373],"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":[0.0002571114,0.00017331517,0.001472509,0.00024417453,0.000099918165,0.000069081776,0.00018120886,0.024999097,0.27720925,0.010596308,0.0047258176,0.67997223],"study_design_scores_gemma":[0.000038659702,0.00013829343,0.0073686545,0.00003420728,0.00011178109,0.00048556976,0.0001304266,0.75646716,0.20172025,0.004264178,0.0290846,0.00015625735],"about_ca_topic_score_codex":0.0020587484,"about_ca_topic_score_gemma":0.004447668,"teacher_disagreement_score":0.0048257266,"about_ca_system_score_codex":0.0003395228,"about_ca_system_score_gemma":0.00091388455,"threshold_uncertainty_score":0.01614374},"labels":[],"label_agreement":null},{"id":"W4415703147","doi":"10.1016/j.rse.2025.115103","title":"Correcting angular effects on MODIS LST in urban areas using an enhanced time-evolving parametric geometric model","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":false,"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; University of Guelph","keywords":"Mean squared error; Inversion (geology); Parametric statistics; Anisotropy; Atmospheric model; Climate model; Vegetation (pathology); Lidar","score_opus":0.010627914106728566,"score_gpt":0.22487500196360183,"score_spread":0.21424708785687327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415703147","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49571553,0.00046596065,0.49603984,0.0003238698,0.00025406136,0.000045762474,0.0007273245,0.002717642,0.0037100357],"genre_scores_gemma":[0.92927015,0.00027695292,0.06794292,0.00005675044,0.000026405605,0.000026710906,0.0006002628,0.00048688264,0.0013129307],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999796,0.00003812266,0.000015014259,0.00005853616,0.00006202098,0.00003039554],"domain_scores_gemma":[0.99954224,0.00014321289,0.000057799756,0.00010867448,0.00013238018,0.000015698832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056771113,0.00052388303,0.00028595873,0.00043026515,0.00028365772,0.0005865758,0.00059694354,0.00046865965,0.0010104505],"category_scores_gemma":[0.0025150976,0.0002902909,0.0004957545,0.0011301279,0.0002780641,0.00074299803,0.00049804215,0.00053185,0.00034423615],"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":[0.000113363414,0.000034751934,0.0086636795,0.000053911408,0.00005463494,0.00009253652,0.00010227593,0.91899467,0.017688833,0.0014909649,0.00086312735,0.05184735],"study_design_scores_gemma":[0.0000063285124,0.000008350386,0.004045704,0.000004379938,0.000021153324,0.000024516647,0.000016520953,0.9894874,0.0053087836,0.00030405133,0.0007588568,0.000013978947],"about_ca_topic_score_codex":0.02372165,"about_ca_topic_score_gemma":0.023357056,"teacher_disagreement_score":0.02372165,"about_ca_system_score_codex":0.00064700475,"about_ca_system_score_gemma":0.0011395963,"threshold_uncertainty_score":0.047167122},"labels":[],"label_agreement":null},{"id":"W4415959079","doi":"10.1016/j.rse.2025.115113","title":"The relationship between spectral and integrated sun-induced chlorophyll fluorescence and its implication for photosynthesis estimation using fluorescence observations","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Oak Ridge National Laboratory; Biological and Environmental Research; Office of Science; Nanjing University; National Natural Science Foundation of China; Chinese Academy of Agricultural Sciences; U.S. Department of Energy","keywords":"Wavelength; Chlorophyll fluorescence; Fluorescence; Photosynthesis; Canopy; Absorption (acoustics); Photosynthetic efficiency; Chlorophyll; Analytical Chemistry (journal)","score_opus":0.04059766741339468,"score_gpt":0.2639888133965319,"score_spread":0.22339114598313722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415959079","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95887846,0.00064192095,0.037971005,0.00005605861,0.000018017048,0.000009708489,0.00021243923,0.000078491124,0.0021338484],"genre_scores_gemma":[0.9968508,0.000114945105,0.0026173377,0.000011203057,0.0000054552856,0.0000030316626,0.00009351265,0.0000049430178,0.0002987375],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998926,0.000016388149,0.000005376345,0.00004906929,0.000028581002,0.000007967121],"domain_scores_gemma":[0.9993007,0.0004903945,0.00006302713,0.000025578884,0.000100222474,0.000020058416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034517553,0.00015108082,0.00013595256,0.00023829099,0.00013022576,0.00025455168,0.00019773064,0.0003481481,0.00052308966],"category_scores_gemma":[0.0014147001,0.00016191391,0.00014526726,0.00037941386,0.00017198888,0.0006242986,0.00012523442,0.00023586467,0.00013883338],"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":[0.0005549793,0.0001836053,0.45832726,0.00025199357,0.00023078853,0.00032141298,0.00019758433,0.02532909,0.42434946,0.0015296446,0.00047332884,0.08825091],"study_design_scores_gemma":[0.00001126591,0.00014303302,0.80994606,0.000009121254,0.00007233382,0.0006532122,0.000074466996,0.14873716,0.03879727,0.0009286744,0.00060155895,0.000025855845],"about_ca_topic_score_codex":0.0018660271,"about_ca_topic_score_gemma":0.0024785402,"teacher_disagreement_score":0.0018660271,"about_ca_system_score_codex":0.00018170185,"about_ca_system_score_gemma":0.00014840695,"threshold_uncertainty_score":0.0037103891},"labels":[],"label_agreement":null},{"id":"W4415976534","doi":"10.1016/j.rse.2025.115110","title":"Automated drone-borne GPR mapping of root-zone soil moisture for precision irrigation","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Geophysical Methods and Applications","field":"Engineering","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":"Memorial University of Newfoundland","funders":"Fonds De La Recherche Scientifique - FNRS; Gouvernement Wallon","keywords":"Water content; Ground-penetrating radar; Reflectometry; Precision agriculture; Irrigation; Moisture; Radar; Soil water; Precipitation","score_opus":0.010124431461222159,"score_gpt":0.24283727020826956,"score_spread":0.2327128387470474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415976534","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8340243,0.00025681645,0.15959127,0.00010204809,0.000027789845,0.000063112166,0.0007035244,0.0015439185,0.0036871908],"genre_scores_gemma":[0.94815737,0.00012543038,0.050520014,0.000041833,0.000008137285,0.000030309695,0.0003418599,0.000055127843,0.00071985996],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99990237,0.000020881995,0.0000029551456,0.000025774267,0.00003251322,0.000015423166],"domain_scores_gemma":[0.99983513,0.000042475764,0.00002463965,0.00003983324,0.000048449157,0.000009415049],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018045076,0.00026144943,0.00021077952,0.00031099154,0.00008383727,0.00033641516,0.00032106135,0.00026189402,0.00093755795],"category_scores_gemma":[0.0003405821,0.00011429359,0.00013571049,0.00024157458,0.000108518856,0.0004667175,0.0003372586,0.00024501834,0.00040741308],"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":[0.00028277468,0.000105908206,0.018172283,0.00021678316,0.000043006676,0.00023621481,0.00032460847,0.026315123,0.6691152,0.00081959454,0.0024110815,0.28195754],"study_design_scores_gemma":[0.00013211498,0.0005527633,0.16896568,0.00008001326,0.00012974747,0.00072467356,0.00068999367,0.58884776,0.21479507,0.0017400859,0.023218904,0.00012320507],"about_ca_topic_score_codex":0.0011433123,"about_ca_topic_score_gemma":0.002873207,"teacher_disagreement_score":0.0011433123,"about_ca_system_score_codex":0.0001279639,"about_ca_system_score_gemma":0.00016630656,"threshold_uncertainty_score":0.0031363964},"labels":[],"label_agreement":null},{"id":"W4416039409","doi":"10.1016/j.rse.2025.115112","title":"Snow effects on altimeter waveforms over sea ice in the Weddell Sea — Part I: Radar waveform decomposition","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Arctic and Antarctic ice dynamics","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 Manitoba","funders":"National Key Research and Development Program of China; Norges Forskningsråd; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Snow; Altimeter; Waveform; Backscatter (email); Sea ice; Radar altimeter; Radar; Scattering; Firn","score_opus":0.006090346295017579,"score_gpt":0.20776423762563223,"score_spread":0.20167389133061464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416039409","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99360764,0.00012240522,0.0047056735,0.000057085505,0.000023259032,0.000010242777,0.00061511196,0.00014365233,0.0007150041],"genre_scores_gemma":[0.9936103,0.00012772183,0.003748539,0.000026662132,0.000014798698,0.000009782032,0.0019235357,0.000035508223,0.00050306675],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99993455,0.000008097695,0.0000056777235,0.000026188836,0.000012250767,0.00001312672],"domain_scores_gemma":[0.9998692,0.000040193074,0.000022441285,0.000025215779,0.000028183673,0.000014833972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002148742,0.00047188028,0.00016037966,0.0004353655,0.00016604782,0.0005256512,0.00024791126,0.00028981332,0.0009320342],"category_scores_gemma":[0.0006171004,0.00021459187,0.00040237283,0.0004196273,0.00018019666,0.00035006393,0.00032027691,0.00030235996,0.00020950363],"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":[0.0005341205,0.00025701092,0.32147124,0.00017260261,0.0002596945,0.0005869286,0.00034244583,0.4293575,0.08338291,0.0014915611,0.0026744015,0.15946966],"study_design_scores_gemma":[0.000032358395,0.000091067726,0.2161086,0.000035194586,0.000054417622,0.00013055679,0.0001289098,0.7684824,0.013295061,0.0004707695,0.0011452389,0.0000254357],"about_ca_topic_score_codex":0.010351076,"about_ca_topic_score_gemma":0.01042203,"teacher_disagreement_score":0.010351076,"about_ca_system_score_codex":0.00033356008,"about_ca_system_score_gemma":0.00031515886,"threshold_uncertainty_score":0.020581603},"labels":[],"label_agreement":null},{"id":"W4416347233","doi":"10.1016/j.rse.2025.115133","title":"On the sensitivity of SAR C- and L-band dual-polarized data for detection of early deforestation in the tropics","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ste. Anne's Hospital; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Australian Speleological Federation; Japan Aerospace Exploration Agency; National Aeronautics and Space Administration","keywords":"Deforestation (computer science); Radar; Backscatter (email); Vegetation (pathology); Tropics; Biomass (ecology); Sensitivity (control systems); Synthetic aperture radar","score_opus":0.013330985879466786,"score_gpt":0.22603541892882004,"score_spread":0.21270443304935324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416347233","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9971083,0.000265017,0.0015518646,0.00005607594,0.000008440266,0.000011402909,0.00022032076,0.00002450767,0.0007541656],"genre_scores_gemma":[0.9983193,0.00010048688,0.0011357461,0.00003495494,0.000009021568,0.0000050216186,0.000324423,0.0000058145392,0.00006526452],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99928206,0.0002117918,0.00005220926,0.00020155462,0.00014055692,0.00011181461],"domain_scores_gemma":[0.9957848,0.0027820594,0.0004417767,0.00033022667,0.00050943077,0.00015157931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024765036,0.00054154324,0.0002537235,0.0009771292,0.00020011513,0.00076220743,0.0003008919,0.00046873896,0.00032308916],"category_scores_gemma":[0.0058257785,0.000240801,0.00042908147,0.0006481107,0.0003740444,0.00071989646,0.0004751943,0.00043861658,0.00015801167],"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":[0.001140912,0.0002596323,0.73145914,0.00018035543,0.00039739793,0.00049324136,0.00054438994,0.1047585,0.1000197,0.0005891819,0.00056648074,0.05959107],"study_design_scores_gemma":[0.000032439388,0.00040607827,0.803533,0.000061780804,0.00014856346,0.00035587643,0.0005362887,0.17269774,0.0210729,0.0003753975,0.000715441,0.000064558655],"about_ca_topic_score_codex":0.0054617166,"about_ca_topic_score_gemma":0.004186617,"teacher_disagreement_score":0.0054617166,"about_ca_system_score_codex":0.00024691297,"about_ca_system_score_gemma":0.00020389847,"threshold_uncertainty_score":0.013097107},"labels":[],"label_agreement":null},{"id":"W4416525028","doi":"10.1016/j.rse.2025.115150","title":"FoScenes: A high-fidelity, large-scale 3D forest plant area density product derived from open-access airborne lidar data","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","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":"Toronto Metropolitan University","funders":"Hong Kong Polytechnic University; Systems Engineering Research Center; Research Grants Council, University Grants Committee; National Aeronautics and Space Administration","keywords":"Leaf area index; Lidar; Hyperspectral imaging; Mean squared error; Tree canopy; Deciduous; Canopy; Satellite; Laser scanning; Reference data","score_opus":0.0355781966462256,"score_gpt":0.2856855892972898,"score_spread":0.25010739265106424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416525028","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3931395,0.0005621857,0.25088584,0.00031634214,0.0001974574,0.00097608304,0.30026993,0.03703037,0.0166223],"genre_scores_gemma":[0.4274431,0.0004571308,0.2782938,0.00014958519,0.000044174096,0.00064555113,0.2858745,0.0022124965,0.004879688],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9997454,0.000015991636,0.00001402996,0.000057953373,0.00014406124,0.000022419428],"domain_scores_gemma":[0.9996654,0.000075342985,0.000042205505,0.00006788527,0.00012021727,0.000029030001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044516555,0.0008088821,0.00037768797,0.0011007414,0.0001999389,0.0006844361,0.0008675535,0.0004057762,0.005881359],"category_scores_gemma":[0.0011162705,0.0004423106,0.0004833153,0.00084775116,0.0002687105,0.0011023523,0.0006868757,0.0006287223,0.0018055686],"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":[0.0010874552,0.0005630995,0.09414569,0.0012680196,0.00060730043,0.0012072152,0.00093319203,0.17656086,0.1466353,0.0059351525,0.101399,0.46965775],"study_design_scores_gemma":[0.00028944825,0.00032432465,0.11250801,0.00017432848,0.00011023,0.00087768777,0.00043334547,0.5998243,0.09067365,0.0036971334,0.19080326,0.00028433424],"about_ca_topic_score_codex":0.0067798677,"about_ca_topic_score_gemma":0.016539032,"teacher_disagreement_score":0.0067798677,"about_ca_system_score_codex":0.00035499348,"about_ca_system_score_gemma":0.0005735272,"threshold_uncertainty_score":0.019675136},"labels":[],"label_agreement":null},{"id":"W4416812863","doi":"10.1016/j.rse.2025.115161","title":"Integrating prior information for improving 3D model-driven GAI estimation with application to wheat crops","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","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":"Natural Resources Canada","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Mean squared error; Multispectral image; Leaf area index; Hyperspectral imaging; Canopy; Atmospheric radiative transfer codes; Satellite; Inversion (geology); Key (lock)","score_opus":0.0037724218320667542,"score_gpt":0.20744104292194107,"score_spread":0.20366862108987432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416812863","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.566692,0.0002535145,0.42942888,0.0002497528,0.00002758461,0.00004237968,0.00044745344,0.0015090303,0.0013494687],"genre_scores_gemma":[0.94188523,0.000062327985,0.057000086,0.00003994671,0.000006277727,0.000024723158,0.00056913256,0.00006695323,0.0003454155],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998815,0.000037610796,0.000006991979,0.000035645815,0.000021588796,0.000016660368],"domain_scores_gemma":[0.9996507,0.00017914757,0.00003721124,0.000060706163,0.000057766567,0.000014409917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006351843,0.00054073526,0.00036275966,0.0003820427,0.00017962676,0.0004526704,0.00061768555,0.0005243981,0.00060814637],"category_scores_gemma":[0.0016624279,0.0004276555,0.00061350316,0.00036463005,0.00021402219,0.0007288661,0.00047343914,0.0006785529,0.0002196026],"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":[0.0000369146,0.00004766846,0.0030490134,0.000020982698,0.000026011283,0.000030834006,0.000027694361,0.9546382,0.008400691,0.0003213394,0.00020770908,0.033193078],"study_design_scores_gemma":[0.0000020239338,0.000006742677,0.0005576549,0.0000012447756,0.0000021117173,0.0000035784517,0.000003128002,0.99841106,0.00083257974,0.00010226998,0.00007425229,0.0000032745],"about_ca_topic_score_codex":0.009725683,"about_ca_topic_score_gemma":0.011092813,"teacher_disagreement_score":0.009725683,"about_ca_system_score_codex":0.00034655115,"about_ca_system_score_gemma":0.000585723,"threshold_uncertainty_score":0.019338131},"labels":[],"label_agreement":null},{"id":"W4417011317","doi":"10.1016/j.rse.2025.115175","title":"The role of cross-polarization in producing high-resolution pan-Arctic sea ice motion from the RADARSAT Constellation Mission over several years","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Sea ice; Synthetic aperture radar; Arctic ice pack; Constellation; Snow; Satellite; Buoy; Sea ice concentration; Arctic","score_opus":0.005411528162069522,"score_gpt":0.1990344280159212,"score_spread":0.19362289985385167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417011317","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92436653,0.0009496787,0.06467907,0.00024794528,0.00015751617,0.000045872705,0.0027393007,0.0006708149,0.0061433516],"genre_scores_gemma":[0.9413221,0.00063485594,0.050284386,0.00011825731,0.000119803364,0.000050258397,0.0058663054,0.00023591999,0.0013680592],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996985,0.00007440997,0.0000226803,0.000082336046,0.0000847853,0.00003735856],"domain_scores_gemma":[0.99919087,0.00021673334,0.00012093148,0.00015089603,0.00028535177,0.0000351778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016223822,0.00060953933,0.00016166891,0.0007939655,0.00021273477,0.0007079044,0.00023288124,0.00023708623,0.0006228743],"category_scores_gemma":[0.0023638064,0.00023273451,0.00033233868,0.0009837399,0.00012269014,0.00058721966,0.00051738217,0.0002729084,0.00030927017],"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":[0.0003433459,0.00010896104,0.50727683,0.00021568999,0.00043005627,0.00045523085,0.0004008246,0.0971589,0.055156667,0.000963031,0.0025562537,0.33493426],"study_design_scores_gemma":[0.00003114714,0.00023106419,0.7576828,0.000082325634,0.00030752918,0.00042570702,0.00016128048,0.18770581,0.03967811,0.0007566378,0.0128584895,0.00007909926],"about_ca_topic_score_codex":0.005465437,"about_ca_topic_score_gemma":0.0113557065,"teacher_disagreement_score":0.005465437,"about_ca_system_score_codex":0.00021082335,"about_ca_system_score_gemma":0.00030899962,"threshold_uncertainty_score":0.010867298},"labels":[],"label_agreement":null},{"id":"W4417120207","doi":"10.1016/j.rse.2025.115186","title":"S2Coast-2023: The first global 10-meter resolution coastline dataset derived from enhanced Sentinel-2 composite imagery using Google Earth Engine","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Coastal and Marine Dynamics","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Ministry of Science and Technology of the People's Republic of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; China Scholarship Council; Google","keywords":"Earth observation; Satellite; Satellite imagery; Image resolution; Segmentation; High resolution; Mean squared error; Root mean square","score_opus":0.009722873102636077,"score_gpt":0.20508322774072724,"score_spread":0.19536035463809115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417120207","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016155051,0.00008871113,0.00069286674,0.00007415139,0.000087488595,0.000047357516,0.97927856,0.001665634,0.0019101806],"genre_scores_gemma":[0.014547394,0.00005012161,0.0018225797,0.000058287875,0.000015660375,0.00007189524,0.98257357,0.00022322536,0.0006372318],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997559,0.000015841364,0.000021551075,0.000055041837,0.00008228324,0.00006943304],"domain_scores_gemma":[0.9991041,0.00003872707,0.0000991481,0.0001703869,0.0004136134,0.00017402311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033666205,0.0011820433,0.00072870567,0.0020913968,0.00040533263,0.00073322834,0.0012649507,0.00083726825,0.012465398],"category_scores_gemma":[0.0011396566,0.00039206495,0.0008744135,0.003458631,0.00026159955,0.00081959774,0.00090379425,0.0008081768,0.0108687505],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058104616,0.00027601857,0.03200148,0.0006613611,0.00035294093,0.00035003753,0.00019071119,0.009290064,0.010338914,0.00081191276,0.91715676,0.0279888],"study_design_scores_gemma":[0.0009852474,0.00012205863,0.3047426,0.00031840394,0.0001727056,0.00038148253,0.0007001324,0.034431793,0.007392623,0.0016455576,0.64881825,0.000289188],"about_ca_topic_score_codex":0.12702571,"about_ca_topic_score_gemma":0.17484449,"teacher_disagreement_score":0.12702571,"about_ca_system_score_codex":0.00079554226,"about_ca_system_score_gemma":0.0024290888,"threshold_uncertainty_score":0.25257272},"labels":[],"label_agreement":null},{"id":"W4417226237","doi":"10.1016/j.rse.2025.115176","title":"Estimation of sea surface foam coverage and effective foam layer thickness from satellite microwave measurements","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Ocean Waves and Remote Sensing","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"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; Second Institute of Oceanography, State Oceanic Administration; State Key Laboratory of Satellite Ocean Environment Dynamics; Natural Science Foundation of Zhejiang Province; Ontario Ministry of Natural Resources and Forestry; National Aeronautics and Space Administration","keywords":"Emissivity; Microwave; Satellite; Layer (electronics); Wind speed; Brightness temperature; Surface layer; Scatterometer","score_opus":0.011944032908766722,"score_gpt":0.21087303487086656,"score_spread":0.19892900196209984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417226237","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9800933,0.00015405286,0.018270427,0.00001358614,0.0000037234831,0.000007290602,0.00060651446,0.00018821548,0.0006627551],"genre_scores_gemma":[0.99237424,0.00005539958,0.0070438283,0.0000027441852,0.0000041879157,0.000005089832,0.00039757945,0.000011843947,0.0001050726],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99990416,0.000016969689,0.0000055196483,0.00002474903,0.000029205286,0.000019408903],"domain_scores_gemma":[0.99958473,0.00018994794,0.000052027277,0.000045604767,0.00010108051,0.000026576683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022375085,0.00033412818,0.00028855444,0.0014096381,0.00012666527,0.0002960381,0.00027482572,0.0003659912,0.0005999857],"category_scores_gemma":[0.001327124,0.00020333931,0.00028574944,0.00067092926,0.0001029363,0.00045996596,0.00027494336,0.00014701986,0.0002451076],"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":[0.0012482591,0.00019788164,0.4485566,0.00023420264,0.00030210346,0.00036568398,0.00020151827,0.15395743,0.16110696,0.0005712004,0.00078481354,0.2324733],"study_design_scores_gemma":[0.000047519683,0.0001248892,0.36735815,0.000022611868,0.00010398156,0.00017769792,0.00009126037,0.6048914,0.026162611,0.00044697718,0.00053160684,0.000041249605],"about_ca_topic_score_codex":0.0041791787,"about_ca_topic_score_gemma":0.0036370372,"teacher_disagreement_score":0.0041791787,"about_ca_system_score_codex":0.00016882234,"about_ca_system_score_gemma":0.00016262395,"threshold_uncertainty_score":0.008309722},"labels":[],"label_agreement":null},{"id":"W4417334985","doi":"10.1016/j.rse.2025.115158","title":"A review of forward modelling and retrieval approaches for forest soil moisture and vegetation optical depth using L-band radiometry","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Soil Moisture and Remote Sensing","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":"University of Guelph; Université du Québec à Trois-Rivières","funders":"Nuclear Safety and Security Commission; European Space Agency; California Institute of Technology; Jet Propulsion Laboratory; National Aeronautics and Space Administration","keywords":"Radiometry; Vegetation (pathology); Water content; Moisture; Reflectivity; Thematic Mapper; Radiometer","score_opus":0.028846209565579116,"score_gpt":0.2460759429226931,"score_spread":0.21722973335711399,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417334985","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004794443,0.8468683,0.1388743,0.00079453725,0.0007588906,0.000091118694,0.0015598326,0.0006809574,0.0055776658],"genre_scores_gemma":[0.024561973,0.90410626,0.06527295,0.00042333742,0.0007101738,0.00014794462,0.002202177,0.0001915289,0.0023836237],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994973,0.000083784085,0.00008120678,0.0001325086,0.0001769158,0.000028142787],"domain_scores_gemma":[0.9985536,0.0008341742,0.0001327,0.00006853179,0.00038857834,0.00002243218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014569133,0.0017585083,0.00139262,0.0024807847,0.0002952061,0.0016766686,0.0017449843,0.0012254571,0.0028028216],"category_scores_gemma":[0.0026721405,0.0007993375,0.0021908123,0.0033035667,0.00034933933,0.0022960652,0.00064860657,0.0010280659,0.0022662254],"study_design_candidate":"not_applicable","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":[0.000093525894,0.00007253061,0.0025239147,0.028601224,0.00053165253,0.00022013775,0.00019349245,0.034255274,0.00850181,0.0074622678,0.017595248,0.89994895],"study_design_scores_gemma":[0.00006017048,0.00041949362,0.0113700945,0.01664615,0.0022277692,0.0012447351,0.00036523846,0.12898512,0.012305128,0.017363144,0.8084973,0.00051567436],"about_ca_topic_score_codex":0.0049301963,"about_ca_topic_score_gemma":0.0035705785,"teacher_disagreement_score":0.0049301963,"about_ca_system_score_codex":0.0004584477,"about_ca_system_score_gemma":0.0012413077,"threshold_uncertainty_score":0.009802997},"labels":[],"label_agreement":null},{"id":"W590735017","doi":"10.1016/j.rse.2015.05.016","title":"Estimating long-term PM2.5 concentrations in China using satellite-based aerosol optical depth and a chemical transport model","year":2015,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":286,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Dalhousie University; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Collaborative Innovation Center for Regional Environmental Quality; National Natural Science Foundation of China; National Aeronautics and Space Administration","keywords":"Environmental science; Satellite; Aerosol; Chemical transport model; Particulates; Remote sensing; Lidar; Air quality index; Population; Meteorology; Atmospheric sciences; Geography; Environmental health; Geology","score_opus":0.0632619667945429,"score_gpt":0.3038773551882903,"score_spread":0.24061538839374738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W590735017","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99796224,0.00012589246,0.0012829213,0.000048590653,0.000006027815,0.0000065945064,0.00025122787,0.000054334163,0.00026210782],"genre_scores_gemma":[0.99833906,0.00008988087,0.0008457183,0.000008180176,0.0000049266228,0.000007173131,0.00046429483,0.0000047020294,0.00023611565],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998267,0.00002136967,0.000015102438,0.00006291848,0.000031911517,0.000041917025],"domain_scores_gemma":[0.9996635,0.00010305288,0.00005243086,0.000030818424,0.00009942571,0.000050810897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007709325,0.0011422738,0.0005071442,0.0010552965,0.00059376704,0.0006710789,0.0008191039,0.0008436772,0.00035532276],"category_scores_gemma":[0.00075894914,0.0007291367,0.0009295669,0.0009206056,0.00032130175,0.0008929307,0.00045098705,0.00028289208,0.00009295489],"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":[0.00020960541,0.00021616418,0.18762901,0.000114938885,0.00036711895,0.00023821228,0.00009681926,0.78238434,0.00831297,0.00040087223,0.0004948599,0.019535156],"study_design_scores_gemma":[0.00003668631,0.000041445193,0.09056161,0.000006462673,0.00010968166,0.000014580648,0.00005246718,0.9072558,0.0015624353,0.00015758682,0.00017482124,0.000026464635],"about_ca_topic_score_codex":0.32764396,"about_ca_topic_score_gemma":0.23078829,"teacher_disagreement_score":0.32764396,"about_ca_system_score_codex":0.0031688018,"about_ca_system_score_gemma":0.0026353325,"threshold_uncertainty_score":0.65147376},"labels":[],"label_agreement":null},{"id":"W627777417","doi":"10.1016/j.rse.2015.06.004","title":"Far-red sun-induced chlorophyll fluorescence shows ecosystem-specific relationships to gross primary production: An assessment based on observational and modeling approaches","year":2015,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":367,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Eidgenössische Technische Hochschule Zürich; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; European Space Agency","keywords":"Primary production; Eddy covariance; Environmental science; Remote sensing; Photosynthetically active radiation; Atmospheric sciences; Ecosystem; Chlorophyll fluorescence; Terrestrial ecosystem; Carbon cycle; Vegetation (pathology); Chlorophyll; Photosynthesis; Ecology; Botany; Physics; Geography; Biology","score_opus":0.15673239533566655,"score_gpt":0.2661405547846063,"score_spread":0.10940815944893975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W627777417","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9974421,0.00006805571,0.0018766177,0.0000148793615,0.000001802247,0.0000040130794,0.00008156247,0.000019956373,0.00049101195],"genre_scores_gemma":[0.9992192,0.000043336404,0.00058029906,0.0000037093316,0.0000014267753,0.0000021921903,0.00007688447,0.000003740172,0.00006915489],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999845,0.00004708591,0.000010407131,0.000047417794,0.000034177832,0.000015921232],"domain_scores_gemma":[0.999551,0.0002336118,0.00006946304,0.000064151594,0.000057247995,0.000024609968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007864045,0.00032741166,0.00023513603,0.00027497904,0.00017568575,0.0003571032,0.00021258532,0.00037534322,0.0003210023],"category_scores_gemma":[0.0009571913,0.00024671905,0.00047634216,0.00026115746,0.00021189141,0.0007719499,0.00019819647,0.00015492193,0.00007587682],"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":[0.00045364015,0.00028600675,0.81005156,0.00009173887,0.00041847563,0.00008936913,0.00018108152,0.086334296,0.071506925,0.0006890054,0.00023857535,0.029659301],"study_design_scores_gemma":[0.000014333711,0.00008855441,0.8546536,0.0000043221917,0.00007554629,0.00006255557,0.0000662544,0.13721824,0.00730676,0.00027969206,0.00020887604,0.00002124088],"about_ca_topic_score_codex":0.007929572,"about_ca_topic_score_gemma":0.012086571,"teacher_disagreement_score":0.007929572,"about_ca_system_score_codex":0.00054454064,"about_ca_system_score_gemma":0.00025499403,"threshold_uncertainty_score":0.0157668},"labels":[],"label_agreement":null},{"id":"W7107878600","doi":"10.1016/j.rse.2025.115154","title":"Nonlinear impacts of urban size and vegetation cover on global surface urban heat: Insights from 6022 cities","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Bundesamt für Energie; University of Illinois at Urbana-Champaign; National Natural Science Foundation of China; National Science Foundation","keywords":"Urbanization; Urban heat island; Vegetation (pathology); Urban climate; Vegetation cover; Urban planning; Climate change; Urban area","score_opus":0.005621813256490085,"score_gpt":0.20452216075902177,"score_spread":0.1989003475025317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7107878600","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99874735,0.000026044187,0.00013934037,0.000044606666,7.6361675e-7,0.0000024156511,0.00034969332,0.000004558975,0.00068531005],"genre_scores_gemma":[0.99876535,0.000061607294,0.00009482362,0.000009083706,0.0000015127631,0.0000049818955,0.0005633371,0.0000075974904,0.00049176416],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997793,0.00007037714,0.000007694586,0.00004056683,0.000029754088,0.00007229006],"domain_scores_gemma":[0.99926573,0.00037051347,0.00008513207,0.000107290645,0.00010485204,0.00006650401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004239997,0.00040391024,0.00035608982,0.00047115976,0.00049864757,0.0010200619,0.00044684732,0.00041172266,0.0020952297],"category_scores_gemma":[0.0011248421,0.0003522866,0.0007012188,0.0017189336,0.00057996344,0.00060744944,0.00083313737,0.00042774784,0.00034367444],"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":[0.00044950645,0.00040506717,0.8812027,0.00007438423,0.00035129488,0.0004195339,0.0012334686,0.09549362,0.0030712467,0.0021238055,0.0018948478,0.01328054],"study_design_scores_gemma":[0.000024998826,0.000082730796,0.94447595,0.000010110626,0.000114805036,0.000051598257,0.002426835,0.049853165,0.00067213306,0.000828343,0.0014261424,0.000033119206],"about_ca_topic_score_codex":0.13409494,"about_ca_topic_score_gemma":0.21599917,"teacher_disagreement_score":0.13409494,"about_ca_system_score_codex":0.0010769976,"about_ca_system_score_gemma":0.0007213964,"threshold_uncertainty_score":0.26662886},"labels":[],"label_agreement":null},{"id":"W7117317895","doi":"10.1016/j.rse.2025.115219","title":"A global intercomparison of SWOT and traditional nadir radar altimetry for monitoring river water surface elevation","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"China Scholarship Council; Centre National d’Etudes Spatiales; National Natural Science Foundation of China","keywords":"Elevation (ballistics); SWOT analysis; Altimeter; Ocean surface topography; Satellite; Synthetic aperture radar; Radar; Interferometric synthetic aperture radar","score_opus":0.015428078918697297,"score_gpt":0.2454576379908763,"score_spread":0.230029559072179,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117317895","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97054,0.00041684098,0.014677156,0.00044628864,0.00035246174,0.00011054913,0.007116841,0.00071001914,0.0056298818],"genre_scores_gemma":[0.9427053,0.00024487055,0.041568227,0.0001931251,0.000041851436,0.00006835263,0.013139873,0.00015898055,0.0018794903],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9996406,0.000081306935,0.000024391964,0.00009553028,0.000116668576,0.00004148366],"domain_scores_gemma":[0.999106,0.00011206327,0.00008542496,0.00018641453,0.00043511816,0.000075036376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022312934,0.0005648311,0.00037431446,0.00061464944,0.00033489233,0.0007144058,0.00052335224,0.0005258547,0.0008289705],"category_scores_gemma":[0.0009943197,0.00023551141,0.00046271557,0.0012179693,0.0002670481,0.0011015135,0.00056956604,0.00026672162,0.00035291942],"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":[0.0027192049,0.0011965391,0.38476074,0.00033572127,0.0010053625,0.0003537091,0.0008269323,0.0683438,0.12722419,0.002631479,0.022587374,0.38801497],"study_design_scores_gemma":[0.00039958287,0.0007521226,0.84651464,0.00006866746,0.0007210087,0.0001480943,0.0006211403,0.09721604,0.02540586,0.0011605674,0.026880877,0.00011140814],"about_ca_topic_score_codex":0.026734067,"about_ca_topic_score_gemma":0.048852827,"teacher_disagreement_score":0.026734067,"about_ca_system_score_codex":0.0005498504,"about_ca_system_score_gemma":0.0014133024,"threshold_uncertainty_score":0.053156912},"labels":[],"label_agreement":null},{"id":"W7125608048","doi":"10.1016/j.rse.2025.115018","title":"3DLCDM: Hybrid supervision for land cover discovery mapping of emerging urban structures in 3D remote sensing","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","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":true,"ca_institutions":"University of Waterloo","funders":"China Scholarship Council","keywords":"Land cover; Remote sensing application; Cover (algebra); Earth remote sensing; Land use","score_opus":0.009594561061901305,"score_gpt":0.23212725754909372,"score_spread":0.22253269648719243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125608048","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.068190776,0.00061575236,0.8788593,0.00034429439,0.0000970738,0.0002761867,0.006105386,0.043190997,0.0023202524],"genre_scores_gemma":[0.41974044,0.00021655786,0.5615416,0.00038134473,0.000055310615,0.00027313805,0.015050449,0.0005871272,0.0021540287],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9995177,0.00006457753,0.000017764582,0.00019208009,0.00015564618,0.00005235197],"domain_scores_gemma":[0.9996536,0.000084357176,0.000038705894,0.000094590505,0.00009407672,0.00003472918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006375706,0.0011417358,0.0007652307,0.0011397233,0.00040731655,0.0006578756,0.0017219383,0.0007277788,0.001576549],"category_scores_gemma":[0.0015281535,0.00043234104,0.0008246133,0.0006579025,0.00042547696,0.00097212475,0.0017947109,0.00094843423,0.0007801734],"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":[0.0004911021,0.00045903615,0.026980055,0.00050153857,0.00034988398,0.0004105613,0.00053835724,0.11274908,0.03320305,0.0024311605,0.052938756,0.7689474],"study_design_scores_gemma":[0.000030167708,0.00003077357,0.0038771606,0.00001892115,0.000021145644,0.00009576723,0.00007546319,0.9776679,0.009842764,0.0024974241,0.005824172,0.000018423414],"about_ca_topic_score_codex":0.015745236,"about_ca_topic_score_gemma":0.033013795,"teacher_disagreement_score":0.015745236,"about_ca_system_score_codex":0.0005465723,"about_ca_system_score_gemma":0.0008488767,"threshold_uncertainty_score":0.03130716},"labels":[],"label_agreement":null},{"id":"W766286664","doi":"10.1016/j.rse.2015.06.017","title":"Spatio-temporal sensitivity of MODIS land surface temperature anomalies indicates high potential for large-scale land cover change detection in Arctic permafrost landscapes","year":2015,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":70,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada; Helmholtz-Gemeinschaft; Heinrich Böll Stiftung","keywords":"Tundra; Environmental science; Permafrost; Arctic; Climatology; Snow; Land cover; Climate change; Anomaly (physics); Physical geography; Remote sensing; Atmospheric sciences; Land use; Geology; Meteorology; Geography","score_opus":0.020697155211652135,"score_gpt":0.21156726581087884,"score_spread":0.1908701105992267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W766286664","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9966383,0.0001662107,0.0012183747,0.000068145506,0.000008338693,0.000007899941,0.00073873356,0.00003693354,0.0011170533],"genre_scores_gemma":[0.9988533,0.0000395424,0.000584839,0.000016182825,0.000005132496,0.0000036329272,0.00038131254,0.000005299971,0.00011072419],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99973446,0.000065250846,0.000019643812,0.00008458789,0.000043114156,0.000052860047],"domain_scores_gemma":[0.9984327,0.0009282367,0.00020069233,0.00013508405,0.00021135445,0.00009195956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00125744,0.00017734747,0.00023242852,0.0006873099,0.00048354312,0.0009364252,0.000211703,0.00035217428,0.0011819314],"category_scores_gemma":[0.0024000348,0.00021491254,0.00035845168,0.0005750763,0.00030005682,0.0005793538,0.00037144937,0.00021087842,0.00016758418],"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":[0.00028436704,0.00008386911,0.9482343,0.00008082616,0.00016338797,0.00014894574,0.00025317565,0.008756801,0.020988168,0.00016205806,0.00025365804,0.020590398],"study_design_scores_gemma":[0.0000038018252,0.00002810688,0.98936427,0.00000786215,0.000027308244,0.00009239202,0.00015179953,0.008191876,0.0016871721,0.00012310605,0.0003153135,0.000006997757],"about_ca_topic_score_codex":0.024499053,"about_ca_topic_score_gemma":0.04411787,"teacher_disagreement_score":0.024499053,"about_ca_system_score_codex":0.00049563654,"about_ca_system_score_gemma":0.0003482678,"threshold_uncertainty_score":0.04871291},"labels":[],"label_agreement":null}]}