{"meta":{"query_hash":"90edaad7179a","filters":{"venue":"International Journal of Remote Sensing"},"cohort_total":232,"direct_labels_cover":1,"predictions_cover":232,"exported":232,"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/90edaad7179a","api":"https://metacan.xera.ac/api/v1/cohort?venue=International+Journal+of+Remote+Sensing"},"results":[{"id":"W1564320201","doi":"10.1080/01431161.2015.1047991","title":"Comparison of data gap-filling methods for Landsat ETM+ SLC-off imagery for monitoring forest degradation in a semi-deciduous tropical forest in Mexico","year":2015,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trent University","funders":"","keywords":"Thematic Mapper; Remote sensing; Land cover; Cohen's kappa; Multispectral pattern recognition; Endmember; Pixel; Multispectral image; Environmental science; Computer science; Satellite imagery; Statistics; Mathematics; Geography; Land use; Hyperspectral imaging; Artificial intelligence","score_opus":0.1123455973542292,"score_gpt":0.4131225587353059,"score_spread":0.3007769613810767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1564320201","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55477816,0.00013017011,0.443504,0.00047446074,0.00082459144,0.00018898303,0.00000650237,0.0000061773635,0.000086939035],"genre_scores_gemma":[0.52991545,0.000020986401,0.46972987,0.00001725768,0.00026855085,4.5923944e-8,0.000021958926,0.000013830864,0.000012059647],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977406,0.00014606635,0.0009759688,0.00028247875,0.0005962325,0.00025862028],"domain_scores_gemma":[0.99798554,0.00071339624,0.00071838923,0.00022153486,0.00025606202,0.000105054605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014479938,0.00017311923,0.00043122505,0.00022550323,0.00003480781,0.00007226265,0.0005338004,0.0001251756,0.0000014384867],"category_scores_gemma":[0.0017160642,0.00014548076,0.0001155258,0.00021141619,0.000070073474,0.00058711314,0.00019390727,0.000310471,0.0000011776599],"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":[0.0005808323,0.00010086546,0.276296,0.000018220602,0.000060827737,0.000044878157,0.0011045737,0.10776075,0.027128447,0.00000855113,0.0006325184,0.58626354],"study_design_scores_gemma":[0.002217319,0.00015130735,0.07097174,0.00058718247,0.00003847227,0.00029320174,0.00083796383,0.9056926,0.013215869,0.0024656388,0.0033083605,0.00022039436],"about_ca_topic_score_codex":0.00027303243,"about_ca_topic_score_gemma":0.0014642116,"teacher_disagreement_score":0.7979318,"about_ca_system_score_codex":0.0005751199,"about_ca_system_score_gemma":0.00005500027,"threshold_uncertainty_score":0.5932533},"labels":[],"label_agreement":null},{"id":"W1593276654","doi":"10.1080/01431161.2014.887236","title":"Georeferencing of UK DMC stereo-images without ground control points by exploiting geometric distortions","year":2014,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Satellite Image Processing and Photogrammetry","field":"Engineering","cited_by":10,"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":"Memorial University of Newfoundland; University of Surrey; European Space Agency","keywords":"Georeference; Computer vision; Artificial intelligence; Computer science; Stereo image; Remote sensing; Computer graphics (images); Image (mathematics); Geology; Geography","score_opus":0.010598624770695148,"score_gpt":0.23964362084650123,"score_spread":0.22904499607580608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1593276654","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39439943,0.0005735257,0.6032016,0.000072271876,0.0007183737,0.000027182397,0.000008872264,0.000032095486,0.00096659746],"genre_scores_gemma":[0.9611793,0.000094539806,0.03822381,0.000077711455,0.0003565565,2.6900734e-8,0.000006393397,0.00003322039,0.000028439516],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843395,0.000055627996,0.00067233905,0.000114320515,0.00052055134,0.00020322288],"domain_scores_gemma":[0.9984394,0.00027379644,0.0004399013,0.00011148648,0.0006401442,0.000095320596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005646971,0.00016341367,0.00031408307,0.00056073314,0.00005067655,0.000096893025,0.00022888625,0.00006364153,0.000013657979],"category_scores_gemma":[0.00047383082,0.00015664179,0.00013914696,0.0002452101,0.000051170984,0.00024368784,0.00002207771,0.0003132661,0.0000037191392],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060808026,0.000021608546,0.0020270993,0.00006439961,0.00034684004,0.00002503927,0.00032415587,0.0027669622,0.08719626,0.0000066348334,0.000336673,0.9068235],"study_design_scores_gemma":[0.0072569195,0.00038212293,0.008884882,0.004029999,0.00050028745,0.0028775856,0.0015835591,0.5792075,0.37419555,0.0042303544,0.015425657,0.0014256021],"about_ca_topic_score_codex":0.000102097656,"about_ca_topic_score_gemma":0.0000067850933,"teacher_disagreement_score":0.9053979,"about_ca_system_score_codex":0.00014483432,"about_ca_system_score_gemma":0.000023354,"threshold_uncertainty_score":0.63876665},"labels":[],"label_agreement":null},{"id":"W1604578072","doi":"10.1080/01431161.2015.1055605","title":"Detection of annual burned forest area using change metrics constructed from MODIS data in Manitoba, Canada","year":2015,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":3,"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 Oceanic and Atmospheric Administration; National Natural Science Foundation of China; National Key Research and Development Program of China; National Aeronautics and Space Administration","keywords":"Moderate-resolution imaging spectroradiometer; Thematic Mapper; Remote sensing; Environmental science; Change detection; Spectroradiometer; Climate change; Physical geography; Satellite imagery; Meteorology; Satellite; Geography; Reflectivity","score_opus":0.06303114666417649,"score_gpt":0.2561436200154829,"score_spread":0.19311247335130644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1604578072","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9851103,0.00006593667,0.01268785,0.00008794383,0.0017874525,0.00008548861,0.00009397254,0.0000036100262,0.00007747982],"genre_scores_gemma":[0.9876957,0.0000048434954,0.011996222,0.000041312236,0.00023633729,9.421012e-9,0.000013555142,0.000010550858,0.00000147479],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99822915,0.00009389327,0.00046971708,0.00015111784,0.0009266594,0.0001294622],"domain_scores_gemma":[0.9988117,0.00013248847,0.0005897486,0.0001841353,0.00018473138,0.00009719976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050053396,0.0000998069,0.00020410758,0.00019131835,0.000017998222,0.00002566325,0.0004053458,0.00005117206,0.000009133823],"category_scores_gemma":[0.00058228767,0.00009649113,0.000028622699,0.00026286903,0.000046059835,0.00053425756,0.00020743837,0.00016433313,0.0000016013647],"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":[0.0004224715,0.00005718459,0.24852803,0.000012980808,0.00023563574,0.0015978157,0.0010756544,0.009025294,0.05092455,9.765308e-7,0.00043960224,0.6876798],"study_design_scores_gemma":[0.0009654126,0.000046596422,0.06456525,0.00022286187,0.000026349591,0.0007531908,0.0010700985,0.92690796,0.0046660663,0.00013577007,0.00050400675,0.00013641098],"about_ca_topic_score_codex":0.8945344,"about_ca_topic_score_gemma":0.87450874,"teacher_disagreement_score":0.9178827,"about_ca_system_score_codex":0.0013015504,"about_ca_system_score_gemma":0.00012281953,"threshold_uncertainty_score":0.3934794},"labels":[],"label_agreement":null},{"id":"W1964175518","doi":"10.1080/01431160110076153","title":"S-Space: A new concept for information extraction from imaging spectrometer data","year":2002,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":4,"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; North Dakota State University","keywords":"Full spectral imaging; Spectral space; Hyperspectral imaging; Remote sensing; Imaging spectrometer; Land cover; Pixel; Curse of dimensionality; Computer science; Imaging spectroscopy; Variogram; Spectral imaging; Image resolution; Principal component analysis; Spectral resolution; Spatial analysis; Spatial dependence; Basis (linear algebra); Pattern recognition (psychology); Artificial intelligence; Spectrometer; Mathematics; Spectral line; Geology; Kriging; Physics; Optics; Land use; Statistics; Geometry","score_opus":0.03491766156629219,"score_gpt":0.2802327638009182,"score_spread":0.24531510223462602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964175518","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013291187,0.00032850163,0.9791978,0.0027832978,0.0032685604,0.000097397904,0.000031644417,0.00007769187,0.00092394365],"genre_scores_gemma":[0.52159977,0.0001460208,0.47601122,0.00019793856,0.0018279306,9.093135e-9,0.00011481962,0.0000314808,0.00007082518],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873716,0.000020265896,0.0005493774,0.00011510366,0.00042436892,0.00015371823],"domain_scores_gemma":[0.9986764,0.00016080322,0.000357773,0.0002703972,0.0004522448,0.00008234038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018976447,0.0001363365,0.00016571335,0.00030736547,0.00003660718,0.00027432063,0.00032486068,0.000053737298,0.000034545683],"category_scores_gemma":[0.000319161,0.00014288384,0.00008493062,0.000097416785,0.000024710178,0.0026497396,0.000034837605,0.00023057933,0.000030796444],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024130537,0.0000042121483,0.0000033919057,0.0000038136204,0.000109356275,0.000020216243,0.00043102234,0.0019778223,0.04266154,0.000009738776,0.019086558,0.9356682],"study_design_scores_gemma":[0.00070717256,0.000012389869,0.00017928441,0.00012547353,0.000039170434,0.00047875298,0.000111234294,0.89294076,0.010777114,0.0005054385,0.09399027,0.00013294104],"about_ca_topic_score_codex":0.00005655082,"about_ca_topic_score_gemma":0.000005513624,"teacher_disagreement_score":0.93553525,"about_ca_system_score_codex":0.0002677097,"about_ca_system_score_gemma":0.000026254971,"threshold_uncertainty_score":0.5826634},"labels":[],"label_agreement":null},{"id":"W1965362766","doi":"10.1080/01431161.2012.700133","title":"Multi-temporal RADARSAT-2 polarimetric SAR data for urban land-cover classification using an object-based support vector machine and a rule-based approach","year":2012,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":124,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Canadian Space Agency","keywords":"Support vector machine; Land cover; Remote sensing; Synthetic aperture radar; Polarimetry; Computer science; Cohen's kappa; Contextual image classification; Pattern recognition (psychology); Environmental science; Artificial intelligence; Geography; Land use; Machine learning; Image (mathematics)","score_opus":0.05743423422570928,"score_gpt":0.3040385935813299,"score_spread":0.2466043593556206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965362766","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03218676,0.00049296767,0.96640426,0.00015427369,0.0003138389,0.00019391642,0.00012044619,0.000071615876,0.00006191027],"genre_scores_gemma":[0.471346,0.000006919678,0.5281207,0.000053473337,0.00027680612,2.1273733e-8,0.000165548,0.000027796714,0.0000027483293],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881315,0.000042782845,0.0004293255,0.00017685663,0.00033865467,0.00019924792],"domain_scores_gemma":[0.9989342,0.00012918658,0.00025345988,0.00031667014,0.00024017582,0.0001263021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063744053,0.00017809068,0.00022819622,0.00039980313,0.00006825052,0.00008670816,0.0003061324,0.000106332154,0.000005948741],"category_scores_gemma":[0.000112219306,0.00016315574,0.00007746877,0.00015064287,0.00004015335,0.00038422347,0.000031615727,0.00020473111,8.823896e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020161232,0.00031994784,0.005829797,0.00008268439,0.0003096924,0.000012485474,0.00020136576,0.00041272337,0.030029912,0.000058350222,0.0004507851,0.9620907],"study_design_scores_gemma":[0.0008120259,0.000035727426,0.002104469,0.000053749012,0.00007438424,0.00023058143,0.000018970639,0.91028696,0.0069946395,0.000022236825,0.079188876,0.0001773773],"about_ca_topic_score_codex":0.00019756553,"about_ca_topic_score_gemma":0.0000048836127,"teacher_disagreement_score":0.9619133,"about_ca_system_score_codex":0.0001875696,"about_ca_system_score_gemma":0.00009062988,"threshold_uncertainty_score":0.6653298},"labels":[],"label_agreement":null},{"id":"W1967005377","doi":"10.1080/01431161.2012.756596","title":"InSAR time-series analysis of land subsidence in Bangkok, Thailand","year":2013,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":120,"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 Space Agency; Chulalongkorn University; European Commission","keywords":"Levelling; Interferometric synthetic aperture radar; Subsidence; Series (stratigraphy); Geology; Flooding (psychology); Water level; Synthetic aperture radar; Physical geography; Geodesy; Remote sensing; Geography; Geomorphology; Cartography","score_opus":0.005757996154143274,"score_gpt":0.22427523126957682,"score_spread":0.21851723511543356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967005377","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6990087,0.0002453594,0.29794058,0.00047089983,0.00013252471,0.00005916389,0.0000041530843,0.000028283013,0.00211031],"genre_scores_gemma":[0.7155249,0.000160905,0.28420526,0.000019993977,0.000051460716,2.6511708e-8,0.000002179179,0.000007965798,0.000027297414],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999262,0.000014748527,0.00035904924,0.000058609796,0.00022868218,0.00007693686],"domain_scores_gemma":[0.99938947,0.00010449586,0.00013180585,0.00009514455,0.00024918714,0.00002988419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014391147,0.00007536668,0.00021086373,0.00047364313,0.000009155839,0.000028175662,0.00015328138,0.00004470277,0.00006525158],"category_scores_gemma":[0.00006358867,0.000064923435,0.000093356466,0.0002581709,0.000030048257,0.00016899015,0.000016433069,0.00010804395,0.00000558734],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020614869,0.000017536564,0.0036910581,0.00001093978,0.0008056999,0.000057500103,0.00040666317,0.0009320445,0.015510417,0.00006625732,0.00041705772,0.97806424],"study_design_scores_gemma":[0.00076576776,0.0000867709,0.08970753,0.0007729506,0.000414722,0.0007182722,0.00026602618,0.763545,0.09132801,0.0055535557,0.046352617,0.00048879767],"about_ca_topic_score_codex":0.00020124756,"about_ca_topic_score_gemma":0.00009333133,"teacher_disagreement_score":0.9775754,"about_ca_system_score_codex":0.00005731646,"about_ca_system_score_gemma":0.000016671964,"threshold_uncertainty_score":0.2647501},"labels":[],"label_agreement":null},{"id":"W1968601348","doi":"10.1080/01431160903380565","title":"LiDAR mapping of canopy gaps in continuous cover forests: A comparison of canopy height model and point cloud based techniques","year":2010,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":92,"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":"Natural Environment Research Council; Bangor University","keywords":"Lidar; Canopy; Point cloud; Remote sensing; Environmental science; Understory; Tree canopy; Cloud cover; Meteorology; Computer science; Cloud computing; Geography","score_opus":0.010751009973144134,"score_gpt":0.269214789483628,"score_spread":0.2584637795104838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968601348","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93766075,0.000028625504,0.05896534,0.00070748373,0.00018396872,0.000099707366,0.00000644621,0.000008334924,0.0023393584],"genre_scores_gemma":[0.8361672,0.000010511845,0.16363256,0.00008168254,0.00006632322,1.5969018e-8,0.000001933102,0.000011416345,0.000028362936],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985084,0.000041412375,0.0006851512,0.00014871324,0.00047682936,0.0001394809],"domain_scores_gemma":[0.998818,0.00010354465,0.0006727356,0.00015379518,0.00016545579,0.000086494576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004675056,0.00012011004,0.00030846574,0.00021233616,0.00003294464,0.000026069338,0.00020026752,0.000085101194,0.000020533847],"category_scores_gemma":[0.00015671265,0.00011109282,0.00008446093,0.00013892457,0.000205752,0.00012158448,0.00006986279,0.00033806244,0.0000016134636],"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":[0.00014254669,0.000101109385,0.01632718,0.000015463345,0.00004244204,0.000033667613,0.0013803567,0.007847239,0.8260782,0.00007963109,0.00046871326,0.1474835],"study_design_scores_gemma":[0.00088732893,0.00010274223,0.011208231,0.00051934476,0.000026361842,0.0003210387,0.00024693998,0.6699807,0.3089028,0.0028155607,0.004771948,0.00021697379],"about_ca_topic_score_codex":0.0010205294,"about_ca_topic_score_gemma":0.0011545168,"teacher_disagreement_score":0.66213346,"about_ca_system_score_codex":0.000127329,"about_ca_system_score_gemma":0.00008024,"threshold_uncertainty_score":0.45302337},"labels":[],"label_agreement":null},{"id":"W1969381355","doi":"10.1080/01431160902821999","title":"The contribution of remote sensing to the implementation of the Montreal Protocol and the monitoring of its success","year":2009,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Atmospheric Ozone and Climate","field":"Earth and Planetary Sciences","cited_by":7,"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":"Montreal Protocol; Ozone layer; Protocol (science); Negotiation; Environmental science; Meteorology; Library science; Computer science; Political science; Ozone; Law; Geography; Medicine","score_opus":0.010964781801672352,"score_gpt":0.29662594637442163,"score_spread":0.28566116457274926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969381355","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98700327,0.00013974351,0.00394218,0.005828743,0.0004597735,0.00236393,0.000006944214,0.00000241774,0.0002529879],"genre_scores_gemma":[0.9970153,0.000074961004,0.0025833445,0.000086548716,0.00022725103,6.574313e-8,7.6973885e-7,0.000001988405,0.000009784022],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99854684,0.00016868088,0.00053933146,0.00006797169,0.00055695925,0.00012020916],"domain_scores_gemma":[0.99811655,0.00028350597,0.000855798,0.000106684274,0.0006089109,0.000028544218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001041613,0.000078570716,0.00015784607,0.000017488423,0.00016498324,0.00005499647,0.00025465825,0.000024824354,0.0000054624043],"category_scores_gemma":[0.00016787487,0.000033972086,0.00009399683,0.00013239551,0.000093094444,0.00012254977,0.000022328046,0.00013346387,4.1013442e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048200967,0.000002404934,0.0037295949,0.0000042983033,0.00006784083,0.0000043297673,0.00091309065,0.0014371865,0.001575141,0.000025799687,0.000022130132,0.9917362],"study_design_scores_gemma":[0.003691596,0.00038510532,0.7852777,0.0008428007,0.00013882636,0.00048268068,0.0055257487,0.13082266,0.06664217,0.0038126921,0.0022115682,0.00016644153],"about_ca_topic_score_codex":0.002277158,"about_ca_topic_score_gemma":0.0011976487,"teacher_disagreement_score":0.99156976,"about_ca_system_score_codex":0.00001311877,"about_ca_system_score_gemma":0.00005913644,"threshold_uncertainty_score":0.3442397},"labels":[],"label_agreement":null},{"id":"W1970425048","doi":"10.1080/01431161.2013.871394","title":"Use of lidar-derived NDTI and intensity for rule-based object-oriented extraction of building footprints","year":2014,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; State Key Laboratory of Earth Surface Processes and Resource Ecology","keywords":"Lidar; Footprint; Computer science; Remote sensing; Object (grammar); Ranging; Segmentation; Intensity (physics); Building model; Process (computing); Extraction (chemistry); Data mining; Tree (set theory); Artificial intelligence; Geography; Mathematics; Simulation","score_opus":0.022858340452510766,"score_gpt":0.28107056132431496,"score_spread":0.2582122208718042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970425048","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60949606,0.0000039494653,0.38981944,0.00027068975,0.00023828978,0.00005909704,0.0000020284767,0.0000049995897,0.00010545112],"genre_scores_gemma":[0.73888403,0.0000070838496,0.2609517,0.00005975239,0.00007334103,9.106454e-9,0.0000017483967,0.000009282222,0.000013042384],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988124,0.00005105658,0.0004893072,0.00014962666,0.0003866086,0.00011101862],"domain_scores_gemma":[0.9983756,0.00030801512,0.0007349891,0.00013249459,0.00038143445,0.00006747704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044969792,0.000101527374,0.00022389945,0.00013670797,0.00005469896,0.00002860276,0.000109285524,0.000057885012,0.0000060244383],"category_scores_gemma":[0.0005818227,0.000096109,0.00012076366,0.000096021446,0.00015159126,0.00016198092,0.000051897627,0.00013232799,8.9016646e-7],"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":[0.00023218141,0.000029221059,0.00095007697,0.000008011861,0.0000451493,0.0000027557578,0.0001456215,0.0020532245,0.69965506,0.000030446558,0.00003470796,0.29681352],"study_design_scores_gemma":[0.0010993727,0.000175806,0.03756714,0.00036792553,0.00007269853,0.00035375459,0.0001116389,0.25722492,0.6952209,0.001499398,0.006133299,0.00017314707],"about_ca_topic_score_codex":0.00036297302,"about_ca_topic_score_gemma":0.000024790916,"teacher_disagreement_score":0.2966404,"about_ca_system_score_codex":0.000104185914,"about_ca_system_score_gemma":0.00002096424,"threshold_uncertainty_score":0.39192113},"labels":[],"label_agreement":null},{"id":"W1970500457","doi":"10.1080/0143116031000115274","title":"Analysis of Temperature Emissivity Separation (TES) algorithm applicability and sensitivity","year":2003,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":86,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Jet Propulsion Laboratory; Fonds Québécois de la Recherche sur la Nature et les Technologies; Defence Research and Development Canada; Natural Sciences and Engineering Research Council of Canada; U.S. Geological Survey; Johns Hopkins University; National Aeronautics and Space Administration","keywords":"Emissivity; Hyperspectral imaging; Advanced Spaceborne Thermal Emission and Reflection Radiometer; Remote sensing; Radiometer; Broadband; Environmental science; Optics; Computer science; Algorithm; Geology; Physics; Digital elevation model","score_opus":0.006741275358777904,"score_gpt":0.2592648817015378,"score_spread":0.2525236063427599,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970500457","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92300606,0.000023291705,0.07603744,0.00014660625,0.00014317405,0.000050030154,0.000007894807,0.000004388955,0.0005811463],"genre_scores_gemma":[0.9578771,0.000016763071,0.041955,0.00006358986,0.000047962974,9.63363e-9,0.0000046500863,0.0000044247786,0.000030494604],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9988131,0.0001579442,0.00033636932,0.00014480486,0.00046119228,0.000086588305],"domain_scores_gemma":[0.9992398,0.00013005125,0.00030969304,0.00009972218,0.00015300827,0.00006772584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082765834,0.000086523694,0.00021315522,0.00012532473,0.000039073875,0.00002965657,0.000044623834,0.00006242735,0.00004887715],"category_scores_gemma":[0.00023932643,0.00007716195,0.00011242648,0.00027633167,0.00009280736,0.00021230562,0.00002946649,0.00014593065,0.0000019067082],"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":[0.00006314008,0.00007998435,0.04958285,0.0000062004788,0.0007395461,0.00011506422,0.00085497275,0.0058446135,0.39692983,0.00003076729,0.00019747022,0.5455556],"study_design_scores_gemma":[0.0011180247,0.000118750104,0.4191042,0.0000889302,0.00082298054,0.0014989098,0.0002487692,0.38164598,0.18966392,0.0024364172,0.0028769535,0.00037615403],"about_ca_topic_score_codex":0.00015072813,"about_ca_topic_score_gemma":0.00011361072,"teacher_disagreement_score":0.5451794,"about_ca_system_score_codex":0.00015367512,"about_ca_system_score_gemma":0.000019860625,"threshold_uncertainty_score":0.3146573},"labels":[],"label_agreement":null},{"id":"W1971084299","doi":"10.1080/01431161.2010.494636","title":"Leaf area index mapping in northern Canada","year":2011,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":18,"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 Space Agency","funders":"Natural Resources Canada; National Oceanic and Atmospheric Administration","keywords":"Thematic Mapper; Remote sensing; Leaf area index; Vegetation (pathology); Biome; Environmental science; Advanced very-high-resolution radiometer; Normalized Difference Vegetation Index; Thematic map; Satellite; Range (aeronautics); Satellite imagery; Geography; Physical geography; Cartography; Ecosystem; Ecology","score_opus":0.015592349112067637,"score_gpt":0.20159577308168686,"score_spread":0.18600342396961922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971084299","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97624195,0.000018589135,0.0061982023,0.0005875624,0.0011745716,0.000042370055,6.4510317e-7,0.000006888402,0.01572922],"genre_scores_gemma":[0.97553945,0.00001363972,0.023799237,0.00035681005,0.00015732311,1.5887202e-9,8.736368e-7,0.000011313959,0.000121370365],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99842644,0.000046317844,0.00044070513,0.00014326975,0.00074979715,0.0001934487],"domain_scores_gemma":[0.9992934,0.000036492343,0.0003650725,0.000104800136,0.000113203816,0.00008702956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023983547,0.00012516358,0.0001606853,0.00009887602,0.000031826792,0.00002727985,0.00031967426,0.000057698257,0.00008227116],"category_scores_gemma":[0.00011892463,0.00010228681,0.00006676036,0.00017366385,0.000052790263,0.00018327401,0.000096115604,0.00032405742,0.000014653747],"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":[0.00010930507,0.000050304083,0.31243205,0.000004093756,0.0001311179,0.005279685,0.003805562,0.008766296,0.0129516795,0.0000038909543,0.0029692524,0.65349674],"study_design_scores_gemma":[0.0009905259,0.000039354716,0.93744546,0.0004684104,0.000014022342,0.0047169654,0.0013993889,0.034211215,0.004696502,0.00134739,0.014239894,0.00043087674],"about_ca_topic_score_codex":0.37372524,"about_ca_topic_score_gemma":0.83135515,"teacher_disagreement_score":0.65306586,"about_ca_system_score_codex":0.0010067088,"about_ca_system_score_gemma":0.00009391003,"threshold_uncertainty_score":0.6304452},"labels":[],"label_agreement":null},{"id":"W1978881754","doi":"10.1080/01431160210156018","title":"Growth profile based crop yield models: A case study of large area wheat yield modelling and its extendibility using atmospheric corrected NOAA AVHRR data","year":2003,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":29,"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":"McGill University","keywords":"Normalized Difference Vegetation Index; Advanced very-high-resolution radiometer; Mathematics; Crop yield; Environmental science; Statistics; Satellite; Leaf area index; Agronomy; Biology","score_opus":0.07220199375687673,"score_gpt":0.28519330365674267,"score_spread":0.21299130989986592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978881754","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79721427,0.000097370954,0.2015406,0.000058211772,0.00047964646,0.00023083789,0.0000140241245,0.000014531865,0.00035052974],"genre_scores_gemma":[0.86894447,0.000018329658,0.13082227,0.00009106918,0.00006952675,1.1298974e-8,0.0000038430107,0.000022561244,0.000027915932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99727744,0.00021198374,0.000787999,0.00047263806,0.00096267596,0.00028727597],"domain_scores_gemma":[0.998065,0.0002644044,0.00069916365,0.00038597334,0.0004328741,0.00015257792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009367686,0.0002592013,0.0003825221,0.000052663258,0.00015648938,0.00009161741,0.00037885134,0.000117008094,0.00007914773],"category_scores_gemma":[0.00064145075,0.0002130065,0.00008606617,0.00030728776,0.00006918561,0.00083352847,0.00025641933,0.00044714377,0.0000021983954],"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":[0.00030799772,0.00081475585,0.001762481,0.000037799888,0.0002835372,0.006899415,0.0031348849,0.9332949,0.039774105,0.0000061979326,0.0003302803,0.013353639],"study_design_scores_gemma":[0.0007435463,0.00012047129,0.00014222656,0.0003146361,0.00009076463,0.010318486,0.0016787929,0.9830102,0.0032207535,0.00011902005,0.000029918101,0.00021121996],"about_ca_topic_score_codex":0.0022946836,"about_ca_topic_score_gemma":0.00054053974,"teacher_disagreement_score":0.07173021,"about_ca_system_score_codex":0.00027720767,"about_ca_system_score_gemma":0.00006613009,"threshold_uncertainty_score":0.8686153},"labels":[],"label_agreement":null},{"id":"W1982719377","doi":"10.1080/01431161.2015.1029099","title":"A new method to generate a high-resolution global distribution map of lake chlorophyll","year":2015,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"European Space Agency; Michigan Technological University; U.S. Geological Survey; National Aeronautics and Space Administration","keywords":"Environmental science; Chlorophyll a; Remote sensing; Satellite; Satellite imagery; Range (aeronautics); Geology","score_opus":0.018469280807523695,"score_gpt":0.26527491054300595,"score_spread":0.24680562973548226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982719377","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20895205,0.00009350438,0.7859924,0.0016533507,0.002219092,0.00005202622,0.00016364522,0.000007500535,0.0008664347],"genre_scores_gemma":[0.8218437,0.0000045838583,0.17675723,0.0001212985,0.0010135424,1.6763818e-9,0.00009217401,0.0000021131043,0.0001653628],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987322,0.00008279251,0.00039515615,0.00009688177,0.00056940556,0.00012356651],"domain_scores_gemma":[0.9987608,0.000028794473,0.00032176063,0.000065246786,0.0006106103,0.00021277908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052048586,0.00008345845,0.00017395186,0.00007175626,0.000020406946,0.000057454152,0.00017451163,0.000039150196,0.00007474292],"category_scores_gemma":[0.00009224712,0.00007015085,0.00007788542,0.00012437893,0.0000092417085,0.0001647555,0.0000250188,0.0000822097,0.000038382295],"study_design_candidate":"design_other","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":[0.00037236468,0.000005338123,0.0016338695,0.000004812502,0.00007053391,0.00010670688,0.0000677169,0.020458613,0.00020488167,0.0000765598,0.004655439,0.97234315],"study_design_scores_gemma":[0.0033299478,0.0013593318,0.050825495,0.0005254466,0.00012106701,0.0041598026,0.00039494527,0.57837766,0.0050314264,0.016681107,0.33862993,0.0005638593],"about_ca_topic_score_codex":0.010099277,"about_ca_topic_score_gemma":0.008812151,"teacher_disagreement_score":0.9717793,"about_ca_system_score_codex":0.000037532376,"about_ca_system_score_gemma":0.00018684728,"threshold_uncertainty_score":0.99649256},"labels":[],"label_agreement":null},{"id":"W1985338631","doi":"10.1080/01431160410001726030","title":"DSM generation and evaluation from QuickBird stereo imagery with 3D physical modelling","year":2004,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":54,"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","keywords":"Digital elevation model; Photogrammetry; Elevation (ballistics); Remote sensing; Lidar; Terrain; Land cover; Digital surface; Scale (ratio); Data set; Computer science; Geology; Geography; Artificial intelligence; Cartography; Land use; Mathematics","score_opus":0.024281636968545968,"score_gpt":0.2731325074385308,"score_spread":0.24885087046998483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985338631","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63631225,0.00002117508,0.3622805,0.00066457276,0.0001401543,0.000050521077,0.0000011519834,0.0000073072943,0.00052240695],"genre_scores_gemma":[0.7811539,0.000018758326,0.21815555,0.00012212357,0.00052094687,1.1878709e-8,0.000006860652,0.000011536014,0.00001027725],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986508,0.000047226553,0.00025047056,0.0001801454,0.00076728594,0.00010410177],"domain_scores_gemma":[0.9993392,0.000041024126,0.00026862614,0.00010949522,0.00017031125,0.000071346825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027823175,0.00010650686,0.00012642234,0.000065269014,0.000083709514,0.0001068457,0.000096308424,0.00003514939,0.0000133252215],"category_scores_gemma":[0.000026024574,0.000088305824,0.000045691013,0.00007913896,0.00008732673,0.00031055248,0.000037148762,0.00016225489,0.00001806468],"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":[0.000048434402,0.000027755726,0.000057047408,6.8519915e-7,0.000054199143,0.000030383888,0.001174791,0.35461515,0.07187078,0.000012704085,0.0000356309,0.57207245],"study_design_scores_gemma":[0.00082019717,0.000051078547,0.000821537,0.000087078864,0.00006260043,0.00037136782,0.00010403245,0.98273766,0.009932861,0.0044738785,0.00041292474,0.00012475459],"about_ca_topic_score_codex":0.00083541323,"about_ca_topic_score_gemma":0.00009990147,"teacher_disagreement_score":0.6281225,"about_ca_system_score_codex":0.00027814356,"about_ca_system_score_gemma":0.000043281358,"threshold_uncertainty_score":0.36010072},"labels":[],"label_agreement":null},{"id":"W1986933285","doi":"10.1080/014311601450022","title":"An assessment of validation techniques for estimating chlorophyll-a concentration from airborne multispectral imagery","year":2001,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Water Quality Monitoring and Analysis","field":"Environmental Science","cited_by":9,"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":"Remote sensing; Multispectral image; Environmental science; Chlorophyll a; Calibration; Sampling (signal processing); Eutrophication; Chlorophyll; Satellite imagery; Computer science; Geology; Ecology","score_opus":0.02132269731685673,"score_gpt":0.350036738664705,"score_spread":0.3287140413478483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986933285","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54054666,0.000002623283,0.45891118,0.00018233829,0.0002629078,0.000032236658,0.0000035664382,0.000008274989,0.00005019951],"genre_scores_gemma":[0.5569788,0.0000056336553,0.44259137,0.000012984286,0.00038876425,2.298702e-8,0.000011868389,0.0000048779643,0.0000056710705],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987712,0.00006328241,0.00048009728,0.00012294362,0.00046405217,0.00009844656],"domain_scores_gemma":[0.9990684,0.0000834787,0.0005497665,0.00008893907,0.00016078996,0.000048655285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047278195,0.000081011545,0.00015602447,0.00005702633,0.000047243153,0.00006740247,0.00015776776,0.000035501278,0.000026000313],"category_scores_gemma":[0.00008473479,0.00007487723,0.00010577757,0.000065851666,0.000046427347,0.00041750236,0.000021391255,0.0000909645,9.4661203e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004296401,0.000041689666,0.0031444265,0.0000021807832,0.00006146825,0.000020709631,0.0002499097,0.008284104,0.59713376,0.000004390138,0.000019161507,0.39099523],"study_design_scores_gemma":[0.00032735974,0.00010148366,0.0104753,0.00012301999,0.000046524623,0.000043572298,0.000098897195,0.5157672,0.47144145,0.0013718493,0.00010660835,0.000096705015],"about_ca_topic_score_codex":0.00078198366,"about_ca_topic_score_gemma":0.0000056272534,"teacher_disagreement_score":0.5074831,"about_ca_system_score_codex":0.00021568868,"about_ca_system_score_gemma":0.000017404267,"threshold_uncertainty_score":0.30534047},"labels":[],"label_agreement":null},{"id":"W1987943629","doi":"10.1080/01431161.2014.902549","title":"Synoptic mapping of high-rise buildings in urban areas based on combined shadow analysis and scale space processing","year":2014,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and Land Use","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":"Natural Resources Canada","funders":"","keywords":"Beijing; Shadow (psychology); Remote sensing; Satellite imagery; Pixel; Scale (ratio); Computer science; Satellite; Geography; Artificial intelligence; Cartography; China; Engineering","score_opus":0.006847545527310313,"score_gpt":0.2100057511686915,"score_spread":0.20315820564138118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987943629","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9676017,0.00007183721,0.030401614,0.00087007857,0.00024809278,0.000034377335,0.0000029057815,0.000007996596,0.0007613879],"genre_scores_gemma":[0.9492104,0.00002005912,0.05046413,0.00013934748,0.0001370496,6.6170663e-10,0.0000059060985,0.000004607133,0.000018486751],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998595,0.0001090248,0.00044978276,0.00016910137,0.0005160913,0.00016101322],"domain_scores_gemma":[0.99878335,0.00027763133,0.00047338285,0.000097185286,0.00026919265,0.00009928699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069438206,0.00012826336,0.00035226427,0.0009179471,0.00005003322,0.000100520105,0.00014160882,0.000058138234,0.000013929575],"category_scores_gemma":[0.0002537399,0.00010404143,0.00010754762,0.00042301338,0.000065516586,0.0001422362,0.000008775244,0.00021085794,0.000001395794],"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":[0.0003650285,0.000023270784,0.14473045,0.000026985535,0.00014412825,0.00010538495,0.0004971434,0.052651368,0.0006887479,0.0000051751667,0.00002203585,0.8007403],"study_design_scores_gemma":[0.00068784755,0.00011149196,0.24759266,0.0005381139,0.00006779224,0.00006942956,0.00008085644,0.74975485,0.0005458144,0.00033550526,0.00011615893,0.0000994831],"about_ca_topic_score_codex":0.0024397878,"about_ca_topic_score_gemma":0.00088541,"teacher_disagreement_score":0.8006408,"about_ca_system_score_codex":0.000018253962,"about_ca_system_score_gemma":0.000045302895,"threshold_uncertainty_score":0.42426863},"labels":[],"label_agreement":null},{"id":"W1988153935","doi":"10.1080/01431161.2013.804222","title":"Regional algorithms for remote-sensing estimates of total suspended matter in the Beaufort Sea","year":2013,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"North Dakota State University","keywords":"Beaufort sea; Oceanography; China; Chinese academy of sciences; Beaufort scale; Fishery; Geography; Marine fisheries; Environmental science; Fish <Actinopterygii>; Geology; Archaeology; Sea ice; Biology","score_opus":0.02166618163175441,"score_gpt":0.25224320499514413,"score_spread":0.23057702336338973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988153935","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94385993,0.00014354092,0.043612693,0.0051915627,0.0013665052,0.0002881967,0.000020410876,0.000007351501,0.005509784],"genre_scores_gemma":[0.9192993,0.000010871762,0.07966012,0.00043310635,0.00047328303,3.949894e-9,0.000027503203,0.000005269555,0.00009051285],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985483,0.000056152876,0.000555667,0.00011778044,0.0005360014,0.00018606546],"domain_scores_gemma":[0.99864006,0.00036771517,0.00040042624,0.0000982349,0.00044166434,0.000051880063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005502897,0.000115297385,0.0002163754,0.00018941265,0.000045581128,0.00010906741,0.00022866775,0.000049173475,0.00021090769],"category_scores_gemma":[0.00009759216,0.000079263475,0.00014376086,0.00009643272,0.000045561635,0.00026709324,0.000017577382,0.00017402983,0.000021986718],"study_design_candidate":"design_other","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":[0.00009941577,0.0000075791804,0.004641856,0.000019106734,0.00007925689,0.00009861259,0.00030123472,0.001062068,0.00025388904,0.0000073550673,0.0016264959,0.9918031],"study_design_scores_gemma":[0.00073926826,0.00014670029,0.043272447,0.00031883418,0.000024341574,0.0055056354,0.00077114726,0.9370575,0.0005823113,0.009711911,0.001700466,0.00016944282],"about_ca_topic_score_codex":0.009334559,"about_ca_topic_score_gemma":0.0015141335,"teacher_disagreement_score":0.9916337,"about_ca_system_score_codex":0.000014278053,"about_ca_system_score_gemma":0.00006075153,"threshold_uncertainty_score":0.99726236},"labels":[],"label_agreement":null},{"id":"W1988536071","doi":"10.1080/01431160500181754","title":"Broadband solar radiances from visible band measurements: A method based on ScaRaB observations and model simulations","year":2005,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Atmospheric aerosols and clouds","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":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Radiance; Remote sensing; Narrowband; Broadband; Zenith; Environmental science; Satellite; Radiometer; Solar zenith angle; Physics; Optics; Geology; Astronomy","score_opus":0.038820428071961476,"score_gpt":0.2931554183053736,"score_spread":0.25433499023341216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988536071","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48897853,0.0000559023,0.507782,0.0020637924,0.00013354907,0.000040489558,0.000013056117,0.0000063412376,0.0009263564],"genre_scores_gemma":[0.6208795,0.000013445876,0.37817028,0.0007467861,0.00013727712,1.2981973e-8,0.0000021901224,0.000007424799,0.0000431112],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998601,0.000051084684,0.00031331653,0.00015461123,0.0007634325,0.00011652063],"domain_scores_gemma":[0.9993215,0.00012481035,0.00024372981,0.00009459585,0.00012381164,0.00009153453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032043966,0.0001081864,0.00013642591,0.000011022556,0.000113889466,0.0000862196,0.00014926966,0.000044981458,0.00013356292],"category_scores_gemma":[0.00012887904,0.00009583774,0.00006791867,0.00008607181,0.000051579565,0.00034286696,0.000027342201,0.00014002183,0.0000057045495],"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":[0.000045527846,0.000023503359,0.0027206358,5.159914e-7,0.000037896625,0.000005563588,0.00017638835,0.8187904,0.014902212,0.000002855354,0.0002446069,0.16304992],"study_design_scores_gemma":[0.0006625967,0.000026754404,0.006409342,0.00007777204,0.000027537353,0.000016470549,0.000027908423,0.9865814,0.0031216203,0.0010325597,0.0019171267,0.00009893022],"about_ca_topic_score_codex":0.0002501023,"about_ca_topic_score_gemma":0.00019704775,"teacher_disagreement_score":0.16779101,"about_ca_system_score_codex":0.00025555986,"about_ca_system_score_gemma":0.000044639477,"threshold_uncertainty_score":0.390815},"labels":[],"label_agreement":null},{"id":"W1989203653","doi":"10.1080/01431160050121348","title":"Adaptive thresholding of texture towards white region normalization","year":2000,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","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":"Simon Fraser University","funders":"","keywords":"Thresholding; Artificial intelligence; Color constancy; Preprocessor; Computer vision; Computer science; Normalization (sociology); Pixel; Remote sensing; Image processing; Pattern recognition (psychology); Image (mathematics); Geography","score_opus":0.01770776087125281,"score_gpt":0.24224732785105094,"score_spread":0.22453956697979813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989203653","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31958655,0.00034912696,0.6659138,0.00063919125,0.0013692928,0.00008226196,0.0000029279097,0.000071281305,0.011985588],"genre_scores_gemma":[0.94828624,0.0003884306,0.05061739,0.00005653046,0.00046846204,4.9291518e-9,0.0000052232544,0.00003354341,0.000144161],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985973,0.000039791794,0.0005885703,0.00010500066,0.00053159037,0.00013773797],"domain_scores_gemma":[0.9986939,0.000033639586,0.00029804115,0.0001406193,0.0007765588,0.000057240668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021522827,0.00014262306,0.00021599594,0.00030056355,0.000029481886,0.00005583998,0.0001926748,0.000095183954,0.00003064242],"category_scores_gemma":[0.00008095369,0.00014070323,0.00013052489,0.00019234086,0.000048976097,0.00040685642,0.000013597633,0.00024717697,0.0000073267597],"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":[0.000088603745,0.00000764088,0.00004002787,0.000011433497,0.00011390136,0.000111434136,0.00067474315,0.07795895,0.016484499,0.00001755133,0.0006071057,0.9038841],"study_design_scores_gemma":[0.00058331393,0.00005800833,0.0026472837,0.0007417507,0.000046836503,0.0019712942,0.00018798775,0.95923305,0.02753792,0.00062438415,0.0061632995,0.00020490545],"about_ca_topic_score_codex":0.000014173853,"about_ca_topic_score_gemma":0.0000053574986,"teacher_disagreement_score":0.9036792,"about_ca_system_score_codex":0.00021277604,"about_ca_system_score_gemma":0.000043598026,"threshold_uncertainty_score":0.57377106},"labels":[],"label_agreement":null},{"id":"W1989382930","doi":"10.1080/01431160310001592445","title":"On the potential of MODIS and MERIS for imaging chlorophyll fluorescence from space","year":2004,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Golder Associates (Canada)","funders":"Fisheries and Oceans Canada; Canadian Space Agency","keywords":"Imaging spectrometer; Radiance; Remote sensing; Moderate-resolution imaging spectroradiometer; Environmental science; Spectrometer; Zenith; Chlorophyll fluorescence; Solar zenith angle; Satellite; Optics; Physics; Fluorescence; Geology","score_opus":0.007070344280444816,"score_gpt":0.2040825427534455,"score_spread":0.1970121984730007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989382930","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9403331,0.00013212906,0.054585773,0.0035494058,0.0007415907,0.0000513015,0.00003417649,0.0000026353596,0.0005698569],"genre_scores_gemma":[0.98875016,0.000045337227,0.010773869,0.00012779195,0.00028510517,1.9089443e-9,0.000004490202,0.0000021125838,0.000011106175],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993479,0.000018311765,0.00020528778,0.0000719485,0.00028203274,0.000074490184],"domain_scores_gemma":[0.9993795,0.00012553066,0.00023067891,0.000050772735,0.00017725688,0.000036228652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016804857,0.00005923211,0.00010082782,0.00005981774,0.0000433739,0.00005409955,0.00013266821,0.000014201035,0.000030942178],"category_scores_gemma":[0.00008777742,0.000038936192,0.00006569876,0.000031216994,0.000036510028,0.000103206825,0.000011843486,0.00008155755,0.0000019712872],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033368892,0.0000069160337,0.0007828786,0.0000059631857,0.00008684432,0.000100764635,0.00023642159,0.0075924974,0.0048691197,0.00018400163,0.00008182002,0.9857191],"study_design_scores_gemma":[0.0022797105,0.00039751615,0.030158,0.0008406769,0.000078220706,0.0011437004,0.0012578159,0.84796304,0.019241823,0.093865626,0.0024760223,0.00029782933],"about_ca_topic_score_codex":0.004644542,"about_ca_topic_score_gemma":0.00036476733,"teacher_disagreement_score":0.98542124,"about_ca_system_score_codex":0.0000062279705,"about_ca_system_score_gemma":0.000032845073,"threshold_uncertainty_score":0.7021189},"labels":[],"label_agreement":null},{"id":"W1990064929","doi":"10.1080/01431160210144697","title":"Comparative analysis of daytime fire detection algorithms using AVHRR data for the 1995 fire season in Canada: Perspective for MODIS","year":2003,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Fire effects on ecosystems","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":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Environmental science; Algorithm; Pixel; Remote sensing; Boreal; Taiga; Daytime; Meteorology; Geography; Computer science; Forestry; Atmospheric sciences; Geology; Artificial intelligence","score_opus":0.035035558611187,"score_gpt":0.3019702400989779,"score_spread":0.26693468148779087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990064929","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81083655,0.00018627946,0.18746604,0.00027016934,0.0006978062,0.00031784075,0.000136336,0.0000023774649,0.00008658024],"genre_scores_gemma":[0.9794542,0.000009526703,0.02038867,0.000036944402,0.00008209149,1.1740586e-7,0.000009073726,0.000008654046,0.000010687143],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985849,0.000113290975,0.00042321335,0.00020485451,0.0005174902,0.00015621903],"domain_scores_gemma":[0.9982922,0.00065095956,0.0005829993,0.00020593886,0.00022216058,0.000045756315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007833561,0.00011548829,0.0003264537,0.00010241221,0.00008441003,0.000031617856,0.0003680433,0.00003454167,0.000015497297],"category_scores_gemma":[0.0004410054,0.00009204251,0.00013095433,0.00035108413,0.000049153812,0.0002770503,0.000060751187,0.00013003356,4.1239767e-7],"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":[0.00055936706,0.000087599445,0.002889643,0.000021553245,0.004221842,0.00006473925,0.0029331811,0.61067766,0.017986713,0.000022363874,0.00058383215,0.35995153],"study_design_scores_gemma":[0.0004508329,0.000040412448,0.0035837241,0.000064318905,0.00027494383,0.000079790414,0.0014558528,0.9899081,0.0034141168,0.00007065494,0.00056716846,0.000090087364],"about_ca_topic_score_codex":0.5294097,"about_ca_topic_score_gemma":0.61422014,"teacher_disagreement_score":0.37923044,"about_ca_system_score_codex":0.0024567991,"about_ca_system_score_gemma":0.00019864828,"threshold_uncertainty_score":0.64244443},"labels":[],"label_agreement":null},{"id":"W1993216824","doi":"10.1080/01431160210154957","title":"The relation between spectral reflectance and dissolved organic carbon in lake water: Kejimkujik National Park, Nova Scotia, Canada","year":2003,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Water Quality Monitoring and Analysis","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":"Geological Survey of Canada","funders":"Health Canada; Oracle","keywords":"Dissolved organic carbon; Environmental science; Watershed; Mercury (programming language); Nova scotia; Spectral signature; Phytoplankton; Hydrology (agriculture); Reflectivity; Deciduous; Satellite imagery; Pollution; Environmental chemistry; Remote sensing; Geology; Nutrient; Oceanography; Ecology; Chemistry","score_opus":0.015425535295230669,"score_gpt":0.2521703311116165,"score_spread":0.23674479581638586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993216824","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99595016,0.000031652482,0.00037821668,0.0020409587,0.0005100525,0.000025442805,0.0000016684535,0.000003018872,0.001058824],"genre_scores_gemma":[0.9980689,0.000015884561,0.0015184587,0.000034450575,0.00021444952,5.4761875e-9,0.0000026409336,0.000007608696,0.00013758948],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99842346,0.00012143151,0.0003994482,0.00012584822,0.0007586515,0.00017114895],"domain_scores_gemma":[0.99950147,0.00011842264,0.00018001172,0.00006326335,0.00007890882,0.000057934787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008072196,0.00009026539,0.00012432905,0.00005799234,0.00007198803,0.00006797709,0.00013869829,0.000033721684,0.00002129898],"category_scores_gemma":[0.00020372657,0.00006267088,0.000037830203,0.000113445356,0.000056666897,0.00012112595,0.00003294865,0.00022234194,0.0000030617246],"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":[0.00016726776,0.000052674335,0.54794765,0.000010507684,0.0005665778,0.00046385606,0.0027541304,0.008828467,0.399146,0.00011170423,0.00043873096,0.03951243],"study_design_scores_gemma":[0.0020299645,0.000100127465,0.71758777,0.00037073885,0.00010790411,0.00077360094,0.0006430479,0.012797392,0.23966315,0.013167727,0.012047049,0.0007115415],"about_ca_topic_score_codex":0.043588523,"about_ca_topic_score_gemma":0.3990728,"teacher_disagreement_score":0.35548428,"about_ca_system_score_codex":0.0005695135,"about_ca_system_score_gemma":0.000067068235,"threshold_uncertainty_score":0.9627803},"labels":[],"label_agreement":null},{"id":"W1993432662","doi":"10.1080/01431160600928591","title":"The use of airborne lidar for orchard tree inventory","year":2008,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":26,"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; Centre de Géomatique du Québec","funders":"","keywords":"Lidar; Remote sensing; Multispectral image; Raster graphics; Orchard; Crown (dentistry); Tree (set theory); Environmental science; Forest inventory; Canopy; Tree canopy; Computer science; Geography; Forest management; Mathematics; Artificial intelligence; Agroforestry","score_opus":0.045285818517202606,"score_gpt":0.2712315256589789,"score_spread":0.2259457071417763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993432662","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9025224,0.00006071319,0.09235833,0.002889189,0.0007756313,0.00012638596,0.0000042158586,0.000010873673,0.0012523063],"genre_scores_gemma":[0.8856143,0.00017536167,0.112993516,0.00021370819,0.0003105829,1.4715803e-8,0.0000019359168,0.000015635309,0.0006749858],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988314,0.000042074164,0.00041680931,0.00009822673,0.0004857619,0.00012573211],"domain_scores_gemma":[0.9988926,0.0002515535,0.00043899575,0.00015378009,0.00020057446,0.00006246794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028883366,0.000080720514,0.00012542738,0.000058302037,0.0001509917,0.00003067063,0.00024611785,0.000037228026,0.000008836222],"category_scores_gemma":[0.00030187942,0.00005884804,0.00016393418,0.000089606576,0.0002448735,0.00015138299,0.000056087905,0.00012692607,0.000011188245],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017552401,0.000030317095,0.00040764865,0.0000021016137,0.000111773166,0.00003994591,0.0006931404,0.0017572672,0.026067464,0.00006237417,0.010830772,0.9598217],"study_design_scores_gemma":[0.0010646401,0.00016552882,0.012524694,0.0001383146,0.000049446666,0.0020474966,0.00015129957,0.09337635,0.02796364,0.0020658688,0.86023587,0.00021686363],"about_ca_topic_score_codex":0.00016864539,"about_ca_topic_score_gemma":0.0000699563,"teacher_disagreement_score":0.9596048,"about_ca_system_score_codex":0.00012925632,"about_ca_system_score_gemma":0.00003740202,"threshold_uncertainty_score":0.23997535},"labels":[],"label_agreement":null},{"id":"W1994809198","doi":"10.1080/01431160701736505","title":"Mapping the height and above‐ground biomass of a mixed forest using lidar and stereo Ikonos images","year":2008,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":143,"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 du Québec à Montréal","funders":"","keywords":"Lidar; Remote sensing; Digital elevation model; Elevation (ballistics); Multispectral image; Environmental science; Terrain; Digital surface; Photogrammetry; Canopy; Percentile; Ranging; Geography; Geodesy; Cartography; Mathematics; Statistics","score_opus":0.026325195189353334,"score_gpt":0.2516140571718428,"score_spread":0.22528886198248949,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994809198","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9642547,0.00019085365,0.033492155,0.0011646849,0.00019950785,0.00006572911,0.0000025019035,0.0000052894156,0.00062457885],"genre_scores_gemma":[0.94858134,0.00013997131,0.05100469,0.00007231519,0.0001377338,4.481949e-9,6.4381965e-7,0.000010721849,0.000052592084],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9989197,0.00005787692,0.00037894284,0.00013535561,0.00038633577,0.00012178633],"domain_scores_gemma":[0.99917865,0.00013159335,0.00040808873,0.000118375436,0.000098556906,0.00006474898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030006652,0.00010711143,0.00016176308,0.00010517517,0.0001654284,0.00005726355,0.00015104974,0.000040837378,0.0000061013056],"category_scores_gemma":[0.00006822317,0.00007874473,0.00006449693,0.0001273814,0.00039317046,0.0001878723,0.000118238095,0.00014267354,0.0000020245386],"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":[0.00009867674,0.000045856108,0.01658306,0.000016748536,0.00025666782,0.0002556977,0.004507489,0.0005397268,0.48179653,0.00003233983,0.00038490925,0.4954823],"study_design_scores_gemma":[0.0025686848,0.00020185592,0.7166082,0.00086737407,0.00014164476,0.039016318,0.0030294906,0.14829814,0.059773523,0.0057138465,0.023077836,0.00070304243],"about_ca_topic_score_codex":0.00063448696,"about_ca_topic_score_gemma":0.00005199952,"teacher_disagreement_score":0.7000252,"about_ca_system_score_codex":0.000076646655,"about_ca_system_score_gemma":0.000021250913,"threshold_uncertainty_score":0.32111168},"labels":[],"label_agreement":null},{"id":"W1996347147","doi":"10.1080/01431160701281023","title":"Canopy chlorophyll concentration estimation using hyperspectral and lidar data for a boreal mixedwood forest in northern Ontario, Canada","year":2007,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Ontario Forest Research Institute; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Foundation for Climate and Atmospheric Sciences","keywords":"Hyperspectral imaging; Lidar; Environmental science; Canopy; Remote sensing; Chlorophyll; Chlorophyll a; Mean squared error; Mathematics; Geography; Botany; Biology; Statistics","score_opus":0.013781119637210085,"score_gpt":0.24796428305527854,"score_spread":0.23418316341806844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996347147","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9530366,0.00002372802,0.045377433,0.00044875094,0.00065735215,0.00012552283,0.000007375382,0.0000039101856,0.00031932152],"genre_scores_gemma":[0.82708,0.000003432737,0.17256747,0.00009184705,0.00020255105,2.547041e-9,0.000030654126,0.000009699298,0.000014332412],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985578,0.000022004595,0.00044374252,0.00020325636,0.00055483915,0.00021837954],"domain_scores_gemma":[0.99919474,0.00009061291,0.00036319296,0.00013234442,0.00012626173,0.00009286946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045615446,0.00012627027,0.00015835493,0.000052256633,0.00006190538,0.00006996073,0.00023654425,0.00005941916,0.0000038157946],"category_scores_gemma":[0.00017060891,0.000111365815,0.000032010408,0.000091305745,0.000058334503,0.00036323458,0.00007776139,0.00019922356,3.9493614e-7],"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":[0.00086163625,0.000078638615,0.22632983,0.000016851383,0.0002003043,0.001410123,0.0033813845,0.06372716,0.077889495,0.000023775967,0.0006128402,0.62546796],"study_design_scores_gemma":[0.0017031818,0.00009951989,0.4586344,0.00025305993,0.000057607132,0.002866782,0.00048743808,0.5272412,0.00508416,0.00064462307,0.0025941613,0.00033391782],"about_ca_topic_score_codex":0.767963,"about_ca_topic_score_gemma":0.99500847,"teacher_disagreement_score":0.62513405,"about_ca_system_score_codex":0.0022481873,"about_ca_system_score_gemma":0.00031017084,"threshold_uncertainty_score":0.5878932},"labels":[],"label_agreement":null},{"id":"W1996393769","doi":"10.1080/01431161003698310","title":"Detecting recent disturbance on Montane blanket bogs in the Wicklow Mountains, Ireland using the MODIS enhanced vegetation index","year":2011,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Peatlands and Wetlands Ecology","field":"Environmental Science","cited_by":20,"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":"Enterprise Ireland; McGill University","keywords":"Bog; Environmental science; Peat; Disturbance (geology); Wetland; Hydrology (agriculture); Moderate-resolution imaging spectroradiometer; Vegetation (pathology); Physical geography; Ecology; Geology; Geography; Satellite; Geomorphology","score_opus":0.02089523213193823,"score_gpt":0.2541523156153111,"score_spread":0.23325708348337287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996393769","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9766263,0.000039495564,0.019699495,0.00065051374,0.00046082344,0.00007570441,5.720173e-7,0.0000037080897,0.0024434214],"genre_scores_gemma":[0.9969677,0.000082882376,0.0022243613,0.00054246426,0.00016233024,5.044579e-8,0.0000010756435,0.000007423511,0.000011756886],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9988204,0.00012680293,0.00032449552,0.00012419831,0.0004441211,0.00015998202],"domain_scores_gemma":[0.9993404,0.00012300104,0.00034749325,0.000109923756,0.000050070717,0.000029068115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006132596,0.00010077479,0.00011721681,0.000064628184,0.00009998982,0.000043195978,0.00030875762,0.000047019774,0.00002510405],"category_scores_gemma":[0.00012749614,0.00005859837,0.000055442797,0.00011940716,0.000072522,0.00015106547,0.000048167487,0.00029586628,0.000004873847],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013345442,0.00018483413,0.08497825,0.0000072059347,0.00017850497,0.001079787,0.021255607,0.044102143,0.029717233,0.000039826242,0.00016673193,0.8169553],"study_design_scores_gemma":[0.0021158229,0.0003400129,0.68678206,0.0003582317,0.000049386068,0.0023014087,0.0017706539,0.29183984,0.0075068246,0.00516893,0.001442251,0.00032458885],"about_ca_topic_score_codex":0.0006642074,"about_ca_topic_score_gemma":0.0013372822,"teacher_disagreement_score":0.8166307,"about_ca_system_score_codex":0.00029561468,"about_ca_system_score_gemma":0.000015487778,"threshold_uncertainty_score":0.23895721},"labels":[],"label_agreement":null},{"id":"W2000299034","doi":"10.1080/01431160310001642296","title":"Contextual classification of Landsat TM images to forest inventory cover types","year":2004,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":58,"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":"Smoothing; Random forest; Spatial contextual awareness; Pattern recognition (psychology); Pixel; Spatial analysis; Computer science; Classifier (UML); Statistics; Land cover; Remote sensing; Artificial intelligence; Mathematics; Geography","score_opus":0.013611226446229873,"score_gpt":0.25428319863496623,"score_spread":0.24067197218873637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000299034","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9664762,0.000029539235,0.025360424,0.0024214333,0.0008025776,0.000070274844,0.0000028531867,0.000009683037,0.0048269937],"genre_scores_gemma":[0.965274,0.000018002485,0.03392676,0.00030784492,0.00026105726,3.3164929e-9,0.0000030401725,0.000010233327,0.00019905428],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99866045,0.000035256457,0.00039218404,0.00012708665,0.00066470855,0.0001202906],"domain_scores_gemma":[0.99915326,0.000041196596,0.000399709,0.00010478886,0.00021131395,0.000089743684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024066876,0.000103703285,0.000158784,0.00009592629,0.000030901512,0.000038582606,0.00023690428,0.00005869453,0.000036201374],"category_scores_gemma":[0.0002443581,0.000081112965,0.00010014216,0.00012424552,0.00009335251,0.0002073573,0.00007507043,0.00015812588,0.0000978649],"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":[0.00028851363,0.00008714201,0.0053742304,0.000008104445,0.000175141,0.00028661662,0.0018570584,0.09950702,0.669193,0.00010930262,0.006509616,0.2166042],"study_design_scores_gemma":[0.0060361368,0.00091859826,0.61422235,0.0020917172,0.00020012922,0.00675923,0.0014767427,0.035689402,0.23318039,0.011884734,0.086380884,0.0011596681],"about_ca_topic_score_codex":0.00025788078,"about_ca_topic_score_gemma":0.0001562776,"teacher_disagreement_score":0.60884815,"about_ca_system_score_codex":0.00037995164,"about_ca_system_score_gemma":0.00003082229,"threshold_uncertainty_score":0.33076906},"labels":[],"label_agreement":null},{"id":"W2001510610","doi":"10.1080/01431161.2012.748992","title":"Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data","year":2012,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":1716,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre de Géomatique du Québec","funders":"","keywords":"Thematic Mapper; Land cover; Remote sensing; Moderate-resolution imaging spectroradiometer; Orthophoto; Cartography; Satellite imagery; Environmental science; Satellite; Geography; Land use","score_opus":0.03088526435108061,"score_gpt":0.2553800190547297,"score_spread":0.22449475470364907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001510610","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9922201,0.0001924602,0.005191985,0.001437726,0.00045479764,0.000047875088,0.000015726806,0.0000067228793,0.0004325846],"genre_scores_gemma":[0.89901704,0.00015521003,0.10033514,0.00003767346,0.0004099835,1.7773848e-9,0.000011401326,0.0000056932345,0.00002787072],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99883085,0.000038684866,0.00032431167,0.00014658479,0.000519967,0.00013958574],"domain_scores_gemma":[0.99917376,0.000081077975,0.00043237416,0.000138352,0.0000961293,0.0000783347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004736697,0.00010337979,0.00013995642,0.000031320345,0.000060960432,0.000052104715,0.00015020893,0.000057739497,0.0000019860386],"category_scores_gemma":[0.00021473397,0.00007474218,0.000019032845,0.000113868526,0.00007966849,0.0007772743,0.00019073153,0.00013385895,0.0000018605101],"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":[0.00046027216,0.000030055691,0.87662894,0.000018378576,0.0001330994,0.00007124197,0.0008282802,0.005052012,0.007374044,0.0000047266954,0.00093163975,0.10846732],"study_design_scores_gemma":[0.00097319396,0.000051455056,0.9576431,0.000559111,0.000034911507,0.0019374286,0.00011518284,0.0298617,0.0005805646,0.000057233447,0.008056491,0.00012961782],"about_ca_topic_score_codex":0.0005973615,"about_ca_topic_score_gemma":0.00012039683,"teacher_disagreement_score":0.1083377,"about_ca_system_score_codex":0.0001711911,"about_ca_system_score_gemma":0.000009979371,"threshold_uncertainty_score":0.30478978},"labels":[],"label_agreement":null},{"id":"W2003079203","doi":"10.1080/01431160152027665","title":"Multitemporal monitoring of soil moisture with RADARSAT SAR during the 1997 Southern Great Plains hydrology experiment","year":2001,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":53,"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 Space Agency; National Aeronautics and Space Administration; U.S. Department of Energy","keywords":"Synthetic aperture radar; Water content; Environmental science; Remote sensing; Correlation coefficient; Moisture; Hydrology (agriculture); Soil science; Geology; Meteorology; Geography; Mathematics","score_opus":0.009195638982366883,"score_gpt":0.23652836510455966,"score_spread":0.22733272612219277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003079203","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9943989,0.0001660134,0.0021267931,0.000782738,0.000775381,0.00006749075,0.000001194958,0.000012468291,0.0016690264],"genre_scores_gemma":[0.98141384,0.000059849284,0.017501995,0.000081060905,0.00069350854,3.1512533e-9,0.000001094125,0.0000240296,0.00022463326],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982698,0.00007981987,0.00044222528,0.00017989997,0.00080217933,0.00022609605],"domain_scores_gemma":[0.9990444,0.00006916884,0.0005285897,0.000178564,0.00009961726,0.00007966328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024650616,0.00018283965,0.00023838994,0.00009369729,0.00012950778,0.000040395586,0.0003033905,0.000079209865,0.000017337165],"category_scores_gemma":[0.00003363613,0.000114198316,0.00013925911,0.00012595016,0.00022219856,0.00014755383,0.00011377836,0.0003253866,0.0000099582885],"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":[0.0018549145,0.00011478214,0.2254644,0.000014137075,0.00077575695,0.0039698835,0.0154826855,0.047683556,0.21284346,0.0000031273642,0.00015105956,0.49164224],"study_design_scores_gemma":[0.010175061,0.00092083385,0.41519663,0.0021024318,0.00036463267,0.047631912,0.018168172,0.04837234,0.42181906,0.00062342617,0.033106584,0.0015189137],"about_ca_topic_score_codex":0.002170206,"about_ca_topic_score_gemma":0.0007024502,"teacher_disagreement_score":0.4901233,"about_ca_system_score_codex":0.00027257248,"about_ca_system_score_gemma":0.000022285809,"threshold_uncertainty_score":0.46568722},"labels":[],"label_agreement":null},{"id":"W2003682330","doi":"10.1080/01431160802509017","title":"A new approach to building identification from very‐high‐spatial‐resolution images","year":2009,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":39,"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 Sherbrooke","funders":"","keywords":"Identification (biology); Computer science; Urban planning; Remote sensing; High resolution; Artificial intelligence; Geography; Civil engineering","score_opus":0.009992272264239606,"score_gpt":0.25138679114711016,"score_spread":0.24139451888287056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003682330","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18832694,0.00009321704,0.80855566,0.0005327478,0.001843128,0.000042501393,0.0000031988916,0.00009499993,0.00050759467],"genre_scores_gemma":[0.74005336,0.000037682697,0.2586682,0.00006223769,0.0011161336,7.611624e-9,0.000008963842,0.000013183582,0.000040214065],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988715,0.000022006281,0.0004210714,0.00012590578,0.00042854075,0.00013094643],"domain_scores_gemma":[0.9993445,0.00003329957,0.00019128244,0.00010105839,0.00023670573,0.00009309997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019309444,0.00011869711,0.00014900819,0.00031948579,0.000042002786,0.00016604936,0.00019517449,0.000071574075,0.000008763272],"category_scores_gemma":[0.00009465638,0.000120754186,0.000082761035,0.00012813455,0.000006137135,0.0003785648,0.000013485403,0.00020786023,0.000021090078],"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":[0.00002899559,0.000008135812,0.000002201229,0.0000016332364,0.00005804733,0.000017189093,0.00013061977,0.085871376,0.3376317,0.000026684815,0.0026837073,0.57353973],"study_design_scores_gemma":[0.0007371604,0.00004688062,0.012206519,0.00040911903,0.000072248484,0.0005083844,0.000073983334,0.7742934,0.2008938,0.00463872,0.0058050505,0.00031473007],"about_ca_topic_score_codex":0.00026943794,"about_ca_topic_score_gemma":0.0000043862756,"teacher_disagreement_score":0.688422,"about_ca_system_score_codex":0.00025488075,"about_ca_system_score_gemma":0.000027879045,"threshold_uncertainty_score":0.49242127},"labels":[],"label_agreement":null},{"id":"W2007061445","doi":"10.1080/01431160701874595","title":"Snow depth estimation over north‐western Indian Himalaya using AMSR‐E","year":2008,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":28,"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":"Snow; Brightness temperature; Terrain; Radiometer; Remote sensing; Snow cover; Environmental science; Meteorology; Geology; Physical geography; Brightness; Climatology; Geography; Cartography","score_opus":0.0381612730997177,"score_gpt":0.2651668199591538,"score_spread":0.2270055468594361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007061445","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96546286,0.00017130139,0.032365043,0.00033035708,0.0013561944,0.000038338254,0.000008330087,0.000010386212,0.0002572014],"genre_scores_gemma":[0.9414354,0.00013609728,0.057439234,0.0003392248,0.00059520226,1.4679602e-9,0.000017100196,0.000004738373,0.000032984033],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99877936,0.000029910098,0.00039145924,0.00010572358,0.000537172,0.00015639892],"domain_scores_gemma":[0.9990128,0.00011547315,0.00037885635,0.000073011346,0.00033621772,0.00008361905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012868637,0.00010737475,0.00016114481,0.00010548836,0.000177042,0.0000610984,0.00016776491,0.000037630412,0.00009218678],"category_scores_gemma":[0.00014414405,0.00009341712,0.00010118781,0.00015747901,0.000059404967,0.00037978785,0.000015919091,0.00016225007,0.000017241935],"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":[0.00004339228,0.000007471036,0.51760185,0.0000026448395,0.00010698207,0.00058192597,0.0008641964,0.046412036,0.000038405735,0.0000012176966,0.00014911815,0.43419078],"study_design_scores_gemma":[0.00029271905,0.00003545026,0.8062956,0.00007400048,0.000016619788,0.0017782241,0.00010408408,0.18961796,0.000042331434,0.00010235222,0.0015422711,0.000098422504],"about_ca_topic_score_codex":0.001823477,"about_ca_topic_score_gemma":0.003984123,"teacher_disagreement_score":0.43409234,"about_ca_system_score_codex":0.00003555732,"about_ca_system_score_gemma":0.00008816838,"threshold_uncertainty_score":0.38094395},"labels":[],"label_agreement":null},{"id":"W2007854916","doi":"10.1080/01431161.2013.876117","title":"Multi-temporal radar backscattering measurements and modelling of rice fields using a multi-frequency (L, S, C, and X) scatterometer","year":2014,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Soil Moisture and Remote Sensing","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":"University of Toronto","funders":"","keywords":"Scatterometer; Leaf area index; Backscatter (email); Canopy; Scattering; Remote sensing; Environmental science; Monte Carlo method; Azimuth; Growing season; Radar; Biomass (ecology); Paddy field; Mathematics; Physics; Agronomy; Optics; Meteorology; Geology; Geography; Wind speed; Statistics; Computer science","score_opus":0.05752551979571841,"score_gpt":0.2737511376302259,"score_spread":0.2162256178345075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007854916","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5767058,0.0000853028,0.42258734,0.0001814237,0.00029477908,0.00003157914,2.8265072e-7,0.0000036742108,0.00010981385],"genre_scores_gemma":[0.5817048,0.000017853617,0.4180379,0.00014069551,0.00008140207,1.3988096e-9,3.5412452e-7,0.000010129718,0.0000068988147],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985852,0.0000816638,0.0004828458,0.00019357393,0.0004937479,0.00016293357],"domain_scores_gemma":[0.99917674,0.00006857392,0.00044051497,0.000105549465,0.00011352491,0.00009506696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005298571,0.00015142988,0.00024345431,0.00014054822,0.00006932965,0.000055923843,0.00013872553,0.00007548238,0.0000042009074],"category_scores_gemma":[0.00008858319,0.00013354284,0.00007483276,0.00007367127,0.00013194159,0.00027609774,0.00012579093,0.00019487306,0.0000014134702],"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":[0.000071086586,0.00004918507,0.02047308,0.00004262638,0.00019701196,0.000072701456,0.0016159684,0.007420068,0.6693672,6.3162247e-7,0.000011590195,0.30067885],"study_design_scores_gemma":[0.0019612245,0.00009519408,0.03251177,0.000733298,0.00009591293,0.0018974547,0.00014745991,0.94270045,0.01901775,0.00034816447,0.00016309867,0.0003282439],"about_ca_topic_score_codex":0.0020473253,"about_ca_topic_score_gemma":0.00018945745,"teacher_disagreement_score":0.9352804,"about_ca_system_score_codex":0.00009667872,"about_ca_system_score_gemma":0.0000111374775,"threshold_uncertainty_score":0.54457194},"labels":[],"label_agreement":null},{"id":"W2007932007","doi":"10.1080/01431160701253220","title":"Estimation of grassland CO<sub>2</sub>exchange rates using hyperspectral remote sensing techniques","year":2007,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":16,"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 Saskatchewan","funders":"","keywords":"Environmental science; Hyperspectral imaging; Remote sensing; Grassland; Vegetation (pathology); Carbon sink; Photochemical Reflectance Index; Reflectivity; Soil science; Ecosystem; Normalized Difference Vegetation Index; Atmospheric sciences; Leaf area index; Ecology; Geography; Geology","score_opus":0.013582660420947442,"score_gpt":0.2812807518834419,"score_spread":0.26769809146249446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007932007","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72112113,0.000049436952,0.2765585,0.00029872303,0.00058788277,0.00010184155,0.000002098215,0.00003035134,0.0012500426],"genre_scores_gemma":[0.6214064,0.000060033217,0.3780074,0.00008766962,0.0003975918,4.1143455e-10,0.00000427232,0.000023398454,0.000013255121],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9972225,0.000105069295,0.00088289665,0.00026169707,0.0011770818,0.0003507627],"domain_scores_gemma":[0.9980622,0.0001705888,0.0011260596,0.00018306136,0.0003192331,0.00013886705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012599262,0.00026180802,0.00036497982,0.00031860892,0.00011056736,0.00008990528,0.00026304845,0.00017550851,0.000011545197],"category_scores_gemma":[0.0003232347,0.00022602291,0.00021788417,0.0003305805,0.00022509003,0.00041044067,0.00009949984,0.00042629297,0.000011335622],"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":[0.00006754597,0.000008872088,0.000052969386,0.0000054621,0.000044666886,0.0002291059,0.0002840241,0.0017552987,0.5268886,0.000001044227,0.0001648128,0.4704976],"study_design_scores_gemma":[0.00034924658,0.00007685656,0.001992063,0.0004938233,0.000050693252,0.0048291194,0.00018186873,0.17234065,0.81813204,0.0009058727,0.0004156957,0.00023209421],"about_ca_topic_score_codex":0.00041207386,"about_ca_topic_score_gemma":0.00014490089,"teacher_disagreement_score":0.4702655,"about_ca_system_score_codex":0.0007717254,"about_ca_system_score_gemma":0.00003830542,"threshold_uncertainty_score":0.9216947},"labels":[],"label_agreement":null},{"id":"W2009812319","doi":"10.1080/014311601300190613","title":"Satellite remote sensing of submerged kelp beds on the Atlantic coast of Canada","year":2001,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Marine and coastal plant biology","field":"Earth and Planetary Sciences","cited_by":10,"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":"Kelp; Kelp forest; Laminaria; Biomass (ecology); Oceanography; Thematic Mapper; Environmental science; Satellite; Satellite imagery; Geology; Ecology; Biology; Algae; Physics","score_opus":0.014825603940383842,"score_gpt":0.2105348020735239,"score_spread":0.19570919813314006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009812319","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98929155,0.000093928815,0.0010964025,0.0016915852,0.0009294669,0.00003723906,0.000014188819,0.0000027367003,0.006842897],"genre_scores_gemma":[0.99664843,0.00029391574,0.002203095,0.00039126104,0.0002605835,1.3108867e-10,0.000016632926,0.0000031542268,0.00018291308],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985648,0.00011084837,0.00051237823,0.00009472749,0.00054096343,0.00017628483],"domain_scores_gemma":[0.9985766,0.00032336087,0.0005533722,0.00010305981,0.00037784636,0.00006576257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046039646,0.000107592845,0.00023069605,0.00012569955,0.0000493463,0.00001794706,0.00021973588,0.000042494456,0.000098737364],"category_scores_gemma":[0.00019361677,0.0000712177,0.00009249976,0.0001259094,0.00006489826,0.000055878205,0.000019526065,0.00021342245,0.000002225283],"study_design_candidate":"design_other","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":[0.0004078692,0.0000038520498,0.009554914,0.0000063374996,0.00012894101,0.0005442891,0.000113473085,0.0008396394,0.0024015526,0.00001255224,0.00030676366,0.9856798],"study_design_scores_gemma":[0.0032585722,0.0016290467,0.33306438,0.0029169533,0.0002726309,0.03202636,0.003390215,0.38942114,0.037719294,0.008618477,0.186426,0.0012569249],"about_ca_topic_score_codex":0.5928764,"about_ca_topic_score_gemma":0.7594265,"teacher_disagreement_score":0.98442286,"about_ca_system_score_codex":0.000015237635,"about_ca_system_score_gemma":0.00020592251,"threshold_uncertainty_score":0.40983468},"labels":[],"label_agreement":null},{"id":"W2012116092","doi":"10.1080/01431160500075857","title":"Detection of intense plankton blooms using the 709 nm band of the MERIS imaging spectrometer","year":2005,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":345,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"Fisheries and Oceans Canada; Canadian Space Agency; European Space Agency; Australian Government","keywords":"Radiance; Imaging spectrometer; Remote sensing; Environmental science; Satellite; Spectrometer; Ocean color; Oceanography; Geology; Physics; Optics; Astronomy","score_opus":0.00955445938651113,"score_gpt":0.21560441238187678,"score_spread":0.20604995299536566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012116092","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9915076,0.0001942443,0.005485131,0.00067038654,0.00090124836,0.00004082739,0.000006943946,0.0000024297203,0.0011912277],"genre_scores_gemma":[0.99743813,0.000027186114,0.001920204,0.0001102066,0.00046559077,9.665325e-10,8.756445e-7,0.0000028572663,0.000034925604],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988878,0.00007963265,0.00042528065,0.0000676658,0.00044149504,0.00009816227],"domain_scores_gemma":[0.99889344,0.00009832188,0.0005917036,0.00009431143,0.00029495606,0.00002729184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043295656,0.00007424483,0.000140762,0.00012279252,0.000055103566,0.00003855594,0.00022572541,0.000020579122,0.00005349188],"category_scores_gemma":[0.000073956326,0.000041983498,0.0001377675,0.00011211972,0.000060565002,0.0001699124,0.000020416479,0.00017100385,0.0000013970979],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016180903,0.0000059802037,0.008974835,0.000008635179,0.00013414779,0.000024387391,0.0005233591,0.0033459072,0.08257798,0.0000018782671,0.000031784773,0.9042093],"study_design_scores_gemma":[0.0007593781,0.00013818311,0.04809364,0.0005325196,0.00011870371,0.00670169,0.0012629618,0.73808116,0.19810276,0.0005784147,0.0054439153,0.00018666284],"about_ca_topic_score_codex":0.0028257424,"about_ca_topic_score_gemma":0.0021084999,"teacher_disagreement_score":0.90402263,"about_ca_system_score_codex":0.000015748765,"about_ca_system_score_gemma":0.000039420807,"threshold_uncertainty_score":0.4271696},"labels":[],"label_agreement":null},{"id":"W2016197844","doi":"10.1080/0143116031000115076","title":"Passive and active airborne microwave remote sensing of snow cover","year":2003,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":25,"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":"Snow; Environmental science; Remote sensing; Snowpack; Brightness temperature; Microwave; Scatterometer; Radiometer; Synthetic aperture radar; Radar; Meteorology; Geology; Wind speed; Geography","score_opus":0.016698253469485794,"score_gpt":0.23659048399671537,"score_spread":0.21989223052722956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016197844","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90694165,0.00054589193,0.087183446,0.0012254736,0.0013827172,0.000059949674,0.00001779716,0.0000054822162,0.0026376068],"genre_scores_gemma":[0.8875332,0.00032931296,0.11157957,0.00027814196,0.00019346662,1.9159614e-10,0.00000420092,0.0000040883638,0.0000780438],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99897826,0.00005976095,0.00036267043,0.00011389883,0.00035015226,0.00013524125],"domain_scores_gemma":[0.99848795,0.00030307707,0.00046280382,0.00006661611,0.000611725,0.0000678347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019664977,0.00010507152,0.00020950648,0.00007406782,0.000076955424,0.000040060946,0.00007868337,0.0000434631,0.000070781374],"category_scores_gemma":[0.000456883,0.0000892153,0.00008916321,0.00011750284,0.000089757275,0.00015973617,0.000012684837,0.00016170635,0.0000046550726],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008057819,0.0000031162122,0.0005946065,0.0000035707753,0.00017641281,0.00012289002,0.0005201411,0.0007161304,0.0008117974,0.00001594584,0.00021928696,0.9967355],"study_design_scores_gemma":[0.0050146277,0.0007231421,0.5352491,0.0019313586,0.00038282087,0.011414245,0.008509465,0.27415502,0.058875803,0.023898808,0.07875225,0.0010933235],"about_ca_topic_score_codex":0.0007496054,"about_ca_topic_score_gemma":0.0004376674,"teacher_disagreement_score":0.9956422,"about_ca_system_score_codex":0.000020964384,"about_ca_system_score_gemma":0.00007149567,"threshold_uncertainty_score":0.36380944},"labels":[],"label_agreement":null},{"id":"W2017980693","doi":"10.1080/0143116042000298243","title":"A reliable and fast ribbon road detector using profile analysis and model‐based verification","year":2005,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Ribbon; Computer science; Detector; Intersection (aeronautics); Perpendicular; Binary number; Road surface; Remote sensing; Computer vision; Artificial intelligence; Geology; Cartography; Geography; Geometry; Mathematics","score_opus":0.01041307232035199,"score_gpt":0.2509169805227287,"score_spread":0.24050390820237671,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017980693","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6448642,0.000118222575,0.35469225,0.000080182705,0.0001253731,0.000020445099,0.000001722158,0.000030011732,0.0000676052],"genre_scores_gemma":[0.80435026,0.000052517305,0.19540744,0.00001935962,0.00014715503,9.4369055e-9,0.0000023549565,0.00001109849,0.0000098373275],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993454,0.000009201443,0.00025894312,0.00008534545,0.00021315267,0.00008791918],"domain_scores_gemma":[0.9995525,0.000014389738,0.00014912507,0.000054494154,0.00018216771,0.00004731691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015275534,0.00008587629,0.00013350639,0.00037969658,0.000041482905,0.00007758347,0.00004755339,0.00005526737,0.0000041117687],"category_scores_gemma":[0.000021460182,0.00008324641,0.000053405012,0.000141386,0.000016305421,0.0002720562,0.000009262296,0.00013688982,7.6151014e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021535272,0.0000043135165,0.00009038624,0.0000065294935,0.0001782263,0.00000690614,0.000097520475,0.5518532,0.15088072,0.0000011874296,0.000011235373,0.29684824],"study_design_scores_gemma":[0.00023568688,0.000009427927,0.0016170354,0.000080103026,0.00013052156,0.00016013651,0.000019968642,0.97039276,0.027066724,0.000023157903,0.00018434886,0.00008015506],"about_ca_topic_score_codex":0.000038205715,"about_ca_topic_score_gemma":0.000013171794,"teacher_disagreement_score":0.41853952,"about_ca_system_score_codex":0.00013604668,"about_ca_system_score_gemma":0.000017687853,"threshold_uncertainty_score":0.339469},"labels":[],"label_agreement":null},{"id":"W2018392008","doi":"10.1080/01431160310001632675","title":"Rock unit discrimination on Landsat TM, SIR-C and Radarsat images using spectral and textural information","year":2004,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"U.S. Geological Survey","keywords":"Lacunarity; Remote sensing; Fractal dimension; Geology; Fractal; Box counting; Radar imaging; Fractal analysis; Grey level; Radar; Pixel; Artificial intelligence; Computer science; Mathematics","score_opus":0.016350248357029105,"score_gpt":0.2520287785723291,"score_spread":0.23567853021529997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018392008","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57881224,0.000039719926,0.41429716,0.0055191694,0.00033396485,0.000027461647,8.877285e-7,0.000011994297,0.0009574054],"genre_scores_gemma":[0.91801775,0.000029395287,0.081570335,0.00021275517,0.00014441392,1.6197321e-9,0.000002046467,0.0000013239568,0.000021961594],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992589,0.000019900599,0.00024156435,0.00008493522,0.00029262493,0.000102124475],"domain_scores_gemma":[0.99926937,0.000042778014,0.00025443867,0.000062794,0.00031884626,0.00005179921],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018478146,0.00008811027,0.00009748524,0.00014881909,0.00007616456,0.00023199321,0.0001527793,0.00003717331,0.000001223854],"category_scores_gemma":[0.00014839647,0.00007404155,0.00003221785,0.00006481655,0.000022762497,0.0009599772,0.00007754197,0.00015563676,0.0000011032054],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016000595,0.000034554712,0.000548776,0.000051782976,0.00014534954,0.00049626175,0.0050525893,0.027954852,0.037706625,0.006665194,0.00008055737,0.9211035],"study_design_scores_gemma":[0.005025493,0.00044193145,0.02353119,0.0016281033,0.000063285646,0.026177727,0.001354233,0.7089498,0.13312304,0.088183224,0.010791621,0.0007303483],"about_ca_topic_score_codex":0.000049586815,"about_ca_topic_score_gemma":0.00000327679,"teacher_disagreement_score":0.9203731,"about_ca_system_score_codex":0.00005165164,"about_ca_system_score_gemma":0.000038216953,"threshold_uncertainty_score":0.30193266},"labels":[],"label_agreement":null},{"id":"W2019919221","doi":"10.1080/01431161.2012.661097","title":"Application of Autoscala software to the Canadian Advanced Digital Ionosonde","year":2012,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Ionosphere and magnetosphere dynamics","field":"Physics and Astronomy","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Western University","funders":"","keywords":"Ionosonde; Reliability (semiconductor); Computer science; Software; Scaling; Radio Science; Remote sensing; Mathematics; Geology; Electron density; Physics; Ionosphere; Electron; Operating system; Geophysics","score_opus":0.004819755270568956,"score_gpt":0.23555283360740475,"score_spread":0.2307330783368358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019919221","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2813883,0.00004372675,0.71142,0.0018107992,0.0010873631,0.00009559963,0.000026008052,0.0000055962496,0.0041225813],"genre_scores_gemma":[0.96707267,6.974326e-7,0.03191003,0.00013542951,0.00074150716,3.260092e-8,0.000011404328,0.000009445674,0.00011879113],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99932134,0.000010895433,0.00024619966,0.00005218541,0.00022735409,0.00014204242],"domain_scores_gemma":[0.9991198,0.00003963424,0.0002283698,0.00008956072,0.0003977977,0.00012482172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011332699,0.00007007815,0.00009522513,0.000046286816,0.000053166325,0.000050704894,0.00018779491,0.000021521646,0.000021770955],"category_scores_gemma":[0.000030168294,0.000054568845,0.000086339634,0.00007432655,0.000018290093,0.00023421529,0.000028030947,0.00011803795,0.000017009588],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016203201,0.000012126964,0.002827882,9.29196e-7,0.000058260342,0.0000011046005,0.00027375418,0.0014559025,0.00036690108,0.0009092066,0.0002878211,0.9937899],"study_design_scores_gemma":[0.0017687641,0.00020593409,0.035364896,0.00032167096,0.000120759425,0.0002905201,0.0023515746,0.057817552,0.008086539,0.013903959,0.87906003,0.0007077702],"about_ca_topic_score_codex":0.005644594,"about_ca_topic_score_gemma":0.002494907,"teacher_disagreement_score":0.99308217,"about_ca_system_score_codex":0.000091935384,"about_ca_system_score_gemma":0.00012472099,"threshold_uncertainty_score":0.8532975},"labels":[],"label_agreement":null},{"id":"W2023738060","doi":"10.1080/01431161.2012.744491","title":"Spectral separability of riparian forests from small and medium-sized rivers across a latitudinal gradient using multispectral imagery","year":2012,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":18,"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":"McGill University","keywords":"Riparian zone; Riparian forest; Multispectral image; Environmental science; Remote sensing; Vegetation (pathology); Geography; Multispectral pattern recognition; Ecology; Mediterranean climate; Canopy; Temperate rainforest; Physical geography; Habitat; Ecosystem; Biology","score_opus":0.021077560275284277,"score_gpt":0.28015456260236304,"score_spread":0.25907700232707875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023738060","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98855597,0.000081166436,0.009304711,0.00033704523,0.0013125949,0.00009246439,0.00001376753,0.000010742959,0.00029152865],"genre_scores_gemma":[0.78719944,0.000019785713,0.21229173,0.000038628452,0.00042657595,3.0173744e-9,0.0000028719926,0.000011285294,0.00000966292],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.997989,0.00011742983,0.00060622103,0.0002128867,0.00070480164,0.00036966422],"domain_scores_gemma":[0.9986647,0.00016501112,0.0006874929,0.00014251254,0.00012494392,0.00021539125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062117976,0.00019981424,0.0003368804,0.00006329117,0.0000909323,0.00006221109,0.00023321791,0.00009841468,0.00003872799],"category_scores_gemma":[0.00026075952,0.00016228097,0.0001874874,0.00012242663,0.00037187332,0.00046776264,0.00017643318,0.0003371644,0.000005213731],"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":[0.00072723697,0.00021586388,0.37775305,0.000015245619,0.00055683136,0.00045233083,0.008728459,0.0050515877,0.50334835,0.000012841759,0.00018982841,0.10294838],"study_design_scores_gemma":[0.0012292227,0.000052619627,0.94240415,0.00018726468,0.00007610036,0.0017215281,0.00032105044,0.024418566,0.02804154,0.0010508678,0.0002709839,0.00022608727],"about_ca_topic_score_codex":0.0022004922,"about_ca_topic_score_gemma":0.0011601181,"teacher_disagreement_score":0.56465113,"about_ca_system_score_codex":0.00053663267,"about_ca_system_score_gemma":0.000030135008,"threshold_uncertainty_score":0.66176254},"labels":[],"label_agreement":null},{"id":"W2023827825","doi":"10.1080/01431160500180699","title":"Deriving terrain and textural information from stereo RADARSAT data for mountainous land cover mapping","year":2005,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Universidad de Buenos Aires","keywords":"Terrain; Remote sensing; Land cover; Digital elevation model; Elevation (ballistics); Classifier (UML); Computer science; Artificial intelligence; Cartography; Geology; Land use; Geography; Mathematics","score_opus":0.02301866619277868,"score_gpt":0.2570222357940321,"score_spread":0.23400356960125343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023827825","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36582714,0.00017792385,0.632104,0.0008079061,0.0007199835,0.0000794385,0.00003943882,0.000036171765,0.00020800241],"genre_scores_gemma":[0.71284217,0.00005946541,0.2860681,0.00015869753,0.00074588356,5.0002718e-9,0.00009917101,0.000017441902,0.0000090348485],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989032,0.00002192826,0.00051241904,0.00010542734,0.00031313422,0.00014387096],"domain_scores_gemma":[0.9990151,0.00016247008,0.000263569,0.00019479552,0.00030593853,0.000058130925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030250638,0.00013206764,0.00017038472,0.00021993944,0.000048670678,0.00028324258,0.000242929,0.00006386211,0.0000044841095],"category_scores_gemma":[0.00025642727,0.00013008463,0.000044799617,0.00004844018,0.00002873876,0.0016496342,0.000068873844,0.00017725537,0.0000067157494],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003484706,0.00000267922,0.000103547194,0.000012941454,0.00012865507,0.000010213848,0.0007790982,0.004433433,0.020988759,0.0000020119062,0.0005248364,0.97297895],"study_design_scores_gemma":[0.0006817588,0.000010347233,0.0014434594,0.00022459881,0.000023124734,0.00044459946,0.00015970836,0.91159993,0.0033131582,0.00019897196,0.081764914,0.00013543169],"about_ca_topic_score_codex":0.000060930357,"about_ca_topic_score_gemma":0.000028473873,"teacher_disagreement_score":0.9728435,"about_ca_system_score_codex":0.00022837635,"about_ca_system_score_gemma":0.000031090334,"threshold_uncertainty_score":0.5304697},"labels":[],"label_agreement":null},{"id":"W2025400581","doi":"10.1080/01431161003801302","title":"Large-scale leaf area index inversion algorithms from high-resolution airborne imagery","year":2011,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":11,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Leaf area index; Remote sensing; Normalized Difference Vegetation Index; Mathematics; Mean squared error; Correlation coefficient; Inversion (geology); Principal component analysis; Vegetation (pathology); Image resolution; Scale (ratio); Statistics; Geography; Geology; Cartography; Optics; Physics","score_opus":0.013485690553668791,"score_gpt":0.2168321540268615,"score_spread":0.2033464634731927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025400581","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82817566,0.000040296953,0.16522448,0.000652678,0.0024260136,0.000063792,0.000010115022,0.000032466432,0.0033745246],"genre_scores_gemma":[0.8057952,0.000047912337,0.19299953,0.00039703926,0.000565611,3.6248247e-9,0.000014315218,0.00001883323,0.00016156923],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99779576,0.000096708485,0.0005194362,0.00027416766,0.0010402217,0.00027371867],"domain_scores_gemma":[0.9987967,0.000059399525,0.00059039396,0.00019374039,0.0002043137,0.00015547134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003607543,0.00020109734,0.00023777984,0.00013178465,0.00010023267,0.00006607628,0.00039289828,0.00015140258,0.00040226412],"category_scores_gemma":[0.000112055866,0.00016428,0.00017806719,0.00017887916,0.00012883442,0.00053901185,0.00023713731,0.00044502693,0.00015554976],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00081401615,0.00032382942,0.009231483,0.0000058733517,0.00046534717,0.002691253,0.008922781,0.0060643745,0.11886352,0.0000112313855,0.024439437,0.82816684],"study_design_scores_gemma":[0.0031785346,0.00022220389,0.3875964,0.00070241874,0.00015010343,0.0021518518,0.0020768656,0.5219176,0.05894703,0.008496252,0.013679536,0.00088118593],"about_ca_topic_score_codex":0.0023016448,"about_ca_topic_score_gemma":0.00033665387,"teacher_disagreement_score":0.82728565,"about_ca_system_score_codex":0.0005172137,"about_ca_system_score_gemma":0.000023307015,"threshold_uncertainty_score":0.6699144},"labels":[],"label_agreement":null},{"id":"W2026203433","doi":"10.1080/01431160802555879","title":"Speed ambiguity in hurricane wind retrieval from SAR imagery","year":2009,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Ocean Waves and Remote Sensing","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":false,"ca_institutions":"Bedford Institute of Oceanography","funders":"European Space Agency; Canadian Space Agency; Chinese Academy of Sciences; National Science Foundation","keywords":"Wind speed; Synthetic aperture radar; Remote sensing; Storm; Range (aeronautics); Maximum sustained wind; Radar; Wind direction; Ambiguity; Meteorology; Computer science; Environmental science; Geology; Geography; Wind gradient; Telecommunications; Engineering","score_opus":0.01364301301212992,"score_gpt":0.24715817936194204,"score_spread":0.23351516634981212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026203433","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9918293,0.00037370186,0.0005408039,0.0022028198,0.0017933664,0.000037247668,0.000011683607,0.000010103365,0.0032009706],"genre_scores_gemma":[0.9688439,0.0001424041,0.02896506,0.00070474134,0.00126337,6.890545e-13,0.000016607408,0.0000041023613,0.000059818853],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983069,0.000084470485,0.00056507107,0.0001693709,0.0006469104,0.00022732878],"domain_scores_gemma":[0.9989851,0.00013330215,0.00038440863,0.000104470084,0.0002677354,0.0001249493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044780393,0.00013841946,0.00026617307,0.00030188047,0.000046380526,0.00009868951,0.0002363375,0.000075826494,0.000106476204],"category_scores_gemma":[0.00022839557,0.00011664334,0.00014233316,0.00019595191,0.000055761648,0.00030208426,0.000010685078,0.0003962762,0.000025276877],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044077612,0.00001669562,0.0020257055,0.0000013564523,0.000056184428,0.0027486403,0.0003238429,0.004433569,0.0053783515,0.0000012311422,0.00025630393,0.98431736],"study_design_scores_gemma":[0.0021268497,0.00032078841,0.47809005,0.0007128186,0.000047410285,0.0028086235,0.000629559,0.49405357,0.005834581,0.011814632,0.0030736602,0.0004874542],"about_ca_topic_score_codex":0.0013156004,"about_ca_topic_score_gemma":0.00012980968,"teacher_disagreement_score":0.9838299,"about_ca_system_score_codex":0.000040141018,"about_ca_system_score_gemma":0.000086334054,"threshold_uncertainty_score":0.47565773},"labels":[],"label_agreement":null},{"id":"W2026327286","doi":"10.1080/01431160152518642","title":"Application of vertical skyward wide-angle photography and airborne video data for phenological studies of beech forests in the German Alps","year":2001,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":13,"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; Jenny ja Antti Wihurin Rahasto; Academy of Finland","keywords":"Beech; Canopy; Environmental science; Remote sensing; Phenology; Photosynthetically active radiation; Tree canopy; Geography; Forestry; Ecology","score_opus":0.0341578715131999,"score_gpt":0.32181236475444824,"score_spread":0.28765449324124837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026327286","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9643433,0.00017948975,0.033374667,0.0016863126,0.00013091219,0.00016209674,0.0000050147314,0.0000036685285,0.00011454988],"genre_scores_gemma":[0.9676288,0.00015311393,0.031960428,0.00016149617,0.000080029524,2.962398e-8,0.0000075727994,0.0000055605688,0.0000029934654],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99864614,0.00007076491,0.00048338654,0.00017075363,0.0005069978,0.00012193125],"domain_scores_gemma":[0.99888766,0.0004317431,0.0002942908,0.00020954461,0.00014554521,0.000031243635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068406574,0.00009735992,0.00022088904,0.00007993658,0.000032896685,0.000016859023,0.00044167656,0.0000593099,0.0000023298144],"category_scores_gemma":[0.00051925605,0.000060660277,0.00007095969,0.00018413481,0.00028178334,0.00017113386,0.00017956475,0.00015288805,7.674267e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000738554,0.00023007543,0.036999915,0.000033137803,0.0003652727,0.0001432634,0.0029592935,0.0012908921,0.114780135,0.00012207472,0.0022239378,0.84011346],"study_design_scores_gemma":[0.0025547207,0.00063079316,0.73279566,0.00057493185,0.0002072284,0.002809708,0.0019860535,0.20646326,0.017518107,0.024772597,0.009312554,0.0003743915],"about_ca_topic_score_codex":0.00011761055,"about_ca_topic_score_gemma":0.00029382462,"teacher_disagreement_score":0.8397391,"about_ca_system_score_codex":0.00006092395,"about_ca_system_score_gemma":0.0000078998355,"threshold_uncertainty_score":0.24736543},"labels":[],"label_agreement":null},{"id":"W2028919806","doi":"10.1080/01431161.2013.871085","title":"Spectral unmixing of multiple lichen species and underlying substrate","year":2014,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Lichen and fungal ecology","field":"Agricultural and Biological Sciences","cited_by":14,"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 Winnipeg","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta; University of Winnipeg","keywords":"Lichen; Crustose; Environmental science; Algae; Remote sensing; Substrate (aquarium); Vegetation (pathology); Abundance (ecology); Cyanobacteria; Ecology; Biology; Geology","score_opus":0.037186949577235916,"score_gpt":0.2516744570689042,"score_spread":0.21448750749166826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028919806","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9956043,0.000051935287,0.0011487993,0.0014065036,0.0003955525,0.000020583613,0.0000018154622,0.000005721725,0.0013648362],"genre_scores_gemma":[0.99059016,0.000040943807,0.008717678,0.00012521849,0.00047529978,2.3712405e-9,0.0000016742374,7.5946065e-7,0.000048264454],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99929726,0.0000481189,0.00027819083,0.00007858566,0.00019198048,0.0001058651],"domain_scores_gemma":[0.99913543,0.00030636872,0.0002741964,0.000017466715,0.00021561528,0.000050905775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029909328,0.00006617618,0.00015019353,0.000028921648,0.000041923315,0.00004322709,0.00012854477,0.00004380698,0.000025557125],"category_scores_gemma":[0.00019059442,0.000029548264,0.000070052614,0.000055468376,0.00005556608,0.00010594066,0.00003539899,0.000119532575,0.0000013835537],"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":[0.00006123679,0.000012552495,0.0077322787,0.000003053594,0.000058309397,0.000021316862,0.00021316488,0.000025757681,0.66921026,0.0002845048,0.000023765338,0.32235384],"study_design_scores_gemma":[0.00093122263,0.00055636326,0.88174427,0.00023149769,0.000039976025,0.0010432246,0.0012664661,0.022729695,0.07462436,0.008117571,0.008442156,0.00027322848],"about_ca_topic_score_codex":0.00011519389,"about_ca_topic_score_gemma":0.00033982357,"teacher_disagreement_score":0.87401193,"about_ca_system_score_codex":0.00002337966,"about_ca_system_score_gemma":0.0000071819013,"threshold_uncertainty_score":0.12049433},"labels":[],"label_agreement":null},{"id":"W2029508794","doi":"10.1080/01431160902906162","title":"Analysis of the factors exciting the ocean–atmosphere heat interaction in the North Atlantic using satellite and vessel data","year":2010,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Oceanic and Atmospheric Administration; State Space Corporation ROSCOSMOS; National Aeronautics and Space Administration","keywords":"Atmosphere (unit); Environmental science; Defense Meteorological Satellite Program; Satellite; Atmospheric sciences; Brightness temperature; Sea surface temperature; Planetary boundary layer; Latitude; Meteorology; Remote sensing; Microwave; Climatology; Geology; Physics","score_opus":0.04896303241238461,"score_gpt":0.28267029088297674,"score_spread":0.23370725847059212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029508794","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9977215,0.00007883852,0.00078419974,0.0007369889,0.0004910503,0.000041470717,0.00001161048,0.0000014232432,0.00013289707],"genre_scores_gemma":[0.998411,0.00004067105,0.0012521051,0.00015446715,0.00011822335,4.845167e-10,0.000020379422,0.0000014037396,0.0000017369625],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989732,0.00015709964,0.00034329505,0.00009322159,0.00034660328,0.0000865834],"domain_scores_gemma":[0.99862134,0.00078738,0.00026862638,0.00018167164,0.00011668728,0.00002430177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006282637,0.00006782188,0.0001341392,0.00005604264,0.000114142545,0.0000948442,0.00046931193,0.00002677359,0.000038759325],"category_scores_gemma":[0.00030653054,0.000029810193,0.00007201132,0.00032191322,0.00008367022,0.0002680751,0.000035548554,0.00032903248,2.922739e-7],"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":[0.000021173262,0.000004551371,0.9053098,0.0000014250807,0.0001621182,0.000008414227,0.0006254119,0.06485358,0.00021783203,0.0000054947377,0.0000034113048,0.028786762],"study_design_scores_gemma":[0.00004928203,0.000006486566,0.5780025,0.000013594262,0.00008004786,0.00005410485,0.00037958106,0.42099556,0.0000048117663,0.00015952719,0.0002317731,0.000022720253],"about_ca_topic_score_codex":0.0022746988,"about_ca_topic_score_gemma":0.005841597,"teacher_disagreement_score":0.35614198,"about_ca_system_score_codex":0.0000049306095,"about_ca_system_score_gemma":0.000022614013,"threshold_uncertainty_score":0.34386793},"labels":[],"label_agreement":null},{"id":"W2029696640","doi":"10.1080/01431160512331314100","title":"Prioritizing ocean colour channels by neural network input reflectance perturbation","year":2005,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Québec Metro High Tech Park (Canada)","funders":"","keywords":"Reflectivity; Remote sensing; Artificial neural network; Radiative transfer; Environmental science; Spectral line; Multilayer perceptron; Atmospheric correction; Atmospheric radiative transfer codes; Chlorophyll a; Biological system; Computer science; Geology; Chemistry; Optics; Physics; Artificial intelligence; Biology","score_opus":0.011480832301882534,"score_gpt":0.23277682700414398,"score_spread":0.22129599470226144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029696640","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9801227,0.000855337,0.004532093,0.0035269638,0.003316002,0.00006783866,0.000008291308,0.000021590338,0.0075492123],"genre_scores_gemma":[0.9881014,0.000095187315,0.0070786043,0.00080879097,0.0033576675,1.2455221e-9,0.000016870952,0.0000047871017,0.0005366686],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873537,0.00006551149,0.00041719325,0.00011584104,0.00046849027,0.00019757384],"domain_scores_gemma":[0.9990776,0.000090454734,0.00037500085,0.00005982327,0.0003055178,0.0000915767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037241832,0.000104909865,0.0001589284,0.00008845411,0.0000838122,0.00015448278,0.00020294123,0.000047341193,0.00006075892],"category_scores_gemma":[0.00007747866,0.000092113696,0.00008259933,0.00010685767,0.000018116418,0.0004040237,0.000015251134,0.00021696955,0.000017308625],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012206399,0.0000053660683,0.0017906098,0.0000044114145,0.00005547786,0.00013103393,0.0001938069,0.019866334,0.00041811215,0.0000105870895,0.004970757,0.9724314],"study_design_scores_gemma":[0.00047564376,0.00014746407,0.0016050336,0.00016273712,0.00001432475,0.0023466654,0.00008089432,0.9167868,0.00025170276,0.00088286743,0.077064596,0.00018129885],"about_ca_topic_score_codex":0.00019630924,"about_ca_topic_score_gemma":0.00042490746,"teacher_disagreement_score":0.97225016,"about_ca_system_score_codex":0.000028964923,"about_ca_system_score_gemma":0.00004416648,"threshold_uncertainty_score":0.37562874},"labels":[],"label_agreement":null},{"id":"W2030909241","doi":"10.1080/01431160902825016","title":"Global long-term monitoring of the ozone layer – a prerequisite for predictions","year":2009,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Atmospheric Ozone and Climate","field":"Earth and Planetary Sciences","cited_by":72,"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":"SCIAMACHY; Environmental science; Ozone layer; Satellite; Ozone; Montreal Protocol; Meteorology; Term (time); Atmospheric sciences; Climatology; Geography; Troposphere; Geology","score_opus":0.0200232165860954,"score_gpt":0.28112638936222795,"score_spread":0.26110317277613254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030909241","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9590096,0.00027948676,0.036964376,0.0008066796,0.0018569542,0.00006233661,0.000022714266,0.000006497678,0.0009913594],"genre_scores_gemma":[0.97817874,0.00006224495,0.020937914,0.00009440827,0.0006716804,1.8962703e-9,0.0000019521524,0.0000015737446,0.000051494186],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99911755,0.000022100328,0.000309866,0.000069485024,0.00036540214,0.00011558803],"domain_scores_gemma":[0.99918944,0.000050361585,0.00033412845,0.000075228,0.00030475712,0.00004606475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016298066,0.00006803661,0.0001145551,0.00001559083,0.00006448516,0.000040636583,0.00021862761,0.00003410462,0.000028193894],"category_scores_gemma":[0.00006331329,0.00004640287,0.00014519888,0.00009725225,0.000028631775,0.00017218826,0.000008220648,0.000078795405,0.000001797498],"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":[0.00017194446,0.000014096323,0.38487527,0.0000052975347,0.0001186648,0.0000312007,0.00013835065,0.0066464576,0.00046970684,0.000012110146,0.00009424231,0.60742265],"study_design_scores_gemma":[0.00039622828,0.00013166621,0.9838474,0.00021055852,0.000041947664,0.00049817347,0.00006547031,0.012725489,0.00066739036,0.0011452195,0.0002134806,0.000056990644],"about_ca_topic_score_codex":0.00007938146,"about_ca_topic_score_gemma":0.0000624926,"teacher_disagreement_score":0.60736567,"about_ca_system_score_codex":0.000019227295,"about_ca_system_score_gemma":0.000050188857,"threshold_uncertainty_score":0.18922542},"labels":[],"label_agreement":null},{"id":"W2031860171","doi":"10.1080/01431160410001716923","title":"Mapping insect‐induced tree defoliation and mortality using coarse spatial resolution satellite imagery","year":2005,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":93,"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":"Vegetation (pathology); Satellite imagery; Environmental science; Remote sensing; Spatial ecology; Satellite; Aerial imagery; Physical geography; Normalized Difference Vegetation Index; Geography; Ecology; Biology; Climate change","score_opus":0.03548617583448088,"score_gpt":0.28509658413276295,"score_spread":0.24961040829828207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031860171","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8564863,0.00004820131,0.14065833,0.0007158731,0.00036072152,0.0000654055,0.0000013601593,0.000015630007,0.0016481734],"genre_scores_gemma":[0.8763529,0.00006276056,0.12274695,0.00018011528,0.000617001,5.573627e-9,0.0000033660292,0.000013279488,0.000023638344],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984431,0.000087486296,0.0005256088,0.00019453063,0.0005757594,0.00017346947],"domain_scores_gemma":[0.99907744,0.000056487883,0.000500788,0.00013745479,0.00012724253,0.000100563804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005429274,0.00013130705,0.00016476959,0.00016015758,0.00012823609,0.00010220184,0.00013044229,0.000073121424,0.000019617586],"category_scores_gemma":[0.00011664177,0.00013006976,0.00009147073,0.00014336519,0.000092002716,0.0003882553,0.00008067694,0.00022185022,0.0000184661],"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":[0.00002804043,0.000014802469,0.00093583076,0.0000015540111,0.000036579524,0.000025800087,0.00053236546,0.0018995925,0.20343922,0.0000038867283,0.000029689145,0.7930527],"study_design_scores_gemma":[0.0008170873,0.000041220002,0.110920034,0.00017547562,0.000055170945,0.0017183525,0.00024391885,0.8582232,0.01965472,0.0010470297,0.00681528,0.00028851858],"about_ca_topic_score_codex":0.0015938249,"about_ca_topic_score_gemma":0.0005789579,"teacher_disagreement_score":0.8563236,"about_ca_system_score_codex":0.00045998496,"about_ca_system_score_gemma":0.000034219654,"threshold_uncertainty_score":0.5304091},"labels":[],"label_agreement":null},{"id":"W2033038277","doi":"10.1080/01431160600868474","title":"A semi‐automated approach for extracting buildings from QuickBird imagery applied to informal settlement mapping","year":2007,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Settlement (finance); Computer science; Remote sensing; Satellite; Ground truth; Software; Satellite imagery; Information extraction; High resolution; Extraction (chemistry); Image resolution; Artificial intelligence; Geology; Engineering","score_opus":0.012681318991298648,"score_gpt":0.2616631676780507,"score_spread":0.24898184868675205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033038277","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3563805,0.000032028365,0.64091796,0.00006623942,0.0011848374,0.00011801418,0.0000092574055,0.00028940054,0.0010017877],"genre_scores_gemma":[0.6073524,0.000008299738,0.39159018,0.0001316764,0.000854094,7.890981e-8,0.000022493914,0.000033194356,0.0000076215542],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981917,0.000008184679,0.0008183662,0.00014747868,0.00050549343,0.00032874508],"domain_scores_gemma":[0.99886,0.0001857078,0.00036858354,0.00009442772,0.00036107912,0.00013022442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083431846,0.00019619038,0.00025843378,0.0005391463,0.000079785044,0.00014926735,0.00021435156,0.00011156748,0.0000053472886],"category_scores_gemma":[0.00010759913,0.00019808815,0.00013743302,0.00017879528,0.0000138177265,0.00037335392,0.000041812422,0.00031863773,0.000005395769],"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":[0.00016635914,0.00001678265,0.000030334644,0.000030361127,0.00031362427,0.000052838303,0.0012473721,0.07771478,0.4612505,0.000010346508,0.0014096976,0.457757],"study_design_scores_gemma":[0.0009299117,0.00003069428,0.0009797714,0.00033698702,0.00004122599,0.00050905475,0.0013859913,0.88414687,0.096771345,0.00006945347,0.014480917,0.00031776776],"about_ca_topic_score_codex":0.000031289932,"about_ca_topic_score_gemma":0.0000032516148,"teacher_disagreement_score":0.8064321,"about_ca_system_score_codex":0.00030971415,"about_ca_system_score_gemma":0.00003103656,"threshold_uncertainty_score":0.80778},"labels":[],"label_agreement":null},{"id":"W2034671455","doi":"10.1080/01431160050030592","title":"Destriping multisensor imagery with moment matching","year":2000,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":255,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Golder Associates (Canada); University of Toronto","funders":"Delta Waterfowl; University of Toronto","keywords":"Histogram; Outlier; Histogram matching; Moment (physics); Artificial intelligence; Matching (statistics); Computer science; Offset (computer science); Computer vision; Histogram equalization; Pattern recognition (psychology); Mathematics; Statistics; Image (mathematics)","score_opus":0.016322810769120375,"score_gpt":0.28357570350057043,"score_spread":0.26725289273145003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034671455","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17503364,0.00008306178,0.82144856,0.0012200631,0.00055569725,0.000029177607,2.9180276e-7,0.000024619012,0.0016048619],"genre_scores_gemma":[0.25116915,0.00003363825,0.7477352,0.00048159092,0.00032517215,4.9635633e-9,2.776712e-7,0.000009797192,0.00024515274],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845123,0.00011513825,0.00039318882,0.0001598341,0.000694084,0.00018654659],"domain_scores_gemma":[0.99888355,0.00013516478,0.00026397023,0.00016170218,0.00046953282,0.00008609346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005837573,0.00012289878,0.00018175397,0.00022825364,0.000076910896,0.00036804978,0.00051952765,0.00003127248,0.00001895787],"category_scores_gemma":[0.000039504386,0.00009823178,0.00010218928,0.00014788753,0.000033234355,0.00072151027,0.00004965118,0.00024712586,0.000015462627],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000098584016,0.000012099669,0.000006827592,0.0000022844406,0.00007003019,0.0018677992,0.0005343209,0.0022542235,0.01600451,0.000060604743,0.000057452406,0.97903126],"study_design_scores_gemma":[0.009428496,0.00067779195,0.0031024911,0.0037117726,0.00011696625,0.0654286,0.00043451006,0.69356656,0.16666134,0.023552323,0.031903446,0.0014156749],"about_ca_topic_score_codex":0.000056341483,"about_ca_topic_score_gemma":0.0000018675053,"teacher_disagreement_score":0.9776156,"about_ca_system_score_codex":0.00011526717,"about_ca_system_score_gemma":0.00008762767,"threshold_uncertainty_score":0.40057757},"labels":[],"label_agreement":null},{"id":"W2035179364","doi":"10.1080/01431161.2010.483485","title":"Evaluation of soil moisture derived from passive microwave remote sensing over agricultural sites in Canada using ground-based soil moisture monitoring networks","year":2010,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":61,"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 Guelph; Agriculture and Agri-Food Canada","funders":"","keywords":"Environmental science; Water content; Remote sensing; Radiometer; Atmospheric sciences; Meteorology; Geology; Geography","score_opus":0.014230694535442642,"score_gpt":0.24592952975900306,"score_spread":0.2316988352235604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035179364","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98415273,0.00016416873,0.01097147,0.00035106245,0.0037456232,0.0001432323,0.0000035849785,0.000010425181,0.0004576843],"genre_scores_gemma":[0.94750696,0.000017308641,0.05085217,0.00017078819,0.0013960613,4.0714525e-9,0.000016327935,0.00003470308,0.0000056569706],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99576336,0.00028844672,0.0009290174,0.00038744605,0.0022386708,0.0003930336],"domain_scores_gemma":[0.99727064,0.00027620787,0.0012264403,0.00024808105,0.0008185764,0.00016002767],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008185489,0.0003567991,0.0004711456,0.00017017158,0.00014055728,0.00011711042,0.0003173126,0.00024266457,0.000016211483],"category_scores_gemma":[0.00044037975,0.00030485526,0.0002180112,0.0003141919,0.0001452071,0.00033479417,0.00013124073,0.001059869,0.000001288992],"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":[0.00006522065,0.000008874912,0.0048457836,0.0000028867616,0.000114521215,0.00021732415,0.00034993768,0.18405557,0.52210957,1.0963983e-7,0.000035928868,0.2881943],"study_design_scores_gemma":[0.0010970713,0.000015916237,0.34446114,0.00050030375,0.00017515726,0.00039654947,0.0006657429,0.5807951,0.0712983,0.0002836183,0.000024021409,0.0002870558],"about_ca_topic_score_codex":0.80625695,"about_ca_topic_score_gemma":0.9258513,"teacher_disagreement_score":0.45081124,"about_ca_system_score_codex":0.0033025777,"about_ca_system_score_gemma":0.0007048002,"threshold_uncertainty_score":0.99994034},"labels":[],"label_agreement":null},{"id":"W2035518358","doi":"10.1080/0143116031000102421","title":"Evaluation of the runoff potential in high relief semi-arid regions using remote sensing data: application to Bolivia","year":2004,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; Natural Resources Canada","funders":"","keywords":"Arid; Remote sensing; Surface runoff; Watershed; Environmental science; Hydrology (agriculture); Land cover; Vegetation (pathology); Scale (ratio); Land use; Radar; Cartography; Geography; Computer science; Geology; Civil engineering","score_opus":0.02381917302609505,"score_gpt":0.31004301352961816,"score_spread":0.2862238405035231,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035518358","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2923385,0.00008646372,0.70578957,0.00063713244,0.0008482531,0.00018238672,0.0000057440056,0.00003458286,0.000077392186],"genre_scores_gemma":[0.67794293,0.000059006892,0.32158324,0.000069714995,0.0003086807,9.365153e-9,0.0000056009585,0.000028299417,0.000002537625],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974794,0.000102243066,0.0007641474,0.00019685666,0.0012787434,0.00017865737],"domain_scores_gemma":[0.99764377,0.0000463489,0.00043818864,0.00047578503,0.0013366701,0.00005922585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012548963,0.00016109264,0.00023465951,0.0004276239,0.000057024998,0.000046186517,0.00049087603,0.000087642155,0.000002499977],"category_scores_gemma":[0.00053326867,0.00014930335,0.000087014414,0.0003318772,0.00004514487,0.0004189505,0.0001973572,0.00037593747,0.000002421169],"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":[0.000018493889,0.0000065131453,0.0000018647255,0.000005020954,0.0000380398,0.000020386517,0.00013078016,0.40376806,0.29563972,0.000012654808,0.000055168024,0.3003033],"study_design_scores_gemma":[0.0006958902,0.000015580325,0.00041377067,0.0008029039,0.000090525726,0.00080464844,0.00007725432,0.8895401,0.0919961,0.015050497,0.00035656898,0.00015616757],"about_ca_topic_score_codex":0.0006306496,"about_ca_topic_score_gemma":0.00020116243,"teacher_disagreement_score":0.48577204,"about_ca_system_score_codex":0.00094381784,"about_ca_system_score_gemma":0.00018050503,"threshold_uncertainty_score":0.60884136},"labels":[],"label_agreement":null},{"id":"W2038093066","doi":"10.1080/01431161003621593","title":"An image fusion method taking into account phenological analogies and haze","year":2011,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"York University","funders":"National Natural Science Foundation of China","keywords":"Pixel; Panchromatic film; Image fusion; Image resolution; Artificial intelligence; Computer vision; Haze; Computer science; Fusion; Remote sensing; Image (mathematics); Geography; Meteorology","score_opus":0.026151534314803493,"score_gpt":0.32241622287901034,"score_spread":0.29626468856420685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038093066","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15645726,0.00017970163,0.840897,0.00008509542,0.0004306194,0.00003370334,8.947732e-7,0.00013521544,0.0017805067],"genre_scores_gemma":[0.40723908,0.000178297,0.5923766,0.000053397627,0.00013414831,1.1279071e-8,7.0761854e-7,0.000014399483,0.000003359831],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99904823,0.000046879795,0.00036871288,0.000119097145,0.0002848356,0.0001322704],"domain_scores_gemma":[0.99913615,0.000051346826,0.00023427788,0.00011156146,0.0003990827,0.000067604546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034600124,0.00013497239,0.00019091532,0.00026976655,0.00004233535,0.0000728444,0.00023723906,0.000072730494,0.000049796603],"category_scores_gemma":[0.00021949288,0.00011586495,0.000059423484,0.00007104293,0.00005996374,0.0006170114,0.00008073653,0.00028980518,0.0000019879615],"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":[0.000031775926,0.000006636301,0.000014959433,0.0000045849024,0.00003280083,0.0002405549,0.00066631206,0.00013301875,0.29379442,0.000037584698,0.000052802112,0.70498455],"study_design_scores_gemma":[0.0007579797,0.00031147094,0.0043863333,0.0005000293,0.000055536824,0.0043087536,0.0015037167,0.37737983,0.56000483,0.047375336,0.0028680703,0.0005480886],"about_ca_topic_score_codex":0.000050192586,"about_ca_topic_score_gemma":0.000009929323,"teacher_disagreement_score":0.7044365,"about_ca_system_score_codex":0.00010323061,"about_ca_system_score_gemma":0.000012303154,"threshold_uncertainty_score":0.47248352},"labels":[],"label_agreement":null},{"id":"W2038739959","doi":"10.1080/01431160903130960","title":"Geostatistical approaches to conflation of continental snow data","year":2009,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","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 Waterloo","funders":"Goddard Space Flight Center; National Geospatial-Intelligence Agency","keywords":"Conflation; Snow; Radiometer; Geostatistics; Environmental science; Snow cover; Variogram; Remote sensing; Sampling (signal processing); Spatial analysis; Primary (astronomy); Meteorology; Spatial variability; Computer science; Geography; Statistics; Kriging; Mathematics; Detector","score_opus":0.13160478116293195,"score_gpt":0.28515294255598256,"score_spread":0.1535481613930506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038739959","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6860025,0.00026688317,0.29984856,0.010602107,0.0012618576,0.000087718145,0.00013453681,0.000007686474,0.0017881367],"genre_scores_gemma":[0.86243427,0.00002588313,0.13680589,0.00035385025,0.00029364356,3.816362e-10,0.0000646455,0.0000011446086,0.000020651638],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99913025,0.000019900608,0.00033021433,0.00008675114,0.00035215518,0.00008074355],"domain_scores_gemma":[0.9992893,0.00015053217,0.00020799624,0.00009172072,0.00020473522,0.000055721663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002389958,0.000055953496,0.00012875494,0.00004953544,0.000033265496,0.000032566786,0.00023248816,0.000019218898,0.00007328238],"category_scores_gemma":[0.000344808,0.000046450827,0.000034173907,0.00007914287,0.000027380664,0.00019490795,0.000017375607,0.000072901996,0.00000576221],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009464625,0.000008433935,0.00405472,0.0000011840528,0.000051973664,0.000032472853,0.0001877387,0.0019022961,0.00010980896,0.00016000491,0.0008489311,0.9925478],"study_design_scores_gemma":[0.00044264388,0.00017797716,0.6961757,0.00011049768,0.000031007214,0.0002379379,0.0004463968,0.28736496,0.00018724038,0.0014966264,0.013226416,0.00010262275],"about_ca_topic_score_codex":0.00032579145,"about_ca_topic_score_gemma":0.00022471148,"teacher_disagreement_score":0.9924452,"about_ca_system_score_codex":0.000006392078,"about_ca_system_score_gemma":0.00003081505,"threshold_uncertainty_score":0.18942097},"labels":[],"label_agreement":null},{"id":"W2040014370","doi":"10.1080/01431161.2013.828183","title":"Estimation of foliar pigment concentration in floating macrophytes using hyperspectral vegetation indices","year":2013,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":11,"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","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hyperspectral imaging; Spectroradiometer; Macrophyte; Vegetation (pathology); Pigment; Environmental science; Photosynthetic pigment; Chlorophyll; Wetland; Aquatic plant; Chlorophyll a; Botany; Reflectivity; Ecology; Chemistry; Remote sensing; Biology; Geography","score_opus":0.008587533752918377,"score_gpt":0.24708373408691978,"score_spread":0.2384962003340014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040014370","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9587323,0.000049232553,0.03955442,0.00030216385,0.0005444532,0.00011767856,3.5144365e-7,0.0000062289414,0.00069314556],"genre_scores_gemma":[0.7700926,0.000013206878,0.22971562,0.000046925514,0.000115994066,4.6513366e-9,0.0000013262454,0.000007032127,0.000007296939],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983749,0.000075394586,0.0006054078,0.00012703774,0.00066966296,0.00014764664],"domain_scores_gemma":[0.9988944,0.00006617749,0.00077711075,0.00006567948,0.0001496352,0.000047031564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002676361,0.000112329544,0.0001697268,0.00010490891,0.00003603441,0.00006947115,0.00014697533,0.00006185956,0.000032013802],"category_scores_gemma":[0.00014310163,0.000096862634,0.0000729525,0.00018097862,0.000074083124,0.0006309006,0.00003910485,0.00017349406,0.000012721107],"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":[0.000019739733,0.00002335368,0.0016572499,0.0000073189485,0.000029951903,0.000029171457,0.0015869853,0.2134443,0.4222938,0.00001240134,0.000021279793,0.3608744],"study_design_scores_gemma":[0.00049998116,0.00004824241,0.032953344,0.00041467295,0.000017413038,0.00031894573,0.00040410517,0.8590697,0.10417052,0.0019691451,0.000015874733,0.0001180299],"about_ca_topic_score_codex":0.0011312944,"about_ca_topic_score_gemma":0.00006921345,"teacher_disagreement_score":0.6456254,"about_ca_system_score_codex":0.00050197006,"about_ca_system_score_gemma":0.00002504687,"threshold_uncertainty_score":0.39499435},"labels":[],"label_agreement":null},{"id":"W2041149041","doi":"10.1080/01431161.2010.531787","title":"A wavelet domain detail compensation filtering technique for InSAR interferograms","year":2011,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Interferometric synthetic aperture radar; Wavelet; Computer science; Speckle noise; Wavelet transform; Artificial intelligence; Computer vision; Speckle pattern; Synthetic aperture radar; Remote sensing; Domain (mathematical analysis); Compensation (psychology); Noise (video); Geology; Algorithm; Mathematics; Image (mathematics)","score_opus":0.022831913682679505,"score_gpt":0.2482159898036514,"score_spread":0.2253840761209719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041149041","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018637272,0.000053255895,0.978478,0.000144642,0.00044222994,0.00021099541,0.0000048477027,0.00010399979,0.0019247634],"genre_scores_gemma":[0.39484254,0.00002825642,0.60490465,0.00003894164,0.00015412802,3.2919996e-7,0.0000031739785,0.000023096409,0.000004895867],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990758,0.000017389286,0.00046122883,0.000099063305,0.0002059577,0.00014061747],"domain_scores_gemma":[0.9992073,0.00006706109,0.00018143868,0.00013123995,0.00035978717,0.000053154108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003109782,0.00013739285,0.00018513028,0.00023289192,0.00003626571,0.00004046898,0.00025349847,0.0000848307,0.000016019008],"category_scores_gemma":[0.000042870863,0.00012904833,0.00013995016,0.00007607135,0.000036793,0.00013542568,0.000029761744,0.00017960594,0.0000024054339],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042502183,0.000013839063,0.000005441614,0.000012168062,0.00011069901,0.00002171906,0.00045157,0.0000033034805,0.04638231,0.0003980611,0.0001478106,0.9524106],"study_design_scores_gemma":[0.0006679922,0.00017298077,0.00013675028,0.00061054056,0.00004589819,0.0023372222,0.00028219228,0.046258654,0.6679576,0.02558575,0.25558582,0.0003586046],"about_ca_topic_score_codex":0.000020005462,"about_ca_topic_score_gemma":0.000008567028,"teacher_disagreement_score":0.952052,"about_ca_system_score_codex":0.00014587346,"about_ca_system_score_gemma":0.00001758288,"threshold_uncertainty_score":0.5262438},"labels":[],"label_agreement":null},{"id":"W2041729449","doi":"10.1080/01431161.2010.512306","title":"A divide-and-conquer approach to contour extraction and invariant feature analysis","year":2010,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Image Retrieval and Classification Techniques","field":"Computer 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":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Education, India; Ministry of Earth Sciences","keywords":"Artificial intelligence; Computer vision; Computer science; Pattern recognition (psychology); Corner detection; Feature extraction; Invariant (physics); Curvature; Robustness (evolution); Spatial analysis; Mathematics; Remote sensing; Geography; Image (mathematics); Geometry","score_opus":0.014247457672089209,"score_gpt":0.2821918169356526,"score_spread":0.2679443592635634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041729449","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03316578,0.0000437101,0.95930094,0.006406454,0.00032584276,0.00004372844,7.9879476e-7,0.000022266246,0.0006904988],"genre_scores_gemma":[0.6343573,0.000024599178,0.36509016,0.00029404636,0.00014598761,1.6526615e-8,5.975353e-7,0.0000028155712,0.00008448171],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914193,0.000036818325,0.00022624547,0.00015849402,0.00035253758,0.00008396099],"domain_scores_gemma":[0.99887425,0.00006804969,0.00024958854,0.00012494739,0.00058135256,0.00010181772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049988384,0.0000806425,0.00015121701,0.00039548054,0.000049832746,0.0003260979,0.00025859382,0.00006267424,0.0000016618021],"category_scores_gemma":[0.00019078799,0.000065522174,0.00007194655,0.00025294747,0.000030382162,0.0004331246,0.00008948219,0.00029947687,9.574425e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028221437,0.000020875561,0.0001320419,0.0000030473966,0.00027773422,0.000047860918,0.000499868,0.000008800958,0.12853514,0.0044755335,0.00020985772,0.86576104],"study_design_scores_gemma":[0.0014044065,0.0002125283,0.0593986,0.00017712684,0.0004534123,0.011630268,0.00019251174,0.6993747,0.145912,0.025373973,0.05509057,0.0007799057],"about_ca_topic_score_codex":0.00004255326,"about_ca_topic_score_gemma":0.000012018914,"teacher_disagreement_score":0.8649811,"about_ca_system_score_codex":0.000027958953,"about_ca_system_score_gemma":0.000035192963,"threshold_uncertainty_score":0.31445697},"labels":[],"label_agreement":null},{"id":"W2042384303","doi":"10.1080/01431160512331316423","title":"Remote sensing of chemical vapours by differential FTIR radiometry","year":2005,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Spectroscopy and Laser Applications","field":"Chemistry","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":"Defence Research and Development Canada","funders":"Defence Research and Development Canada","keywords":"Vapours; Remote sensing; Interferometry; Environmental science; Radiometry; Radiometer; Trace gas; Fourier transform infrared spectroscopy; Optics; Infrared; Spectrometer; Materials science; Analytical Chemistry (journal); Physics; Chemistry; Meteorology; Geology; Environmental chemistry","score_opus":0.008882379820123657,"score_gpt":0.27984980477821125,"score_spread":0.2709674249580876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042384303","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9018017,0.0001858637,0.09322924,0.0017038909,0.00036379325,0.000025125111,0.000018129884,0.00002469924,0.0026475599],"genre_scores_gemma":[0.9110174,0.00005891032,0.087044425,0.00011894247,0.0015457952,2.4410582e-9,0.000019442341,0.000027214488,0.00016784154],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981716,0.000021457818,0.0007076097,0.00018532535,0.00069807644,0.00021592282],"domain_scores_gemma":[0.9983853,0.0001490199,0.00066022883,0.00020106738,0.00047431974,0.00013003057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015734416,0.00017535554,0.0003053161,0.00021001478,0.000041586853,0.00005521325,0.00032852017,0.00013283578,0.00020093635],"category_scores_gemma":[0.00015791245,0.00017425278,0.00024756152,0.00014491461,0.000097285185,0.0001315515,0.00006161521,0.000448414,0.000007985101],"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":[0.00009482984,0.00002424306,0.000005981558,0.0000073898286,0.00015077682,0.000023232173,0.00009913951,0.00002685061,0.6880359,0.000009453859,0.00087251386,0.31064966],"study_design_scores_gemma":[0.00084359763,0.000013197064,0.00001484958,0.00022777323,0.000066846784,0.001159564,0.000079704485,0.05822474,0.9303155,0.00042662045,0.008475519,0.00015210256],"about_ca_topic_score_codex":0.000072723175,"about_ca_topic_score_gemma":0.000004301113,"teacher_disagreement_score":0.31049755,"about_ca_system_score_codex":0.0002878578,"about_ca_system_score_gemma":0.00007682387,"threshold_uncertainty_score":0.7105822},"labels":[],"label_agreement":null},{"id":"W2043205560","doi":"10.1080/01431161003782072","title":"Variabilité saisonnière et interannuelle de la concentration de la chlorophylle dans la zone côtière du golfe de Guinée à partir des images SeaWiFS","year":2011,"lang":"fr","type":"article","venue":"International Journal of Remote Sensing","topic":"Oceanographic and Atmospheric Processes","field":"Earth and Planetary Sciences","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":"Fisheries and Oceans Canada","funders":"","keywords":"Forestry; Geography; Humanities; Art","score_opus":0.010228895465156634,"score_gpt":0.23641105403508134,"score_spread":0.2261821585699247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043205560","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8332307,0.002926676,0.14636908,0.0011133788,0.00074336387,0.000050837043,0.000029220502,0.000022057506,0.015514714],"genre_scores_gemma":[0.8636076,0.0032284814,0.13196819,0.0003183309,0.00059230893,1.9991463e-8,0.000007750461,0.000014584509,0.00026272272],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9974002,0.0010093438,0.00056389946,0.00020435991,0.00041697934,0.0004051671],"domain_scores_gemma":[0.9976433,0.0010528646,0.00045114392,0.00010513993,0.00048597765,0.00026160447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002375356,0.0002324856,0.0002747557,0.000053705175,0.0001614855,0.0003582477,0.00039368006,0.00020881812,0.00031366106],"category_scores_gemma":[0.0007675951,0.00021637537,0.0002007529,0.00020310748,0.00093588006,0.0007254265,0.00003936086,0.00056418835,0.00001059673],"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":[0.0007877481,0.00027861405,0.36278027,0.0001649637,0.0006662885,0.0038640655,0.058469914,0.009444073,0.0066367313,0.0008646407,0.0015123549,0.5545303],"study_design_scores_gemma":[0.0034591162,0.00087834237,0.47248524,0.0038294052,0.0005272921,0.036312062,0.017167542,0.31503293,0.027198983,0.04570257,0.07628389,0.001122621],"about_ca_topic_score_codex":0.0050096367,"about_ca_topic_score_gemma":0.0003275108,"teacher_disagreement_score":0.5534077,"about_ca_system_score_codex":0.00011261823,"about_ca_system_score_gemma":0.0006148454,"threshold_uncertainty_score":0.8823531},"labels":[],"label_agreement":null},{"id":"W2044237996","doi":"10.1080/01431161.2013.788263","title":"The importance of accurate visibility parameterization during atmospheric correction: impact on boreal forest classification","year":2013,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Carleton University; York University","funders":"","keywords":"Visibility; Environmental science; Atmospheric correction; Radiative transfer; Radiance; Remote sensing; Land cover; Atmospheric model; Thematic Mapper; Meteorology; Geology; Satellite imagery; Satellite; Physics; Land use","score_opus":0.010228460104929922,"score_gpt":0.26154163175645906,"score_spread":0.2513131716515291,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044237996","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98956513,0.000013491017,0.006585461,0.0007535046,0.0012962312,0.00014327797,8.635901e-7,0.000012690811,0.0016293363],"genre_scores_gemma":[0.9900476,0.000054130258,0.009509037,0.000056664838,0.00021779764,2.5237817e-8,0.000004114611,0.000012056643,0.00009861775],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99823594,0.00010153736,0.00061933446,0.00017182202,0.00070632977,0.00016505276],"domain_scores_gemma":[0.99811894,0.00018952998,0.0010868611,0.0002132208,0.00031786048,0.00007358299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033257695,0.00014560358,0.00016246639,0.00001959766,0.00012205998,0.00012708141,0.00027683747,0.00006877748,0.00003830481],"category_scores_gemma":[0.00039371094,0.00008624143,0.00015947854,0.00021956266,0.00013789648,0.00039856107,0.000056232042,0.00024575324,0.000025494504],"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":[0.0004856751,0.00008043821,0.10260343,0.000006743054,0.00019572991,0.00004192511,0.0005036817,0.081305236,0.114427954,0.000011798876,0.0025480145,0.6977894],"study_design_scores_gemma":[0.00019209983,0.00007742597,0.7602909,0.00006984651,0.000009201544,0.00032614186,0.00009666242,0.23501648,0.0032168168,0.0005058286,0.00012122435,0.00007731734],"about_ca_topic_score_codex":0.00040426847,"about_ca_topic_score_gemma":0.00014497677,"teacher_disagreement_score":0.69771206,"about_ca_system_score_codex":0.00058636046,"about_ca_system_score_gemma":0.000024746842,"threshold_uncertainty_score":0.35168236},"labels":[],"label_agreement":null},{"id":"W2046540007","doi":"10.1080/01431161.2012.713141","title":"Investigation of correlation between remotely sensed impervious surfaces and chloride concentrations","year":2012,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Smart Materials for Construction","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Environment and Climate Change Canada; University of Waterloo","funders":"","keywords":"Impervious surface; Geography; Ecosystem; Environmental science; Aquatic ecosystem; Hydrology (agriculture); Physical geography; Environmental resource management; Remote sensing; Ecology; Oceanography; Geology","score_opus":0.01893281783766509,"score_gpt":0.24252310670740124,"score_spread":0.22359028886973614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046540007","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9894419,0.00002981377,0.008647197,0.0002661045,0.0013971481,0.000057498968,0.000003929517,0.000008406419,0.00014802045],"genre_scores_gemma":[0.977082,0.000027799819,0.022470016,0.000036725283,0.0003629987,5.2193823e-9,0.0000061888913,0.00000801494,0.0000062363083],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99887043,0.00006963493,0.00045591555,0.00007654824,0.0004131777,0.00011428488],"domain_scores_gemma":[0.99904376,0.00010287442,0.00059286994,0.000061686726,0.00011032439,0.000088497836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004051646,0.00008187764,0.0001420362,0.00007834456,0.000047841433,0.000031098112,0.00006440404,0.0000560874,0.000027745826],"category_scores_gemma":[0.00015392134,0.00007855087,0.000043103177,0.00008366869,0.00015399589,0.00058312214,0.000045424833,0.00009421781,0.000009078841],"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":[0.00007961201,0.000003908411,0.601816,0.000005401442,0.00007037802,0.0000066507546,0.0016332451,0.0010938634,0.35578403,0.00003148902,0.000071892835,0.039403517],"study_design_scores_gemma":[0.0004921268,0.00004321311,0.8085193,0.00009647425,0.00005767414,0.00089429406,0.00018939942,0.007727501,0.18036963,0.0011277233,0.00036669913,0.00011597171],"about_ca_topic_score_codex":0.00039799555,"about_ca_topic_score_gemma":0.000027234362,"teacher_disagreement_score":0.20670328,"about_ca_system_score_codex":0.00017472506,"about_ca_system_score_gemma":0.000022419053,"threshold_uncertainty_score":0.32032114},"labels":[],"label_agreement":null},{"id":"W2046938184","doi":"10.1080/01431160701268996","title":"Short‐term response of arctic vegetation NDVI to temperature anomalies","year":2007,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":35,"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 Oceanic and Atmospheric Administration; National Center for Atmospheric Research","keywords":"Normalized Difference Vegetation Index; Environmental science; Vegetation (pathology); Climate change; Biomass (ecology); Arctic; Physical geography; Productivity; Precipitation; Arctic vegetation; Land cover; Climatology; Atmospheric sciences; Tundra; Land use; Ecology; Geography; Geology; Meteorology","score_opus":0.026004643315868315,"score_gpt":0.2862136676682088,"score_spread":0.2602090243523405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046938184","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9966289,0.00025668784,0.0007973945,0.00072743295,0.0011938458,0.00003864527,0.000027825317,0.0000039842894,0.000325333],"genre_scores_gemma":[0.9947516,0.000041942374,0.0042980714,0.0003410745,0.00049981114,7.5509665e-10,0.000028998858,0.000003418317,0.000035098452],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99891156,0.000051430223,0.00037912812,0.00008131526,0.00044511867,0.00013144815],"domain_scores_gemma":[0.9988969,0.00026536378,0.00017264942,0.000065402885,0.0005033047,0.00009637431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008812867,0.00008039108,0.00013644208,0.000317328,0.000035361998,0.000052819043,0.00014911006,0.00004797135,0.00008741386],"category_scores_gemma":[0.00019026577,0.00006719497,0.0000797601,0.00012332649,0.00003107316,0.00017400514,0.000009193163,0.00013077764,0.00001274545],"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":[0.0048034196,0.00001935501,0.19113629,0.000042263127,0.00014429439,0.0012214178,0.0049714358,0.0016284016,0.35645872,0.0000050825283,0.00016170966,0.43940762],"study_design_scores_gemma":[0.00023228122,0.00018223537,0.97948444,0.0004315652,0.00001580553,0.0011970241,0.00043975926,0.0011902673,0.015961794,0.00010523276,0.00065888365,0.00010072514],"about_ca_topic_score_codex":0.00020206679,"about_ca_topic_score_gemma":0.0020684295,"teacher_disagreement_score":0.78834814,"about_ca_system_score_codex":0.000020164438,"about_ca_system_score_gemma":0.000034948167,"threshold_uncertainty_score":0.27401313},"labels":[],"label_agreement":null},{"id":"W2047115366","doi":"10.1080/01431161.2014.941242","title":"Ocean remote sensing for well-being of all","year":2014,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Atmospheric and Environmental Gas Dynamics","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":"Fisheries and Oceans Canada","funders":"","keywords":"Remote sensing; Environmental science; Geology; Computer science","score_opus":0.006838230353217805,"score_gpt":0.2320914936755362,"score_spread":0.22525326332231838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047115366","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4610359,0.0000070379306,0.5353855,0.00042350427,0.00047036132,0.00004306454,2.8861356e-7,0.000005882031,0.0026284722],"genre_scores_gemma":[0.5751322,0.000025665904,0.42409104,0.0003787378,0.00018804143,3.2760555e-10,0.0000012559327,0.000017180932,0.00016587853],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998535,0.000045258916,0.0005133562,0.00015850064,0.0005513312,0.0001965726],"domain_scores_gemma":[0.9990451,0.00012317928,0.0005596129,0.00012821594,0.000050861338,0.00009306693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005160093,0.00013842668,0.00022117671,0.000026735894,0.000049553782,0.000026299202,0.00024241486,0.00006465322,0.00003521413],"category_scores_gemma":[0.00012756919,0.00012943521,0.00018171481,0.00005802117,0.00013403827,0.00016764883,0.0001196612,0.00015464061,0.0000107404385],"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":[0.0001277353,0.000014041225,0.00045207344,0.00000718005,0.00010189182,0.000039371364,0.00054751284,0.07350202,0.016623111,0.000026609316,0.00026518133,0.90829325],"study_design_scores_gemma":[0.0007521743,0.00013173692,0.0010219364,0.00015514289,0.000052030373,0.0006538475,0.00017164827,0.96205926,0.0046414104,0.008004539,0.022174068,0.00018220727],"about_ca_topic_score_codex":0.00020600278,"about_ca_topic_score_gemma":0.000012729319,"teacher_disagreement_score":0.9081111,"about_ca_system_score_codex":0.00026658238,"about_ca_system_score_gemma":0.000008525944,"threshold_uncertainty_score":0.5278215},"labels":[],"label_agreement":null},{"id":"W2047152380","doi":"10.1080/01431160500406888","title":"Assessment of land‐cover changes related to shrimp aquaculture using remote sensing data: a case study in the Giao Thuy District, Vietnam","year":2006,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":91,"is_retracted":false,"has_abstract":true,"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":"Mangrove; Deforestation (computer science); Reforestation; Aquaculture; Shrimp; Land cover; Feature (linguistics); Shrimp farming; Wetland; Environmental science; Remote sensing; Land use; Geography; Environmental resource management; Ecology; Agroforestry; Computer science; Fishery; Biology","score_opus":0.03734489949515291,"score_gpt":0.3302010912201152,"score_spread":0.2928561917249623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047152380","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85480064,0.00010640185,0.1427495,0.00064562965,0.0008953673,0.00028568562,0.000015841732,0.00003268264,0.00046821745],"genre_scores_gemma":[0.90190685,0.000022409073,0.097572975,0.000060574766,0.0003539077,5.3486917e-9,0.00002258737,0.00004189329,0.00001877369],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973642,0.00023386769,0.0009548745,0.0002676842,0.00092385133,0.0002554783],"domain_scores_gemma":[0.99813145,0.00020137885,0.0005102854,0.0005081638,0.00058807497,0.000060652623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012373315,0.00025663894,0.0003932698,0.0004913831,0.00007739244,0.00019816316,0.0004342498,0.00010651482,0.0000021726082],"category_scores_gemma":[0.00021509697,0.00020455044,0.000094638934,0.00054589397,0.000042105334,0.0003332714,0.00011454831,0.00057901867,0.0000020306297],"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":[0.00010601261,0.00018736093,0.0014886934,0.00007178355,0.0006705431,0.026735513,0.0048841983,0.28751758,0.15463002,0.000010217704,0.0008464094,0.52285165],"study_design_scores_gemma":[0.000903064,0.00007164162,0.0070735663,0.0004933718,0.000113449176,0.019773971,0.0016880895,0.9680937,0.00077203946,0.00008618425,0.00070345704,0.00022749019],"about_ca_topic_score_codex":0.0014575798,"about_ca_topic_score_gemma":0.0011240363,"teacher_disagreement_score":0.6805761,"about_ca_system_score_codex":0.00048782502,"about_ca_system_score_gemma":0.00007846392,"threshold_uncertainty_score":0.83413243},"labels":[],"label_agreement":null},{"id":"W2051229389","doi":"10.1080/01431160050144974","title":"Évaluation de la reprÉsentativitÉ spatiale thermique des stations mÉtÉorologiques du rÉseau d'Andalousie","year":2000,"lang":"fr","type":"article","venue":"International Journal of Remote Sensing","topic":"3D Surveying and Cultural Heritage","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":false,"ca_institutions":"Université Laval","funders":"","keywords":"Geography; Geology","score_opus":0.03652925589957701,"score_gpt":0.294087206180317,"score_spread":0.25755795028073997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051229389","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9594504,0.0020366458,0.0231769,0.003687235,0.0013688848,0.00006504102,0.000044322693,0.000021227028,0.0101493085],"genre_scores_gemma":[0.89090365,0.005379981,0.10051528,0.00040543466,0.0011659544,1.1793413e-8,0.000032441018,0.000009401385,0.0015878136],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9970312,0.001514431,0.0005187685,0.0001614452,0.00051663833,0.00025753462],"domain_scores_gemma":[0.9980413,0.00084987975,0.00032389475,0.000085203574,0.00056251074,0.00013726672],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0024860085,0.00016546594,0.00021105372,0.00010956812,0.0002023432,0.00029279376,0.00022167413,0.0001442593,0.0018812706],"category_scores_gemma":[0.00055851915,0.0001432237,0.00016438524,0.00013033769,0.0003206324,0.00053691113,0.00000968621,0.00038832944,0.000057789952],"study_design_candidate":"design_other","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":[0.00017789593,0.000029239121,0.019494269,0.000009857644,0.00013358338,0.0006596144,0.0050822063,0.016504819,0.0006071406,0.000016555208,0.00054474606,0.9567401],"study_design_scores_gemma":[0.0019219386,0.0007525956,0.49765068,0.0014919415,0.00020797012,0.015970813,0.0029583457,0.38709533,0.0036076743,0.0402062,0.047477085,0.00065944355],"about_ca_topic_score_codex":0.00833137,"about_ca_topic_score_gemma":0.0028284746,"teacher_disagreement_score":0.9560806,"about_ca_system_score_codex":0.000102551116,"about_ca_system_score_gemma":0.00031763947,"threshold_uncertainty_score":0.9990311},"labels":[],"label_agreement":null},{"id":"W2051440202","doi":"10.1080/01431161.2014.978035","title":"Land-use and land-cover classification in semi-arid regions using independent component analysis (ICA) and expert classification","year":2014,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":15,"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":"","keywords":"Land cover; Thematic Mapper; Thematic map; Watershed; Remote sensing; Cohen's kappa; Vegetation (pathology); Land use; Arid; Arable land; Preprocessor; Environmental science; Cartography; Geography; Computer science; Satellite imagery; Artificial intelligence; Statistics; Mathematics; Geology; Agriculture","score_opus":0.04407450553201075,"score_gpt":0.27924470252677597,"score_spread":0.2351701969947652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051440202","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6878119,0.00011585222,0.3109686,0.0005693544,0.00033556312,0.00006347902,0.0000020397401,0.000029480136,0.00010369301],"genre_scores_gemma":[0.97259796,0.0004897864,0.026547192,0.00006952471,0.00023527336,3.9910493e-8,0.000019168745,0.00002924204,0.000011819141],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99829584,0.00014293984,0.00066371774,0.00023353846,0.0004911636,0.00017279325],"domain_scores_gemma":[0.99867266,0.00022767881,0.00037120588,0.00021024703,0.00041028578,0.000107944776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005736086,0.0001848676,0.000319616,0.00088771794,0.000057933812,0.0002644431,0.00012916565,0.00012913016,0.0000024541298],"category_scores_gemma":[0.00023434786,0.00018521353,0.00009279465,0.00027612914,0.000073856565,0.0005094096,0.000037021768,0.00032293363,0.0000024937974],"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":[0.00016423305,0.000066808854,0.048840236,0.000039881466,0.0010089985,0.000096120806,0.0015943017,0.069606386,0.68866706,0.00020563553,0.00016179844,0.18954854],"study_design_scores_gemma":[0.0004987293,0.000011818782,0.18896283,0.000117904136,0.000092899274,0.0003172133,0.00007112257,0.8076472,0.001197956,0.00018353401,0.00075857516,0.00014019795],"about_ca_topic_score_codex":0.00020432878,"about_ca_topic_score_gemma":0.00011228211,"teacher_disagreement_score":0.7380408,"about_ca_system_score_codex":0.0003612692,"about_ca_system_score_gemma":0.00002753189,"threshold_uncertainty_score":0.7552789},"labels":[],"label_agreement":null},{"id":"W2054084537","doi":"10.1080/01431160500106975","title":"A method to obtain large quantities of reference data","year":2006,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":8,"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 du Québec à Montréal","funders":"Office of Experimental Program to Stimulate Competitive Research; National Science Foundation","keywords":"Reference data; Field (mathematics); Aerial photography; Spatial analysis; Remote sensing; Test site; Vegetation (pathology); Cluster (spacecraft); Plot (graphics); Autocorrelation; Scatter plot; Computer science; Environmental science; Cartography; Geography; Statistics; Data mining; Mathematics; Geology","score_opus":0.0378519987888753,"score_gpt":0.33790957382126935,"score_spread":0.30005757503239405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054084537","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21514806,0.000027552278,0.7709464,0.0019342548,0.00021456384,0.00005735768,0.000027610782,0.000011018042,0.011633175],"genre_scores_gemma":[0.5260503,0.00000631758,0.4734199,0.00016104887,0.00013722412,1.950588e-9,0.000009699118,0.0000070830074,0.00020839249],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99858874,0.000074876894,0.00045332624,0.00016488232,0.00058122334,0.00013696653],"domain_scores_gemma":[0.99905956,0.00010829379,0.0003485883,0.00028522385,0.00014242643,0.00005593525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008082946,0.00008539667,0.00016087768,0.00011439757,0.00004513285,0.000039115483,0.0005429632,0.00003800919,0.00005568546],"category_scores_gemma":[0.00009769537,0.00007670349,0.000052199073,0.00015825886,0.000047677528,0.00022192138,0.0002585186,0.00013842907,0.000040740393],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000101640035,0.00008477399,0.0005699455,0.0000056702816,0.00008109637,0.00009662114,0.00042431892,0.0053988774,0.22377016,0.0015611257,0.011239409,0.75666636],"study_design_scores_gemma":[0.0012346047,0.00016644485,0.02052219,0.00044035056,0.0000979391,0.0022010126,0.00088441285,0.19633368,0.07037674,0.019893505,0.6873356,0.00051352306],"about_ca_topic_score_codex":0.0021868602,"about_ca_topic_score_gemma":0.00033160436,"teacher_disagreement_score":0.75615287,"about_ca_system_score_codex":0.00009554076,"about_ca_system_score_gemma":0.000028056818,"threshold_uncertainty_score":0.3305893},"labels":[],"label_agreement":null},{"id":"W2056189760","doi":"10.1080/01431160210155028","title":"Confusion in data fusion","year":2003,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Nova Scotia Hospital; Bedford Institute of Oceanography; Canadian Hydrographic Service","funders":"","keywords":"Computer science; Sensor fusion; Metadata; Overlay; Context (archaeology); Fusion; Term (time); Data mining; Confusion; Object (grammar); Information retrieval; Artificial intelligence; World Wide Web","score_opus":0.021915292324024387,"score_gpt":0.2952424418548685,"score_spread":0.2733271495308441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056189760","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.122543536,0.00046796346,0.86954147,0.00023345364,0.0018811309,0.000057954425,0.000004858896,0.00007390651,0.00519576],"genre_scores_gemma":[0.622472,0.00046452796,0.37678754,0.000082518854,0.00013438359,3.5946104e-9,0.0000055792166,0.000020021722,0.00003345999],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991089,0.000030479132,0.00036911166,0.00008330073,0.0003074013,0.00010079117],"domain_scores_gemma":[0.99942166,0.000050596555,0.000108230204,0.00017816883,0.00020343025,0.00003794676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035733584,0.00008209253,0.00012534538,0.00026889268,0.000012709439,0.000028484392,0.00029622807,0.00004262415,0.000032906653],"category_scores_gemma":[0.00032576945,0.00007923714,0.000030487712,0.00009473886,0.000016175041,0.00032821763,0.00006580613,0.00023804724,0.000004986958],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020603937,0.00000962082,0.000034580924,0.000005445034,0.000022126778,0.00049142155,0.000121146535,0.0019895406,0.149345,0.00005015753,0.0015451862,0.84636515],"study_design_scores_gemma":[0.001615592,0.000049683644,0.00042314298,0.001129842,0.00001632016,0.0039031552,0.00027742013,0.4689009,0.26362562,0.009269151,0.25037608,0.00041307893],"about_ca_topic_score_codex":0.000007780536,"about_ca_topic_score_gemma":0.000016907656,"teacher_disagreement_score":0.8459521,"about_ca_system_score_codex":0.00011136585,"about_ca_system_score_gemma":0.00002541554,"threshold_uncertainty_score":0.32311967},"labels":[],"label_agreement":null},{"id":"W2056254247","doi":"10.1080/01431160210154056","title":"Monitoring secondary tropical forests using space-borne data: Implications for Central America","year":2003,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":106,"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":"","keywords":"Synthetic aperture radar; Remote sensing; Carbon sequestration; Environmental science; Carbon sink; Amazon rainforest; Biomass (ecology); Radar; Stratification (seeds); Geography; Climate change; Ecology; Computer science","score_opus":0.0368091106949423,"score_gpt":0.31792478567840227,"score_spread":0.28111567498345996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056254247","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33685857,0.00007284763,0.65834254,0.00194428,0.0010323909,0.00012936878,0.00002018674,0.00001664523,0.0015831394],"genre_scores_gemma":[0.63779366,0.00003256135,0.36160254,0.00006828524,0.00044248864,1.6199305e-8,0.000006561341,0.000017022194,0.000036865127],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9986395,0.000055237815,0.00044454038,0.00025471853,0.00033119702,0.00027475637],"domain_scores_gemma":[0.99883515,0.0001344362,0.00039205572,0.00034509163,0.00013913264,0.00015413496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016592185,0.0001342275,0.0001817727,0.00008719691,0.00016162176,0.00010023422,0.00042366234,0.000058149664,0.000025267094],"category_scores_gemma":[0.0003009166,0.0001293578,0.000117645206,0.00015539298,0.00011636361,0.00032966517,0.00010118603,0.00022615273,0.000008970704],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006552514,0.00006307513,0.008626108,0.0000049377545,0.00015789918,0.00002642197,0.0003221155,0.0061312662,0.06463291,0.00019652126,0.0015241939,0.918249],"study_design_scores_gemma":[0.0030101698,0.00019433147,0.37162277,0.0003885054,0.00029468464,0.0057633864,0.0007357429,0.20411398,0.027637469,0.015984084,0.36932772,0.0009271625],"about_ca_topic_score_codex":0.0001349217,"about_ca_topic_score_gemma":0.00003415852,"teacher_disagreement_score":0.91732186,"about_ca_system_score_codex":0.00034696193,"about_ca_system_score_gemma":0.000093405644,"threshold_uncertainty_score":0.52750576},"labels":[],"label_agreement":null},{"id":"W2056289506","doi":"10.1080/0143116042000298220","title":"Comparison of function‐ and structure‐based schemes for classification of remotely sensed data","year":2005,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; York University","funders":"Universitas Sam Ratulangi","keywords":"Thematic map; Multispectral image; Computer science; Class (philosophy); Remote sensing; Land cover; Classification scheme; Watershed; Function (biology); Data mining; Pattern recognition (psychology); Geography; Artificial intelligence; Land use; Cartography; Machine learning; Ecology","score_opus":0.07598598703332751,"score_gpt":0.3455163693930938,"score_spread":0.26953038235976634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056289506","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38409173,0.00021375045,0.61440307,0.00050854625,0.0005669271,0.00008533571,0.00002920116,0.0000205409,0.00008087124],"genre_scores_gemma":[0.71162647,0.000015258845,0.2880145,0.000022673223,0.00025542604,3.8496886e-9,0.000040458344,0.000020135856,0.000005087142],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984454,0.000034691642,0.0008416248,0.00014736774,0.00042313343,0.000107781125],"domain_scores_gemma":[0.99772257,0.00021955046,0.0006973669,0.00030206604,0.0010087971,0.00004964525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002886548,0.00013124617,0.0003057706,0.00031347352,0.000027233471,0.000036799705,0.00024114554,0.00008988103,0.0000033487559],"category_scores_gemma":[0.00040312076,0.00013212896,0.00007062423,0.00010801505,0.00008289471,0.000366667,0.000028893895,0.00017373556,4.8528676e-7],"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":[0.0001311932,0.000013060083,0.0001133278,0.000045046676,0.0001077439,9.333879e-7,0.00006836007,0.0061360584,0.5726096,0.00003784402,0.0002797055,0.4204571],"study_design_scores_gemma":[0.0007305085,0.000043081698,0.005143217,0.00019255723,0.00006700776,0.00006397221,0.000079064,0.8579888,0.1300483,0.000241909,0.005309543,0.00009207814],"about_ca_topic_score_codex":0.0000054782163,"about_ca_topic_score_gemma":0.000012522422,"teacher_disagreement_score":0.8518527,"about_ca_system_score_codex":0.00009993287,"about_ca_system_score_gemma":0.0000631541,"threshold_uncertainty_score":0.53880626},"labels":[],"label_agreement":null},{"id":"W2058020454","doi":"10.1080/01431161.2012.727040","title":"Comparing matrix distance measures for unsupervised POLSAR data classification of sea ice based on agglomerative clustering","year":2012,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","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":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Hierarchical clustering; Cluster analysis; Computer science; Matrix (chemical analysis); Sea ice; Artificial intelligence; Pattern recognition (psychology); Data mining; Geography; Meteorology","score_opus":0.07151308321292425,"score_gpt":0.3092287215248877,"score_spread":0.2377156383119634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058020454","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27996445,0.00010752999,0.7172396,0.00073152856,0.0010687581,0.00008810509,0.00011160678,0.0000065781737,0.00068183156],"genre_scores_gemma":[0.9086606,0.000020512047,0.09064052,0.00012067479,0.0004090856,4.2524544e-9,0.00013311984,0.0000044686167,0.00001102557],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987842,0.000063884676,0.00038506268,0.0001075692,0.00050056743,0.0001587592],"domain_scores_gemma":[0.9986258,0.000342086,0.00043775197,0.00015133125,0.0003665066,0.00007649879],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071916945,0.00009506209,0.00017921464,0.000118949465,0.00007543758,0.000041495357,0.00033576775,0.00003438481,0.000015437789],"category_scores_gemma":[0.00025392626,0.00007985819,0.000066855326,0.0000699738,0.000050876817,0.000409596,0.000017767296,0.00013647146,0.0000027093602],"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":[0.001811105,0.00007258782,0.1688624,0.0000881928,0.00029501563,0.000024126957,0.0012086689,0.058889642,0.0017020707,0.00012952923,0.00018569852,0.76673096],"study_design_scores_gemma":[0.00046752067,0.00005271788,0.04805628,0.0002344885,0.000030706346,0.000071368886,0.000392691,0.9491925,0.00016918947,0.00008832389,0.0011620518,0.00008212395],"about_ca_topic_score_codex":0.00025585108,"about_ca_topic_score_gemma":0.00025443124,"teacher_disagreement_score":0.8903029,"about_ca_system_score_codex":0.0000303461,"about_ca_system_score_gemma":0.00006671657,"threshold_uncertainty_score":0.32565224},"labels":[],"label_agreement":null},{"id":"W2060634510","doi":"10.1080/01431160802392646","title":"Finite Gamma mixture modelling using minimum message length inference: application to SAR image analysis","year":2009,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Université de Sherbrooke","funders":"","keywords":"Computer science; Inference; Set (abstract data type); Image (mathematics); Segmentation; Data set; Synthetic aperture radar; Finite set; Artificial intelligence; Image segmentation; Unsupervised learning; Synthetic data; Pattern recognition (psychology); Algorithm; Data mining; Mathematics","score_opus":0.021962810220889247,"score_gpt":0.3197440377333011,"score_spread":0.2977812275124119,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060634510","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020098744,0.00008930872,0.9764761,0.002438617,0.0003659053,0.00007666064,0.0000022845786,0.000027350214,0.00042503406],"genre_scores_gemma":[0.41997325,0.00002277593,0.5791651,0.0005634418,0.00025009652,5.8483005e-9,0.0000013449408,0.000005797013,0.000018207933],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979533,0.00013014188,0.0006329086,0.00030989118,0.000737897,0.00023585666],"domain_scores_gemma":[0.99771285,0.0001608754,0.0005418381,0.00033078942,0.0010808359,0.00017283742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008353481,0.00018945834,0.00035275889,0.0008416452,0.000084830404,0.00033699686,0.000814186,0.00009678073,0.0000040383306],"category_scores_gemma":[0.00013853869,0.00017334036,0.0002889698,0.0007827971,0.000019537927,0.00059927703,0.00009281319,0.0003233504,0.0000045868137],"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":[0.000037405076,0.000024270206,0.0000072290936,0.000002510495,0.0002664043,0.0001429119,0.00092090614,0.15181491,0.025916507,0.001356897,0.000036815978,0.8194732],"study_design_scores_gemma":[0.00021566566,0.00004339286,0.000049280676,0.00008824803,0.000106171574,0.00015605497,0.000015898298,0.9706153,0.004032962,0.023953717,0.00054740196,0.00017588878],"about_ca_topic_score_codex":0.000034410325,"about_ca_topic_score_gemma":0.0000033163926,"teacher_disagreement_score":0.8192974,"about_ca_system_score_codex":0.0001445498,"about_ca_system_score_gemma":0.000102461454,"threshold_uncertainty_score":0.7068615},"labels":[],"label_agreement":null},{"id":"W2063873325","doi":"10.1080/01431161.2011.593584","title":"Multi-type change detection of building models by integrating spatial and spectral information","year":2011,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Science Council","keywords":"Change detection; Lidar; Computer science; Point cloud; Spatial analysis; Remote sensing; Scheme (mathematics); Work (physics); Building model; Data mining; Artificial intelligence; Geography; Simulation","score_opus":0.028606928927043127,"score_gpt":0.2534881305052558,"score_spread":0.22488120157821267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063873325","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5112223,0.00001986455,0.48783544,0.00005555261,0.00025721328,0.000045491386,0.0000013575774,0.00000672506,0.0005560385],"genre_scores_gemma":[0.8311062,0.00003284276,0.16871718,0.000052378153,0.00007841381,7.325553e-9,0.0000015625806,0.0000058163714,0.000005618687],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911654,0.000028744316,0.00038064242,0.000078510064,0.00029956704,0.0000960027],"domain_scores_gemma":[0.99926615,0.000022408522,0.00045571718,0.000066608045,0.00013537421,0.00005375813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022137843,0.00008477772,0.000112686386,0.000107213484,0.00004433215,0.000030418587,0.00010972896,0.000048655795,0.0000117452455],"category_scores_gemma":[0.000071009075,0.00007742866,0.000047293397,0.000103854705,0.0000711338,0.00068802625,0.000050703704,0.00014880717,0.0000028369961],"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":[0.000045105062,0.000013797754,0.00011826593,0.0000019464526,0.000020011068,0.0000027138924,0.0021085176,0.00008431246,0.2990756,0.000007644035,0.000011089171,0.698511],"study_design_scores_gemma":[0.0005911769,0.00018099336,0.004770064,0.00014576044,0.000031565633,0.00060565147,0.0005342728,0.72220814,0.2684379,0.0014264972,0.0008954546,0.00017248823],"about_ca_topic_score_codex":0.0030675645,"about_ca_topic_score_gemma":0.00015548941,"teacher_disagreement_score":0.72212386,"about_ca_system_score_codex":0.000113653,"about_ca_system_score_gemma":0.0000070702154,"threshold_uncertainty_score":0.46372604},"labels":[],"label_agreement":null},{"id":"W2063895620","doi":"10.1080/01431161.2012.723835","title":"Climatological, annual, and seasonal variability in chlorophyll concentration in the Gulf of Mexico, western Caribbean, and Bahamas using NASA colour maps","year":2012,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","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":"Université Laval","funders":"Goddard Space Flight Center; Vedecká Grantová Agentúra MŠVVaŠ SR a SAV; Consejo Nacional de Ciencia y Tecnología; Northwestern University","keywords":"SeaWiFS; Seasonality; Ocean color; Climatology; Oceanography; Environmental science; Pacific ocean; Chlorophyll a; Geography; Geology; Phytoplankton; Satellite; Nutrient; Mathematics","score_opus":0.019060244730536396,"score_gpt":0.250794948723541,"score_spread":0.23173470399300464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063895620","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99747425,0.00024136233,0.0009065558,0.00053365517,0.00031924894,0.00007048296,0.000024696277,0.0000014359035,0.0004283243],"genre_scores_gemma":[0.9975944,0.00005848063,0.0020766605,0.0001289001,0.00013173548,4.793452e-9,0.000006351857,0.0000015035414,0.000001967767],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99876904,0.0002697063,0.0004121457,0.00008621377,0.0003187005,0.00014419203],"domain_scores_gemma":[0.99916285,0.00031721036,0.00027360677,0.000048780475,0.00014244828,0.00005509824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015072409,0.000078563695,0.000186033,0.000070957445,0.000028984437,0.00005104419,0.00010343305,0.000050857187,0.000013515315],"category_scores_gemma":[0.00016075122,0.00005471114,0.000030698422,0.000084420026,0.000067809444,0.00033677858,0.000022756056,0.00017268544,4.542338e-7],"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":[0.000117966985,0.000017924052,0.9466751,0.000014275959,0.000016494567,0.00006619457,0.0009207554,0.0005216315,0.00016421477,0.000032288197,0.000008738411,0.051444408],"study_design_scores_gemma":[0.0006737101,0.00009645691,0.90693146,0.00015925107,0.000014896299,0.0023278971,0.0013314151,0.08657958,0.00011868108,0.0009049091,0.00075403426,0.0001077162],"about_ca_topic_score_codex":0.0020378986,"about_ca_topic_score_gemma":0.0018160393,"teacher_disagreement_score":0.08605795,"about_ca_system_score_codex":0.000014572572,"about_ca_system_score_gemma":0.0000429973,"threshold_uncertainty_score":0.3080707},"labels":[],"label_agreement":null},{"id":"W2065910099","doi":"10.1080/01431160903349040","title":"Employing ground-based spectroscopy for tree-species differentiation in the Gulf Islands National Park Reserve","year":2010,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Parks Canada; University of British Columbia","funders":"University of British Columbia; Parks Canada","keywords":"Hyperspectral imaging; Imaging spectrometer; Remote sensing; Spectrometer; Wavelength; Environmental science; Scale (ratio); Spectral signature; Reflectivity; Linear discriminant analysis; Vegetation (pathology); Geography; Cartography; Mathematics; Statistics; Optics; Physics","score_opus":0.037426350942636924,"score_gpt":0.2985831266195442,"score_spread":0.2611567756769073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065910099","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9602078,0.000008012585,0.026668893,0.0049785543,0.0011044478,0.00010729471,0.000025219133,0.000007916151,0.0068918597],"genre_scores_gemma":[0.9930287,0.000010956231,0.0058515645,0.00056988106,0.00042412488,1.8862949e-7,0.000045348555,0.0000085289375,0.000060729308],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9984314,0.00004917439,0.00035021768,0.00012141038,0.00088666513,0.00016117292],"domain_scores_gemma":[0.99918205,0.00022857377,0.00028931873,0.00008518277,0.00017887964,0.000036028563],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00074085157,0.000099166995,0.000112836206,0.000109166525,0.000097957934,0.00015555839,0.00034577257,0.000053568903,0.0011171849],"category_scores_gemma":[0.00036413735,0.00007383978,0.00012810362,0.00013394785,0.00008252628,0.00020887758,0.000036362297,0.00025260609,0.00001561256],"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":[0.0012915927,0.00046707128,0.050089534,0.000020463622,0.00015959094,0.00013598826,0.0035982165,0.00094849896,0.85128236,0.0040905112,0.025603661,0.062312543],"study_design_scores_gemma":[0.0046307947,0.00025715487,0.80483365,0.00013628589,0.00004353053,0.00045201142,0.002932359,0.0613572,0.035098042,0.01288851,0.07691337,0.00045710476],"about_ca_topic_score_codex":0.00008712482,"about_ca_topic_score_gemma":0.002448493,"teacher_disagreement_score":0.8161843,"about_ca_system_score_codex":0.00044287133,"about_ca_system_score_gemma":0.00002735045,"threshold_uncertainty_score":0.9997959},"labels":[],"label_agreement":null},{"id":"W2066938450","doi":"10.1080/014311600750019840","title":"Radarsat data analysis for monitoring and evaluation of irrigation projects in the monsoon","year":2000,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Space Agency","keywords":"Remote sensing; Environmental science; Land cover; Synthetic aperture radar; Geography; Land use","score_opus":0.05231941819283346,"score_gpt":0.3318816179887085,"score_spread":0.27956219979587504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066938450","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5905197,0.0003626561,0.40805787,0.00033352466,0.00009518003,0.00016934633,0.000007426125,0.000010218807,0.000444043],"genre_scores_gemma":[0.6401841,0.000110265326,0.35959312,0.0000052670543,0.000094014555,4.697979e-8,0.0000072604316,0.0000047220756,0.000001202305],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912417,0.000040269042,0.00029976215,0.00007710847,0.00040324772,0.000055470482],"domain_scores_gemma":[0.99931234,0.00014351038,0.00010744989,0.00016378085,0.0002608637,0.00001205569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011434797,0.00005888974,0.00011661297,0.00021910347,0.00001913514,0.000027644603,0.00020356581,0.000032790405,0.0000046384007],"category_scores_gemma":[0.00007791958,0.000045623303,0.000043429383,0.00018408147,0.0000173355,0.00013952608,0.000010448175,0.00007907362,1.1936844e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011301503,0.0000057733046,0.00012491396,0.000004518738,0.0001387679,0.0000010785274,0.00040877584,0.0006604513,0.00073329365,0.00001602951,0.000016624343,0.9978785],"study_design_scores_gemma":[0.00035640385,0.000017639384,0.004351944,0.00013194505,0.00032066513,0.00008415571,0.00032730843,0.9667235,0.014125822,0.002618166,0.010871847,0.000070581904],"about_ca_topic_score_codex":0.000055118504,"about_ca_topic_score_gemma":0.000014889329,"teacher_disagreement_score":0.99780786,"about_ca_system_score_codex":0.0000634382,"about_ca_system_score_gemma":0.000021524102,"threshold_uncertainty_score":0.18604644},"labels":[],"label_agreement":null},{"id":"W2069748987","doi":"10.1080/01431161003745590","title":"A method for obtaining and applying classification parameters in object-based urban rooftop extraction from VHR multispectral images","year":2011,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multispectral image; Extraction (chemistry); Remote sensing; Computer science; Object (grammar); Multispectral pattern recognition; Object based; Artificial intelligence; Pattern recognition (psychology); Computer vision; Geography","score_opus":0.04699599688226821,"score_gpt":0.3061145417061512,"score_spread":0.259118544823883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069748987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24918532,0.00012257672,0.7494608,0.00014632198,0.0006618144,0.00016096313,0.0000049449277,0.00004977092,0.00020745248],"genre_scores_gemma":[0.50896055,0.000020087493,0.49082676,0.000025289008,0.00013095081,1.5802212e-7,0.000007353753,0.00002493018,0.000003917337],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858147,0.00008728274,0.0006329181,0.00021038573,0.00029206113,0.00019591034],"domain_scores_gemma":[0.9986293,0.00045569893,0.00037837835,0.00012965956,0.00033462286,0.000072354946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005866339,0.00017865699,0.00025507773,0.00049651385,0.000043466585,0.00011254687,0.00013758785,0.000108685475,0.0000021830604],"category_scores_gemma":[0.00032693802,0.00019285148,0.00010720232,0.0001229132,0.000041321386,0.0004082181,0.00001128392,0.0003230807,0.0000014195643],"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":[0.00016531441,0.000015417629,0.00024191219,0.000013317771,0.00008939424,0.00005189802,0.000949192,0.005150062,0.30233017,0.0000122035935,0.000054305296,0.6909268],"study_design_scores_gemma":[0.00091428176,0.00003452211,0.011182875,0.0002614491,0.000040153325,0.00014677785,0.00042165257,0.8683957,0.11730712,0.00083281926,0.00029052177,0.00017216758],"about_ca_topic_score_codex":0.00019325472,"about_ca_topic_score_gemma":0.00005071711,"teacher_disagreement_score":0.8632456,"about_ca_system_score_codex":0.00034621984,"about_ca_system_score_gemma":0.00004417039,"threshold_uncertainty_score":0.78642553},"labels":[],"label_agreement":null},{"id":"W2074234119","doi":"10.1080/01431160210144606","title":"Towards an automated ocean feature detection, extraction and classification scheme for SAR imagery","year":2003,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Oceanographic and Atmospheric Processes","field":"Earth and Planetary Sciences","cited_by":67,"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 Space Agency; National Aeronautics and Space Administration","keywords":"Computer science; Synthetic aperture radar; Feature extraction; Artificial intelligence; Feature (linguistics); Remote sensing; Pattern recognition (psychology); Wavelet; Histogram; Computer vision; Geology; Image (mathematics)","score_opus":0.01578557259746305,"score_gpt":0.2747427211017871,"score_spread":0.25895714850432405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074234119","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8452463,0.00047413257,0.151694,0.000637194,0.0012019197,0.00006608319,0.0000072165017,0.000054515927,0.00061859615],"genre_scores_gemma":[0.8258937,0.00012615492,0.17360535,0.00010433182,0.00022671295,1.3822968e-9,0.000010371261,0.0000037074453,0.000029655059],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927247,0.000042234846,0.00021942885,0.00011765387,0.00024322826,0.00010498174],"domain_scores_gemma":[0.9989819,0.00006206352,0.00029138027,0.00004783811,0.00053767586,0.00007913429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035905745,0.00008620373,0.00010417119,0.0000695397,0.00010296175,0.0001270053,0.000078957215,0.00006378372,0.000015506634],"category_scores_gemma":[0.00023880858,0.00007261183,0.00005312279,0.00012154324,0.000042184245,0.0005716831,0.0000019994018,0.00013236186,0.0000012255958],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001551757,0.000013088754,0.0058341045,0.000014660255,0.00008301122,0.000029361148,0.00013223368,0.00026556113,0.0061606886,0.000019954981,0.00039895566,0.9868932],"study_design_scores_gemma":[0.0009309488,0.0002792722,0.10950813,0.0001257075,0.00005386983,0.0033850248,0.0009142703,0.8455708,0.009843207,0.00446023,0.024663325,0.00026522178],"about_ca_topic_score_codex":0.000039223567,"about_ca_topic_score_gemma":0.000044728902,"teacher_disagreement_score":0.986628,"about_ca_system_score_codex":0.000011143443,"about_ca_system_score_gemma":0.00007230621,"threshold_uncertainty_score":0.29610246},"labels":[],"label_agreement":null},{"id":"W2076227585","doi":"10.1080/01431160600647704","title":"A pixel‐based semi‐empirical system for predicting vegetation diversity in boreal forest","year":2007,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","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":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary; AXYS Technologies (Canada)","funders":"Directorate for Biological Sciences","keywords":"Species richness; Taiga; Vegetation (pathology); Scale (ratio); Geospatial analysis; Geography; Remote sensing; Boreal; Digital elevation model; Multivariate statistics; Environmental science; Pixel; Physical geography; Ecology; Cartography; Forestry; Statistics; Computer science; Mathematics","score_opus":0.02186321681921638,"score_gpt":0.28880919616753215,"score_spread":0.26694597934831577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076227585","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6957669,0.000009721055,0.3016524,0.00038583612,0.00052084745,0.00006116294,8.6810456e-7,0.000007527583,0.0015947288],"genre_scores_gemma":[0.96871364,0.0000022616923,0.030969981,0.0001623129,0.00013271545,2.197104e-8,0.0000029319542,0.0000051018515,0.000011036715],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990453,0.00003245972,0.00035666474,0.00010325459,0.00031933232,0.00014296507],"domain_scores_gemma":[0.99915385,0.0003278406,0.0003156113,0.000038122518,0.00012054132,0.000044022683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096190156,0.00007008827,0.0001199928,0.00013451085,0.00012453133,0.000014103746,0.0001287812,0.000058134676,0.0000016972264],"category_scores_gemma":[0.00021982705,0.00006705565,0.00007488793,0.00008970033,0.00006077566,0.00015143576,0.00009254797,0.00013775939,0.0000030409738],"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":[0.0002919121,0.000030750136,0.93827873,0.000015660784,0.000054523574,0.00013827093,0.0011266907,0.02356781,0.00058644905,0.000048063914,0.000038344093,0.035822816],"study_design_scores_gemma":[0.0008780634,0.00005996249,0.6184341,0.00011860024,0.000016886932,0.00011275716,0.0003252553,0.3789665,0.00020257091,0.00072004675,0.000102287486,0.00006298003],"about_ca_topic_score_codex":0.00013177384,"about_ca_topic_score_gemma":0.0034392802,"teacher_disagreement_score":0.35539868,"about_ca_system_score_codex":0.0005711158,"about_ca_system_score_gemma":0.000018271543,"threshold_uncertainty_score":0.27344498},"labels":[],"label_agreement":null},{"id":"W2076319407","doi":"10.1080/01431160601075616","title":"Observations of ice surface temperature and thickness in the Baltic Sea","year":2007,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":16,"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":"","keywords":"Open water; Sea ice; Standard deviation; Geology; Atmospheric sciences; Environmental science; Arctic ice pack; Atmosphere (unit); Altitude (triangle); Climatology; Geodesy; Meteorology; Oceanography; Geography; Geometry","score_opus":0.01706044590832754,"score_gpt":0.2482179830059361,"score_spread":0.23115753709760856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076319407","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99278754,0.00009809369,0.0040857065,0.0021522283,0.0004261053,0.000034402015,0.00000457511,0.0000015316244,0.00040981924],"genre_scores_gemma":[0.9736855,0.00008359112,0.025620889,0.00046188262,0.00012531236,2.50548e-10,0.0000048353404,0.000001387722,0.000016591875],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991887,0.000047693644,0.0002709601,0.000052106465,0.00035091848,0.0000895994],"domain_scores_gemma":[0.9989726,0.0005303482,0.00018364482,0.00004613826,0.00023859943,0.000028670413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095402927,0.0000524332,0.00008879415,0.00006672208,0.000042971213,0.000030094172,0.00015554248,0.000036446432,0.00001062717],"category_scores_gemma":[0.00017108557,0.00003459328,0.00003249687,0.00011679813,0.000059942544,0.00015511482,0.00000693305,0.00022662719,7.845197e-7],"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":[0.0006662739,0.000040781935,0.66535604,0.000046020727,0.00012740804,0.00086275395,0.009631579,0.020408116,0.004853589,0.0003241021,0.0001043038,0.29757902],"study_design_scores_gemma":[0.0007063086,0.00005566476,0.93435293,0.00020256943,0.000020387111,0.0016846418,0.003892104,0.056977432,0.00012658328,0.0014210277,0.00047539026,0.00008494955],"about_ca_topic_score_codex":0.0007496798,"about_ca_topic_score_gemma":0.0011153995,"teacher_disagreement_score":0.29749408,"about_ca_system_score_codex":0.000007194529,"about_ca_system_score_gemma":0.00004831205,"threshold_uncertainty_score":0.1410673},"labels":[],"label_agreement":null},{"id":"W2077570405","doi":"10.1080/01431160412331291297","title":"GLC2000: a new approach to global land cover mapping from Earth observation data","year":2005,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":1867,"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":"Land cover; Vegetation (pathology); General partnership; Geography; Remote sensing; Geomatics; Earth observation; Database; Product (mathematics); Environmental resource management; Cover (algebra); Cartography; Land use; Computer science; Satellite; Political science; Environmental science; Engineering","score_opus":0.04154139217003906,"score_gpt":0.2669772099775927,"score_spread":0.22543581780755367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077570405","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6208564,0.00005028921,0.35812008,0.0068618534,0.0010163771,0.00011423331,0.00002825964,0.000026317372,0.012926152],"genre_scores_gemma":[0.1796734,0.000019277228,0.81526625,0.002212871,0.0020223444,1.759119e-9,0.000056326717,0.000012322173,0.0007371772],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99804187,0.000055072036,0.0004401029,0.0003045396,0.0009692468,0.00018914115],"domain_scores_gemma":[0.9990265,0.00004723341,0.00033352312,0.0003085936,0.000096429496,0.00018775694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031133837,0.00015150559,0.00018365227,0.000047992868,0.000051762832,0.00016982244,0.00071274413,0.00007887035,0.00010995976],"category_scores_gemma":[0.00023719617,0.00012539513,0.000070096336,0.00023127104,0.0000322756,0.00081981655,0.00035766696,0.00021073264,0.00038995594],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000881894,0.00003720936,0.0025212436,0.0000011387326,0.000119085234,0.00006396891,0.00051055604,0.057605907,0.011880249,0.000005600942,0.03732361,0.8898432],"study_design_scores_gemma":[0.0010167486,0.000025787282,0.09919907,0.00021492528,0.000041024705,0.0012198034,0.00008804685,0.359614,0.0008926625,0.0006023866,0.53676176,0.0003237755],"about_ca_topic_score_codex":0.0016960817,"about_ca_topic_score_gemma":0.0002933685,"teacher_disagreement_score":0.88951945,"about_ca_system_score_codex":0.00042699926,"about_ca_system_score_gemma":0.000046298934,"threshold_uncertainty_score":0.5113465},"labels":[],"label_agreement":null},{"id":"W2077986734","doi":"10.1080/01431160600840978","title":"Multi‐year circumpolar assessment of the area burnt in boreal ecosystems using SPOT‐VEGETATION","year":2007,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":38,"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":"Russian Academy of Sciences","keywords":"Boreal; Biome; Vegetation (pathology); Remote sensing; Environmental science; Satellite; Taiga; Physical geography; Circumpolar star; Climatology; Ecosystem; Geography; Geology; Forestry; Ecology; Oceanography","score_opus":0.01337795055693926,"score_gpt":0.26767637682214995,"score_spread":0.2542984262652107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077986734","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7486398,0.000015095257,0.2501667,0.000049978247,0.00046708834,0.000052874522,4.7594455e-7,0.0000018874922,0.0006060584],"genre_scores_gemma":[0.8083657,0.000013394638,0.19149804,0.00004352348,0.000058313883,4.1212393e-9,5.171402e-7,0.0000088755005,0.000011642823],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984981,0.000049953873,0.00053235685,0.000107125314,0.0006693433,0.00014315694],"domain_scores_gemma":[0.999212,0.000042747204,0.00056377175,0.000101724414,0.000030294308,0.000049447342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007245891,0.000095049574,0.000145289,0.000030737334,0.000035920297,0.000016084316,0.0002274916,0.00005260849,0.000019339936],"category_scores_gemma":[0.00003633884,0.00007402164,0.000104190134,0.00011866158,0.00009030978,0.00017136896,0.00009791764,0.0002046635,0.0000016100119],"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":[0.000048913535,0.00011064989,0.42090297,0.000007683753,0.0000656427,0.00016674325,0.0007946398,0.3952283,0.11174466,0.000014322084,0.000005394274,0.070910096],"study_design_scores_gemma":[0.0004789561,0.000017575432,0.5057859,0.00013655816,0.00001122848,0.00026811892,0.0003559141,0.49157512,0.0011115099,0.000081105354,0.00011209529,0.00006591496],"about_ca_topic_score_codex":0.0008628567,"about_ca_topic_score_gemma":0.00032029886,"teacher_disagreement_score":0.11063314,"about_ca_system_score_codex":0.0009773461,"about_ca_system_score_gemma":0.000023149132,"threshold_uncertainty_score":0.3018515},"labels":[],"label_agreement":null},{"id":"W2078287670","doi":"10.1080/01431161.2014.965284","title":"Mapping simulated error due to terrain slope in airborne lidar observations","year":2014,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":8,"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 Lethbridge; Dalhousie University","funders":"","keywords":"Terrain; Lidar; Ranging; Remote sensing; Computer science; Raised-relief map; Geodesy; Algorithm; Geology; Geography","score_opus":0.02186376563516619,"score_gpt":0.27063216419037767,"score_spread":0.2487683985552115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078287670","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84384835,0.0000050690037,0.1451996,0.00714188,0.0004304943,0.00009210827,0.0000013009336,0.000019467003,0.0032617226],"genre_scores_gemma":[0.8628573,0.000001796585,0.13562061,0.0011154023,0.00022980556,9.506356e-9,0.0000034165728,0.000017033499,0.00015466915],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998379,0.00009723502,0.0005830631,0.00020222785,0.0005294687,0.00020898569],"domain_scores_gemma":[0.99912316,0.00013680436,0.0002808914,0.00019051883,0.00013322012,0.00013541438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066997536,0.00013262397,0.00020702384,0.0002733744,0.00009055799,0.000098268036,0.00032972652,0.000063756386,0.000051091585],"category_scores_gemma":[0.00047616803,0.00012889135,0.000091600064,0.0004407217,0.000066088374,0.00019046114,0.000112014364,0.00025973393,0.000116524425],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000476933,0.000044789045,0.0010299833,0.0000024658652,0.000041067047,0.00013096625,0.0031256126,0.08081313,0.071213394,0.000026435477,0.0008058955,0.84271854],"study_design_scores_gemma":[0.0013922172,0.00011929061,0.23959288,0.00057350623,0.000019197276,0.0011644603,0.0006829119,0.57878417,0.0051508555,0.0044115763,0.16760561,0.0005033388],"about_ca_topic_score_codex":0.0007111695,"about_ca_topic_score_gemma":0.00038932372,"teacher_disagreement_score":0.84221524,"about_ca_system_score_codex":0.0003175696,"about_ca_system_score_gemma":0.000030203337,"threshold_uncertainty_score":0.52560365},"labels":[],"label_agreement":null},{"id":"W2080849056","doi":"10.1080/01431160512331326567","title":"Usefulness and limits of MODIS GPP for estimating wheat yield","year":2005,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":112,"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":"McMaster University; U.S. Department of Agriculture; National Aeronautics and Space Administration","keywords":"Yield (engineering); Moderate-resolution imaging spectroradiometer; Environmental science; Productivity; Climatology; Physical geography; Statistics; Mathematics; Geography; Satellite; Geology; Economics","score_opus":0.023421792290894406,"score_gpt":0.266000275147829,"score_spread":0.24257848285693456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080849056","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8269175,0.00003974613,0.16897191,0.00211942,0.0006677326,0.000077316836,0.0000026240748,0.000008076537,0.0011956281],"genre_scores_gemma":[0.5742393,0.000009287053,0.42518738,0.00012476095,0.00035978397,3.1772913e-9,6.7934144e-7,0.000007652904,0.000071159375],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881357,0.000022508619,0.00042599876,0.00013127732,0.00047175205,0.00013487741],"domain_scores_gemma":[0.9990487,0.00020401184,0.00043377368,0.00007913599,0.00016341273,0.00007095086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030715668,0.00011049661,0.00018587222,0.00006702796,0.000052024396,0.00004769291,0.00017223164,0.00006441828,0.000018468356],"category_scores_gemma":[0.00042015727,0.00008771837,0.000096151256,0.000068415175,0.000085116684,0.00027795474,0.000062853294,0.00014882362,0.0000039930196],"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":[0.0000566173,0.000016457334,0.00017371368,0.000007365399,0.00005045645,0.000019387939,0.00062401744,0.022373222,0.08901757,0.000008331099,0.0010952323,0.88655764],"study_design_scores_gemma":[0.00095450244,0.00013756458,0.0053808396,0.0007687035,0.00006520986,0.002809585,0.00023336707,0.86702883,0.11524097,0.0013024639,0.005809407,0.00026853918],"about_ca_topic_score_codex":0.00007242937,"about_ca_topic_score_gemma":0.00006337895,"teacher_disagreement_score":0.8862891,"about_ca_system_score_codex":0.00014024322,"about_ca_system_score_gemma":0.000011973255,"threshold_uncertainty_score":0.35770515},"labels":[],"label_agreement":null},{"id":"W2083264558","doi":"10.1080/01431161.2013.770580","title":"Remote-sensing assessment of glacier fluctuations in the Hindu Raj, Pakistan","year":2013,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Impact","funders":"National Oceanic and Atmospheric Administration; United States Agency for International Development; National Aeronautics and Space Administration","keywords":"Glacier; Advanced Spaceborne Thermal Emission and Reflection Radiometer; Surge; Glacier terminus; Geology; Physical geography; Climate change; Climatology; Glacier mass balance; Glacier morphology; Altitude (triangle); Geography; Remote sensing; Geomorphology; Oceanography; Cryosphere; Ice stream; Digital elevation model","score_opus":0.024328786540062087,"score_gpt":0.29509464850334877,"score_spread":0.27076586196328667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083264558","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93276995,0.00018995783,0.056978118,0.005168613,0.0010778686,0.00011753959,0.0000070482265,0.0000049187233,0.0036859966],"genre_scores_gemma":[0.88310647,0.00009563604,0.1160187,0.00046624886,0.0002617143,1.6163965e-9,0.00000777378,0.0000029066964,0.00004053976],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9984733,0.000093433955,0.0005628767,0.000093913375,0.0006325986,0.00014387624],"domain_scores_gemma":[0.9984699,0.00040552937,0.00039996393,0.00011337572,0.00057125115,0.00004000823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005757541,0.00009265334,0.0001698701,0.000101059806,0.000094056355,0.0001018584,0.00024286036,0.000035205852,0.000205762],"category_scores_gemma":[0.00017865263,0.000063277024,0.00010217324,0.00022796991,0.00006498655,0.00022083704,0.00001507273,0.00022798828,0.000007771416],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013511208,0.000009197098,0.009787776,0.000003828874,0.000084358486,0.00005996031,0.0011470327,0.004600185,0.0005033996,0.00004527355,0.00046905887,0.9832764],"study_design_scores_gemma":[0.00031904245,0.000045382018,0.6907505,0.00010650796,0.000017572196,0.00018863611,0.0025152883,0.29797998,0.000032760996,0.0028398726,0.0051239673,0.00008052366],"about_ca_topic_score_codex":0.006037008,"about_ca_topic_score_gemma":0.0022574759,"teacher_disagreement_score":0.9831959,"about_ca_system_score_codex":0.00003205456,"about_ca_system_score_gemma":0.00009213644,"threshold_uncertainty_score":0.91261905},"labels":[],"label_agreement":null},{"id":"W2084461873","doi":"10.1080/01431160701266818","title":"DEM‐optical‐radar data integration for palaeohydrological mapping in the northern Darfur, Sudan: implication for groundwater exploration","year":2007,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Groundwater and Watershed Analysis","field":"Environmental Science","cited_by":41,"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":"University of Arizona","keywords":"Shuttle Radar Topography Mission; Pluvial; Geology; Structural basin; Altitude (triangle); Physical geography; Drainage basin; Holocene; Climate change; Digital elevation model; Geography; Geomorphology; Remote sensing; Oceanography; Cartography","score_opus":0.05378977233362997,"score_gpt":0.30113127389198996,"score_spread":0.24734150155836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084461873","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38890207,0.0000062262143,0.60743463,0.0031996882,0.000199735,0.00014513597,0.0000028344764,0.0000044993853,0.00010516163],"genre_scores_gemma":[0.9174602,0.000009527365,0.0816804,0.0003263763,0.00037037866,2.6530398e-7,0.00012547773,0.000010036732,0.000017313088],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845,0.00005235816,0.00060377474,0.00022463022,0.0004588699,0.00021039654],"domain_scores_gemma":[0.99905026,0.00025532907,0.00029098112,0.00021676555,0.00014394913,0.000042687534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023764132,0.00011626386,0.00015196047,0.00014257741,0.00009370072,0.00013771595,0.0005873136,0.00006686085,0.000007767587],"category_scores_gemma":[0.00021011946,0.00007572044,0.000112710746,0.00014094418,0.000062608895,0.00073718675,0.00008827213,0.00014453506,0.0000066010007],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000310683,0.00009109747,0.002736401,0.00000569801,0.0000937313,0.000036548416,0.0028030346,0.001068829,0.07672628,0.00012830582,0.00025388144,0.9157455],"study_design_scores_gemma":[0.005668149,0.0008890285,0.061640814,0.00033586286,0.0004010843,0.0016235065,0.009768482,0.715222,0.047841165,0.0961893,0.059305027,0.0011155902],"about_ca_topic_score_codex":0.00019648587,"about_ca_topic_score_gemma":0.0013202814,"teacher_disagreement_score":0.91462994,"about_ca_system_score_codex":0.0002465915,"about_ca_system_score_gemma":0.000010734183,"threshold_uncertainty_score":0.308779},"labels":[],"label_agreement":null},{"id":"W2087049991","doi":"10.1080/01431160902893501","title":"Two-dimensional tomographic retrieval of MIPAS/ENVISAT measurements of ozone and related species","year":2010,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Atmospheric Ozone and Climate","field":"Earth and Planetary Sciences","cited_by":14,"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":"Atmospheric sounding; Atmosphere (unit); Ozone; Environmental science; Depth sounding; Remote sensing; Michelson interferometer; Atmospheric sciences; Nadir; Meteorology; Satellite; Interferometry; Geology; Physics; Optics","score_opus":0.018102294157790175,"score_gpt":0.23990251670324733,"score_spread":0.22180022254545717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087049991","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99577415,0.0002592564,0.00007232046,0.00033036745,0.0013373022,0.000031900992,0.0000075250314,0.0000039855217,0.002183171],"genre_scores_gemma":[0.9743979,0.00004803721,0.025318272,0.000041440813,0.00013741826,3.7297396e-10,0.0000054599927,0.0000034939444,0.00004797064],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9983934,0.000046194506,0.000561204,0.00010012799,0.0007823016,0.00011674453],"domain_scores_gemma":[0.9986147,0.00011477008,0.0006121313,0.000078320954,0.00050082424,0.00007924271],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061361445,0.00009576583,0.0002271139,0.00008566952,0.00003646645,0.000025015757,0.00014772612,0.00005725377,0.00038616225],"category_scores_gemma":[0.00016514915,0.00007752212,0.00010922518,0.0001476455,0.0001803433,0.00020954569,0.000017680288,0.00024555973,0.0000032507073],"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":[0.001594693,0.000079089616,0.18534687,0.000039178136,0.0011002058,0.00038898716,0.0009143106,0.005150755,0.48141393,0.000058343463,0.00016373004,0.3237499],"study_design_scores_gemma":[0.0044829203,0.00072271365,0.8326344,0.00074509054,0.0002477674,0.00537932,0.0006801712,0.04228902,0.106099986,0.0056625046,0.00056982366,0.0004863015],"about_ca_topic_score_codex":0.00028730856,"about_ca_topic_score_gemma":0.00016680827,"teacher_disagreement_score":0.6472875,"about_ca_system_score_codex":0.0000043199125,"about_ca_system_score_gemma":0.000054757562,"threshold_uncertainty_score":0.4228206},"labels":[],"label_agreement":null},{"id":"W2087158567","doi":"10.1080/01431160110113962","title":"Canadian Arctic vegetation mapping","year":2002,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":38,"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 Science Foundation","keywords":"Circumpolar star; Vegetation (pathology); Arctic; Arctic vegetation; Terrain; Scale (ratio); Physical geography; Tundra; Remote sensing; Geography; Geology; Cartography; Oceanography","score_opus":0.04347948501043875,"score_gpt":0.24019945165065834,"score_spread":0.1967199666402196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087158567","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9722899,0.0014110534,0.0013729518,0.007356799,0.0037125782,0.000041684707,0.000030488374,0.000008186,0.013776312],"genre_scores_gemma":[0.99525815,0.00020551728,0.0029236847,0.0007961898,0.0007023739,4.0628464e-10,0.000025487001,0.0000023668929,0.000086240405],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929756,0.000023650506,0.0002109734,0.000059348527,0.00027560254,0.00013288394],"domain_scores_gemma":[0.9993496,0.00005469637,0.00014084576,0.000041004307,0.00028588282,0.00012795306],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00015669582,0.000057424,0.00007885644,0.00027171685,0.0000542734,0.00008550468,0.0001225236,0.00003009935,0.001376941],"category_scores_gemma":[0.00007005525,0.000051481442,0.000057078898,0.00007942403,0.000014947647,0.00019520249,0.0000028335267,0.0001197996,0.00015999375],"study_design_candidate":"design_other","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":[0.000010112778,0.0000025950521,0.028632758,0.000009243927,0.00005588684,0.0007221515,0.0015414332,0.0007774776,0.00051619124,0.0000037286657,0.00055803783,0.96717036],"study_design_scores_gemma":[0.00077739713,0.00009787761,0.25907537,0.00081521296,0.00003016419,0.0063510146,0.00076386856,0.6768664,0.00021783382,0.0021019967,0.05258061,0.0003222158],"about_ca_topic_score_codex":0.0902265,"about_ca_topic_score_gemma":0.40458256,"teacher_disagreement_score":0.9668482,"about_ca_system_score_codex":0.000049467097,"about_ca_system_score_gemma":0.00003285848,"threshold_uncertainty_score":0.9995359},"labels":[],"label_agreement":null},{"id":"W2088169575","doi":"10.1080/01431160600794621","title":"Detecting grassland spatial variation by a wavelet approach","year":2006,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":40,"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 Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Parks Canada","keywords":"Transect; Grassland; Environmental science; Spatial variability; Normalized Difference Vegetation Index; Topographic Wetness Index; Vegetation (pathology); Leaf area index; Enhanced vegetation index; Elevation (ballistics); Spatial ecology; Scale (ratio); Remote sensing; Physical geography; Soil science; Hydrology (agriculture); Geology; Geography; Digital elevation model; Ecology; Vegetation Index; Cartography; Mathematics; Statistics","score_opus":0.004313433458352486,"score_gpt":0.20016609952693157,"score_spread":0.1958526660685791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088169575","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5418306,0.00002089823,0.44747877,0.0004891908,0.0008191345,0.00005768378,0.000002055762,0.00001885307,0.009282815],"genre_scores_gemma":[0.83822584,0.0000048943566,0.16070254,0.00008426186,0.0007705714,2.9313298e-9,0.000009035163,0.000013733178,0.0001891162],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982282,0.000077707205,0.00045999265,0.00018096335,0.0008688559,0.00018427866],"domain_scores_gemma":[0.9991039,0.000059933656,0.00056048896,0.00009199912,0.00012624449,0.000057418834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038742417,0.00014212582,0.00016150539,0.00007415487,0.00008171823,0.00012349748,0.00022098332,0.00009075717,0.00003021695],"category_scores_gemma":[0.00012773239,0.00011424884,0.000110866575,0.00013483176,0.00005382237,0.00023351553,0.000071050315,0.00028263513,0.000021981186],"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":[0.000078957506,0.00006083907,0.0007870711,0.0000032814758,0.00007871789,0.00015431887,0.00033729972,0.008513936,0.40972254,0.000006382423,0.009118659,0.571138],"study_design_scores_gemma":[0.0031713133,0.00020959396,0.09349274,0.00026737346,0.000133311,0.012107753,0.00027868425,0.7689203,0.07649838,0.005151571,0.03880226,0.00096673553],"about_ca_topic_score_codex":0.0021366226,"about_ca_topic_score_gemma":0.00015859894,"teacher_disagreement_score":0.7604064,"about_ca_system_score_codex":0.00036799084,"about_ca_system_score_gemma":0.000012907666,"threshold_uncertainty_score":0.46589324},"labels":[],"label_agreement":null},{"id":"W2091738548","doi":"10.1080/01431160701294653","title":"Estimating afternoon MODIS land surface temperatures (LST) based on morning MODIS overpass, location and elevation information","year":2007,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Urban Heat Island Mitigation","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":"Natural Resources Canada; Canadian Forest Service; University of British Columbia","funders":"Canadian Space Agency","keywords":"Environmental science; Moderate-resolution imaging spectroradiometer; Morning; Elevation (ballistics); Land cover; Climatology; Meteorology; Daytime; Atmospheric sciences; Insolation; Satellite; Remote sensing; Geography; Land use; Geology","score_opus":0.0056314291697205894,"score_gpt":0.22982873626946607,"score_spread":0.22419730709974547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091738548","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6760304,0.000009956746,0.32262263,0.00038938803,0.00034836985,0.00006447904,0.0000021698743,0.000012188069,0.0005204339],"genre_scores_gemma":[0.91830194,0.0000052970063,0.08113395,0.00033909793,0.00017541285,1.8921153e-8,0.000020507086,0.000008860063,0.000014900983],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985604,0.000038431797,0.0004453576,0.000110837216,0.0007048533,0.0001400785],"domain_scores_gemma":[0.99911386,0.00013584185,0.00041858226,0.000079655656,0.00018162123,0.00007045525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073043926,0.000118955984,0.00011092345,0.00015097027,0.00008738392,0.00013915623,0.00009558076,0.000061421066,0.000017792041],"category_scores_gemma":[0.00031231236,0.00010928853,0.000038072405,0.00013712776,0.00004016059,0.0009091412,0.0000310257,0.00019703589,0.000013810446],"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":[0.0004387154,0.000031069456,0.027795188,0.000023551358,0.000042890864,0.0000534197,0.0013500224,0.5635152,0.030630331,0.00002674552,0.0005075937,0.37558526],"study_design_scores_gemma":[0.0006827384,0.00007493059,0.059463296,0.00029311783,0.000014932888,0.00014570405,0.00006401364,0.9303378,0.008024921,0.00037528295,0.00039368667,0.00012956288],"about_ca_topic_score_codex":0.00023241778,"about_ca_topic_score_gemma":0.000059003578,"teacher_disagreement_score":0.3754557,"about_ca_system_score_codex":0.00037078734,"about_ca_system_score_gemma":0.00002424332,"threshold_uncertainty_score":0.4456657},"labels":[],"label_agreement":null},{"id":"W2092002643","doi":"10.1080/0143116031000150095","title":"Mapping subalpine forest types using networks of nearest neighbour classifiers","year":2004,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Geospatial analysis; Computer science; Classifier (UML); Remote sensing; Montane ecology; Data mining; Ranging; Artificial intelligence; Geography; Ecology","score_opus":0.014277717789838764,"score_gpt":0.2413184504276618,"score_spread":0.22704073263782304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092002643","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8038633,0.000069243535,0.19249463,0.00073113444,0.0012846662,0.000053972166,9.4884683e-7,0.00001101717,0.0014911086],"genre_scores_gemma":[0.86523956,0.00004304247,0.13399361,0.00015959912,0.0005102466,1.1367849e-9,0.000002001138,0.000017091637,0.00003483461],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982439,0.000046026522,0.00058255123,0.00016377149,0.0007360114,0.00022771755],"domain_scores_gemma":[0.998759,0.000052864754,0.0007397892,0.00013219533,0.00020884383,0.00010735383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030464702,0.0001612706,0.00024870742,0.00013062057,0.00006880472,0.00006363459,0.00032000188,0.00010954419,0.000031938496],"category_scores_gemma":[0.00016644009,0.00013186781,0.0001752371,0.0002741139,0.00016979153,0.00027564692,0.00012260326,0.00033218894,0.000010152747],"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":[0.00006728618,0.000026793561,0.0022457733,0.000004663085,0.00012360093,0.00034769138,0.0004529103,0.81838197,0.08620881,0.000029107134,0.000157151,0.09195425],"study_design_scores_gemma":[0.0029822476,0.00023231503,0.10059036,0.0020973014,0.00011592366,0.00970223,0.0011344523,0.8532303,0.017279677,0.0055551534,0.006324316,0.00075571315],"about_ca_topic_score_codex":0.0006619457,"about_ca_topic_score_gemma":0.00018766339,"teacher_disagreement_score":0.098344594,"about_ca_system_score_codex":0.0005591442,"about_ca_system_score_gemma":0.00004669635,"threshold_uncertainty_score":0.5377413},"labels":[],"label_agreement":null},{"id":"W2092482879","doi":"10.1080/01431161.2010.489060","title":"Rock type classification of drill core using continuous wavelet analysis applied to thermal infrared reflectance spectra","year":2011,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Laurentian University; University of Alberta","funders":"Networks of Centres of Excellence of Canada","keywords":"Endmember; Wavelet; Mineralogy; Remote sensing; Geology; Scale (ratio); Spectral line; Hyperspectral imaging; Computer science; Artificial intelligence; Physics","score_opus":0.04906064676643471,"score_gpt":0.282069802876811,"score_spread":0.2330091561103763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092482879","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8284395,0.000055952547,0.16677956,0.000017095033,0.00048982556,0.00003410852,0.0000025470902,0.000021001697,0.0041604135],"genre_scores_gemma":[0.9269934,0.0000047892577,0.072682805,0.000034276138,0.00021783457,1.1959514e-8,0.0000029418263,0.000018713323,0.000045200763],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891835,0.000015894317,0.00047864203,0.00009967745,0.0003540051,0.00013343079],"domain_scores_gemma":[0.99899745,0.000023283728,0.0003264316,0.00010234035,0.0004911294,0.000059347927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020511865,0.000110907655,0.00026129722,0.00049974496,0.000038362108,0.000026274849,0.00019738165,0.000045459223,0.000014629701],"category_scores_gemma":[0.000044329772,0.000109720095,0.00009993401,0.00045631762,0.000012310025,0.00010392526,0.00002588065,0.00019979652,0.0000026238647],"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":[0.00021074832,0.000014090261,0.00021314269,0.000013230016,0.00073356985,0.000047620368,0.002126219,0.22544023,0.73102385,0.000064760374,0.00007682662,0.04003573],"study_design_scores_gemma":[0.0004302088,0.000086482825,0.004039667,0.00019079602,0.00025419449,0.0002855697,0.00044170686,0.9467028,0.04635572,0.00076772814,0.00019961769,0.00024553802],"about_ca_topic_score_codex":0.000019518997,"about_ca_topic_score_gemma":0.000008040432,"teacher_disagreement_score":0.7212625,"about_ca_system_score_codex":0.00015356789,"about_ca_system_score_gemma":0.000035018504,"threshold_uncertainty_score":0.44742557},"labels":[],"label_agreement":null},{"id":"W2093280389","doi":"10.1080/01431160902926574","title":"Non-destructive estimation of wheat leaf chlorophyll content from hyperspectral measurements through analytical model inversion","year":2010,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":45,"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; University of New Brunswick","funders":"Agriculture and Agri-Food Canada","keywords":"Canopy; Leaf area index; Chlorophyll; Environmental science; Agronomy; Growing season; Hyperspectral imaging; Mathematics; Remote sensing; Botany; Biology; Geography","score_opus":0.03380997256936492,"score_gpt":0.2657893885286183,"score_spread":0.23197941595925337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093280389","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9531695,0.0000064536184,0.04256468,0.00081524556,0.00116802,0.00007934991,0.0000043554837,0.000008577728,0.00218379],"genre_scores_gemma":[0.73643947,0.000006416542,0.2632091,0.00011413558,0.00018758842,2.7747995e-9,0.000004430065,0.000010636559,0.000028230499],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99773926,0.00003413702,0.00054065906,0.0002209057,0.001291955,0.00017307005],"domain_scores_gemma":[0.998786,0.00005054363,0.00055605755,0.00016463814,0.00034050844,0.00010230233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023732804,0.00017371007,0.00026847495,0.00007355378,0.0000681049,0.00006527096,0.0003163429,0.00012575308,0.00005367559],"category_scores_gemma":[0.00022405646,0.00013763754,0.00018767397,0.00011898023,0.00025129796,0.0005112856,0.000102875616,0.0004739562,0.000018908922],"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":[0.00019217939,0.00004570308,0.00074913586,0.000002030003,0.00013722198,0.00004878914,0.0012140187,0.059263855,0.8813653,0.000018428143,0.00027420226,0.056689154],"study_design_scores_gemma":[0.0008607057,0.00006905606,0.011474736,0.00014432098,0.00007265899,0.00028148806,0.00022966042,0.8179615,0.16432849,0.004373149,0.00004129198,0.00016295635],"about_ca_topic_score_codex":0.0012122769,"about_ca_topic_score_gemma":0.00010765673,"teacher_disagreement_score":0.7586976,"about_ca_system_score_codex":0.000395553,"about_ca_system_score_gemma":0.00004617196,"threshold_uncertainty_score":0.5612696},"labels":[],"label_agreement":null},{"id":"W2098054319","doi":"10.1080/01431161.2014.999165","title":"Object-based larch tree-crown delineation using high-resolution satellite imagery","year":2015,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Larch; Satellite imagery; Remote sensing; Crown (dentistry); Satellite; Computer science; Tree (set theory); Aerial imagery; Artificial intelligence; Object based; Computer vision; Geology; Object (grammar); Cartography; Geography; Mathematics","score_opus":0.0291811944125894,"score_gpt":0.286905725853756,"score_spread":0.2577245314411666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098054319","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5073769,0.000060361253,0.4892292,0.0009582981,0.00071416755,0.00006136937,0.000001951149,0.000021449463,0.0015763085],"genre_scores_gemma":[0.7112972,0.0000139460135,0.28788656,0.00024093504,0.000443835,5.7825646e-9,0.000010465223,0.000016910568,0.00009014508],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980114,0.00011869919,0.00053441204,0.00019221668,0.000942616,0.00020066935],"domain_scores_gemma":[0.9987256,0.000078876335,0.0004658029,0.00017526252,0.0003910458,0.00016342997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076688675,0.00014253437,0.00017466453,0.00019846529,0.0000890724,0.00011264477,0.0002240286,0.000076537064,0.000023428704],"category_scores_gemma":[0.00024186059,0.00013412056,0.00012350509,0.00024109973,0.00012075052,0.00029569736,0.00006360937,0.00023487794,0.000075332915],"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":[0.00019089034,0.00003902709,0.0004030139,0.0000019426363,0.00004244217,0.00010053445,0.00028668708,0.066447295,0.088510975,0.000009763258,0.0006744955,0.84329295],"study_design_scores_gemma":[0.0012898538,0.00008584422,0.0045763794,0.00015192837,0.000062299085,0.001056047,0.0001933958,0.9451056,0.026798887,0.0012391823,0.019168533,0.00027203027],"about_ca_topic_score_codex":0.0014841666,"about_ca_topic_score_gemma":0.0001476701,"teacher_disagreement_score":0.87865835,"about_ca_system_score_codex":0.00071055273,"about_ca_system_score_gemma":0.00011570953,"threshold_uncertainty_score":0.5469278},"labels":[],"label_agreement":null},{"id":"W2098452952","doi":"10.1080/0143116031000072948","title":"Estimation of the moisture content of bare soil from RADARSAT-1 SAR using simple empirical models","year":2003,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","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":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Synthetic aperture radar; Remote sensing; Water content; Environmental science; Backscatter (email); Champion; Radar; Mean squared error; Surface roughness; Empirical modelling; Soil science; Geology; Mathematics; Computer science; Statistics; Materials science; Geography","score_opus":0.044220035911988635,"score_gpt":0.2821888599944218,"score_spread":0.23796882408243314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098452952","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90349424,0.000091362126,0.0937401,0.00031123345,0.000720382,0.000057092617,0.0000036616327,0.0000036914253,0.001578233],"genre_scores_gemma":[0.92313033,0.000011431506,0.07652445,0.00021108013,0.000093750816,4.9077387e-10,0.0000017112293,0.00001298524,0.000014236357],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980613,0.00013907939,0.00062957755,0.00013829039,0.0009005516,0.00013119266],"domain_scores_gemma":[0.99888086,0.00011941146,0.00058238255,0.00017404593,0.00018494889,0.00005835591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030151824,0.00013176414,0.0002598416,0.00006564126,0.000061046,0.000022294911,0.00023833441,0.00008823873,0.000016803164],"category_scores_gemma":[0.00028737684,0.000091521775,0.00022774824,0.00013977144,0.00016470859,0.00020771696,0.0000848537,0.00023235008,0.0000013330082],"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":[0.00011548003,0.000058491878,0.004561654,0.0000073582087,0.00021813494,0.000060209197,0.0021641501,0.5427077,0.15865234,0.000026116419,0.00022949993,0.29119888],"study_design_scores_gemma":[0.0009948511,0.00004729466,0.023159033,0.00044120292,0.00012466032,0.00069297286,0.0008811656,0.77391225,0.18640392,0.012518359,0.00063211564,0.00019219198],"about_ca_topic_score_codex":0.0019050258,"about_ca_topic_score_gemma":0.0002640718,"teacher_disagreement_score":0.29100668,"about_ca_system_score_codex":0.00024297132,"about_ca_system_score_gemma":0.00006369786,"threshold_uncertainty_score":0.37321496},"labels":[],"label_agreement":null},{"id":"W2099867025","doi":"10.1080/01431160701311291","title":"Improved topographic correction of forest image data using a 3‐D canopy reflectance model in multiple forward mode","year":2007,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Forest Service; University of Lethbridge","funders":"","keywords":"Terrain; Remote sensing; Canopy; Scale (ratio); Pixel; Bidirectional reflectance distribution function; Vegetation (pathology); Tree canopy; Geology; Reflectivity; Environmental science; Geography; Cartography; Physics; Optics","score_opus":0.02956448803068986,"score_gpt":0.3160503694935734,"score_spread":0.28648588146288356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099867025","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54372597,0.000010458834,0.4552279,0.00008264773,0.00030406064,0.00005286027,0.0000039109077,0.000005194205,0.0005870015],"genre_scores_gemma":[0.7508712,0.000019223278,0.24893688,0.000043499316,0.00008653256,3.4279488e-9,0.0000057731263,0.000012485779,0.000024430137],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848574,0.0000298537,0.0006172031,0.00021140878,0.0004587358,0.00019705977],"domain_scores_gemma":[0.99884075,0.00009971626,0.0005424228,0.00029250255,0.00015230136,0.0000723073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007170461,0.00011419446,0.00018553814,0.00025025735,0.000053878204,0.000030043595,0.00039392436,0.00006538077,0.0000036931706],"category_scores_gemma":[0.0002377036,0.00011261786,0.000080102094,0.00030121373,0.000120246,0.0003832587,0.00014000593,0.0002508235,0.0000013789636],"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":[0.00020937738,0.000039862065,0.0019058351,0.0000030805916,0.00003169312,0.00003517335,0.0003740976,0.16297647,0.66172993,0.0000034101433,0.00007205603,0.17261899],"study_design_scores_gemma":[0.00045177987,0.000022129418,0.0017146718,0.00010786494,0.000015853719,0.00027271564,0.000112353846,0.9782879,0.018083366,0.0006417179,0.00019065052,0.000099018056],"about_ca_topic_score_codex":0.0032740994,"about_ca_topic_score_gemma":0.008391804,"teacher_disagreement_score":0.8153114,"about_ca_system_score_codex":0.00032722205,"about_ca_system_score_gemma":0.000053878313,"threshold_uncertainty_score":0.49494806},"labels":[],"label_agreement":null},{"id":"W2100125369","doi":"10.1080/01431160500181812","title":"Satellite‐derived ecosystems classification: image segmentation by ecological region for improved classification accuracy, a boreal case study","year":2006,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Parks Canada","keywords":"Thematic Mapper; Segmentation; Context (archaeology); Remote sensing; Computer science; Image segmentation; Pattern recognition (psychology); Boreal; Contextual image classification; Statistic; Artificial intelligence; Satellite imagery; Ecology; Geography; Mathematics; Image (mathematics); Statistics; Biology","score_opus":0.022970347045118255,"score_gpt":0.285286007216536,"score_spread":0.2623156601714178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100125369","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9029682,0.000029787492,0.09226325,0.00214275,0.0009292197,0.0007420171,0.000010214669,0.000039597777,0.00087496184],"genre_scores_gemma":[0.94046766,0.000033197462,0.05852891,0.0001146076,0.000622818,2.9214746e-7,0.00008177821,0.000024286912,0.00012645609],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99759626,0.00021754685,0.000981092,0.00037940787,0.0005787396,0.0002469461],"domain_scores_gemma":[0.9976014,0.00028308475,0.0013688506,0.00020264638,0.00044425638,0.00009978669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006011029,0.0002377526,0.00027664544,0.00010574855,0.00018916783,0.00026061112,0.00028596618,0.00014426532,0.000014062684],"category_scores_gemma":[0.00022789638,0.00019146326,0.00017731705,0.00018523716,0.00010381273,0.00053838326,0.00006281649,0.00025499635,0.0000138429705],"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":[0.0002005234,0.00031136585,0.0008879597,0.0000061457645,0.000090678666,0.00094826595,0.00057867623,0.0001477055,0.68443733,0.0000062974605,0.008122239,0.3042628],"study_design_scores_gemma":[0.010432512,0.0018127194,0.11683973,0.0002738678,0.00048277728,0.08096025,0.018778875,0.67067176,0.061396632,0.0024557065,0.03426323,0.0016319357],"about_ca_topic_score_codex":0.0011307553,"about_ca_topic_score_gemma":0.0009260731,"teacher_disagreement_score":0.67052406,"about_ca_system_score_codex":0.0009559574,"about_ca_system_score_gemma":0.000033042936,"threshold_uncertainty_score":0.7807645},"labels":[],"label_agreement":null},{"id":"W2101065971","doi":"10.1080/0143116032000160507","title":"Temporal analysis of forest structural condition at an acid mine site using multispectral digital camera imagery","year":2004,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","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":"Carleton University","funders":"National Geographic Society","keywords":"Environmental science; Vegetation (pathology); Remote sensing; Multispectral image; Tailings; Canopy; Shrub; Geology; Geography; Ecology","score_opus":0.00910959670256883,"score_gpt":0.26090786297673907,"score_spread":0.25179826627417023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101065971","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9840049,0.000020357002,0.015014798,0.00019308102,0.00048724154,0.000058475296,0.00003460339,0.000012814304,0.0001737315],"genre_scores_gemma":[0.91184074,0.0000039534684,0.087718174,0.00006657441,0.00023606018,1.5613847e-9,0.00009191918,0.000014361187,0.000028244092],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979336,0.000040274383,0.000652482,0.00022864161,0.00092845905,0.00021653553],"domain_scores_gemma":[0.99857414,0.000034714645,0.00088047667,0.00015447097,0.00021832499,0.00013785869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016226659,0.00019566064,0.00035157107,0.00029705127,0.00008582246,0.0001243861,0.0002302453,0.00008370543,0.00007181104],"category_scores_gemma":[0.00009793471,0.00015980365,0.0003309053,0.00041397123,0.0002180921,0.0009933606,0.00011151779,0.00021566435,0.0000073285714],"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":[0.00018523209,0.000039074617,0.043198086,0.0000028684815,0.0006949565,0.0006568359,0.0010050097,0.35200316,0.5759686,0.0000036546046,0.000036644684,0.026205879],"study_design_scores_gemma":[0.0019334866,0.00022336778,0.443695,0.0002216345,0.0006987121,0.006277798,0.0003520275,0.48022527,0.06472915,0.00096433563,0.00014292961,0.00053630903],"about_ca_topic_score_codex":0.0013253357,"about_ca_topic_score_gemma":0.0016884173,"teacher_disagreement_score":0.51123947,"about_ca_system_score_codex":0.0009778398,"about_ca_system_score_gemma":0.000028035547,"threshold_uncertainty_score":0.6516604},"labels":[],"label_agreement":null},{"id":"W2102061966","doi":"10.1080/01431160410001705042","title":"Associations between Woodland Caribou telemetry data and Landsat TM spectral reflectance","year":2004,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Alberta Pacific Forest Industries; Alberta Environment and Protected Areas; University of Alberta","funders":"Alberta-Pacific Forest Industries","keywords":"Woodland caribou; Thematic Mapper; Habitat; Remote sensing; Woodland; Geography; Land cover; Environmental science; Ecology; Satellite imagery; Land use","score_opus":0.025929757631927845,"score_gpt":0.29049439059516025,"score_spread":0.2645646329632324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102061966","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9811126,0.000032473028,0.01174175,0.0057226433,0.000368876,0.000029028328,0.000030917967,0.0000072221196,0.0009544704],"genre_scores_gemma":[0.9697084,0.00003720355,0.029331787,0.0004249286,0.00042290328,3.3389516e-9,0.00003503772,0.0000054369557,0.00003434044],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99915504,0.000032722783,0.00027081708,0.00012655334,0.0003054426,0.00010939487],"domain_scores_gemma":[0.99942875,0.00008719194,0.00027915393,0.00010558527,0.0000479818,0.00005135004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039882417,0.00006593775,0.000119162534,0.000056308672,0.00008072924,0.000044689397,0.00024785678,0.00005481808,0.000023841167],"category_scores_gemma":[0.00024329171,0.00006180296,0.000026745478,0.0000857232,0.000057412857,0.00036093415,0.00012401499,0.00019381983,0.000011560085],"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":[0.00004272293,0.00002228949,0.90549755,0.0000012226508,0.00020047343,0.00019001523,0.0004708446,0.0005782627,0.00141035,0.000037450944,0.0013021779,0.090246655],"study_design_scores_gemma":[0.00077575725,0.00004762809,0.9874848,0.000043414526,0.00004184728,0.00039634507,0.000040962022,0.0012397986,0.00035622212,0.006958821,0.0025105083,0.000103903876],"about_ca_topic_score_codex":0.00024391848,"about_ca_topic_score_gemma":0.0007259559,"teacher_disagreement_score":0.09014275,"about_ca_system_score_codex":0.00022940677,"about_ca_system_score_gemma":0.00003487351,"threshold_uncertainty_score":0.25202516},"labels":[],"label_agreement":null},{"id":"W2104045645","doi":"10.1080/01431160412331291242","title":"Technical Note: Crop stress detection using AVIRIS hyperspectral imagery and artificial neural networks","year":2004,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Hyperspectral imaging; Environmental science; Remote sensing; Vegetation (pathology); Imaging spectrometer; Artificial neural network; Nutrient; Spectrometer; Geology; Artificial intelligence; Computer science; Ecology; Biology","score_opus":0.011145944984742072,"score_gpt":0.2546423491028338,"score_spread":0.2434964041180917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104045645","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7012567,0.00003748959,0.2967447,0.0006530963,0.0010527457,0.00004942777,9.346938e-7,0.000019589206,0.00018533187],"genre_scores_gemma":[0.86133015,0.000020801044,0.13757287,0.00013796458,0.00091383985,2.2326816e-9,9.986173e-7,0.000016410613,0.000006976089],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983865,0.00005742127,0.00045408157,0.00022801338,0.00063478615,0.0002392001],"domain_scores_gemma":[0.9992346,0.00004795327,0.0003724658,0.00010583241,0.00011978985,0.00011932756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026102658,0.00017205061,0.00019226216,0.000101625315,0.00013086325,0.00017396445,0.00018130426,0.00013516613,0.000011807619],"category_scores_gemma":[0.00014526949,0.00014419995,0.00013091789,0.00017446002,0.00021555812,0.00037305735,0.00012025397,0.0005499199,0.000004810493],"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":[0.00008673654,0.000020741396,0.000076860546,0.0000011048454,0.00001531055,0.00046323647,0.00012389841,0.22394954,0.45589924,0.0000024607793,0.000013992479,0.31934687],"study_design_scores_gemma":[0.0007506838,0.00013188356,0.009033145,0.00022169718,0.00007871748,0.019682316,0.00016748454,0.8823163,0.085486755,0.0015872932,0.0001651927,0.00037851205],"about_ca_topic_score_codex":0.00041037187,"about_ca_topic_score_gemma":0.00025978082,"teacher_disagreement_score":0.6583668,"about_ca_system_score_codex":0.0006930734,"about_ca_system_score_gemma":0.000018245397,"threshold_uncertainty_score":0.58803034},"labels":[],"label_agreement":null},{"id":"W2105602976","doi":"10.1080/01431160701313826","title":"Comparison and improvement of wavelet‐based image fusion","year":2007,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Wavelet; Wavelet transform; Artificial intelligence; Image fusion; Stationary wavelet transform; Discrete wavelet transform; Wavelet packet decomposition; Second-generation wavelet transform; Pattern recognition (psychology); Orthogonal wavelet; Biorthogonal wavelet; Lifting scheme; Computer vision; Computer science; Biorthogonal system; Transformation (genetics); Fusion; Mathematics; Image (mathematics)","score_opus":0.010009726990960028,"score_gpt":0.301327414056103,"score_spread":0.291317687065143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105602976","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3408512,0.00009779968,0.6581855,0.000065844455,0.00031082568,0.00003129609,0.0000011001847,0.000025468564,0.00043097715],"genre_scores_gemma":[0.60792637,0.000033140463,0.39191058,0.0000331793,0.0000815313,2.1298976e-9,7.752048e-7,0.000010024052,0.000004399866],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999018,0.000006206897,0.00049120694,0.000056646608,0.0003298565,0.00009807647],"domain_scores_gemma":[0.99916846,0.00006882053,0.0002351889,0.000065600696,0.000413453,0.00004848909],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000327452,0.00008411772,0.00016388574,0.0002462746,0.000014361312,0.00001737319,0.00009522887,0.00003528819,0.000008770711],"category_scores_gemma":[0.00007386318,0.0000796353,0.00005367281,0.000054698812,0.000035878664,0.00012561276,0.00003170085,0.00016394061,5.5029227e-7],"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":[0.000026739812,0.000005184762,0.000016908767,0.000007981674,0.000014582794,0.000038882416,0.00004688111,0.00008621763,0.5378001,0.000002094806,0.00005774162,0.46189672],"study_design_scores_gemma":[0.0003701617,0.00007307143,0.00071422354,0.000205002,0.000008324404,0.00011050876,0.00008145835,0.09186732,0.9051635,0.00030984782,0.0010230922,0.00007348543],"about_ca_topic_score_codex":0.000009503193,"about_ca_topic_score_gemma":0.000004689191,"teacher_disagreement_score":0.46182323,"about_ca_system_score_codex":0.00009505745,"about_ca_system_score_gemma":0.00001180868,"threshold_uncertainty_score":0.3247433},"labels":[],"label_agreement":null},{"id":"W2106723523","doi":"10.1080/01431161.2015.1004764","title":"Spatially explicit estimation of aboveground boreal forest biomass in the Yukon River Basin, Alaska","year":2015,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":10,"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":"Taiga; Boreal; Environmental science; Vegetation (pathology); Remote sensing; Latitude; Structural basin; Drainage basin; Biomass (ecology); Physical geography; Geology; Forestry; Geography; Oceanography; Geomorphology; Cartography","score_opus":0.019469818454454252,"score_gpt":0.2711766154488155,"score_spread":0.25170679699436127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106723523","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88345623,0.000014635666,0.10768525,0.0019196146,0.0002804911,0.00008285965,0.0000018633345,0.000005595327,0.0065534604],"genre_scores_gemma":[0.94190717,0.00000900988,0.057708267,0.00018140601,0.00015228316,1.3503969e-8,0.000006154548,0.000009586606,0.000026133726],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983319,0.000099076635,0.00048325176,0.00012016513,0.00083858747,0.00012701328],"domain_scores_gemma":[0.99903774,0.00012109843,0.0004597905,0.00016367779,0.00015332454,0.000064384374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007769812,0.00010277568,0.00015136907,0.00013244002,0.00003408621,0.00005358429,0.0003271038,0.00005291456,0.000006192288],"category_scores_gemma":[0.0002012577,0.000077050274,0.000084343155,0.0002120291,0.00012559697,0.00024275506,0.00005578611,0.0001653645,0.000019700847],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018133827,0.000116401105,0.0067161447,0.000005198239,0.000064763844,0.00016286586,0.0067715333,0.035928313,0.0062355944,0.00017734415,0.0019921777,0.9416483],"study_design_scores_gemma":[0.001981004,0.00024271333,0.4516025,0.00029517486,0.00006397029,0.0020844918,0.0010971426,0.5080913,0.006341574,0.019254746,0.00862731,0.00031807498],"about_ca_topic_score_codex":0.0042148368,"about_ca_topic_score_gemma":0.0008167658,"teacher_disagreement_score":0.94133025,"about_ca_system_score_codex":0.00023670837,"about_ca_system_score_gemma":0.000048831622,"threshold_uncertainty_score":0.63716006},"labels":[],"label_agreement":null},{"id":"W2108094322","doi":"10.1080/01431161.2014.960614","title":"The Jeffries–Matusita distance for the case of complex Wishart distribution as a separability criterion for fully polarimetric SAR data","year":2014,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":106,"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 Calgary; Environment and Climate Change Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency; ArcticNet","keywords":"Wishart distribution; Polarimetry; Pattern recognition (psychology); Artificial intelligence; Synthetic aperture radar; Covariance matrix; Inverse-Wishart distribution; Covariance; Computer science; Selection (genetic algorithm); Mathematics; Algorithm; Statistics; Machine learning; Multivariate statistics; Physics","score_opus":0.027047626393578286,"score_gpt":0.31575158469511355,"score_spread":0.2887039583015353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108094322","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010813918,0.00043794131,0.9854191,0.0022097186,0.00037140865,0.00025646036,0.00036312494,0.000025196801,0.00010315076],"genre_scores_gemma":[0.7384117,0.00006601711,0.26114276,0.000046583344,0.0002467013,1.00549634e-7,0.000066783774,0.000012798561,0.000006514346],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991058,0.0000308178,0.00043752984,0.00011160136,0.00019361464,0.00012061712],"domain_scores_gemma":[0.9973009,0.0013973056,0.00024513016,0.00038389073,0.0006397205,0.00003303974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097051053,0.000100347934,0.00015567402,0.00004831557,0.00017118933,0.000084577696,0.0004519578,0.000045213656,0.0000028020656],"category_scores_gemma":[0.001037606,0.00006554254,0.000108539156,0.000104438426,0.000090813424,0.00014887679,0.00005785475,0.00011920956,3.4155175e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000082258826,0.000010335061,0.000004798651,0.000017811573,0.000103201615,0.000004406319,0.000039837258,0.00003170208,0.00089187396,0.0019177182,0.0022202427,0.9946758],"study_design_scores_gemma":[0.00017996065,0.00003640504,0.00013379288,0.00005168457,0.000043026834,0.0009178821,0.00006313814,0.4222468,0.0020061126,0.0043898104,0.569873,0.000058418864],"about_ca_topic_score_codex":0.0001351434,"about_ca_topic_score_gemma":0.00004449468,"teacher_disagreement_score":0.9946174,"about_ca_system_score_codex":0.00010970185,"about_ca_system_score_gemma":0.000028201945,"threshold_uncertainty_score":0.26727474},"labels":[],"label_agreement":null},{"id":"W2109931483","doi":"10.1080/01431160701408477","title":"ASTER DEMs for geomatic and geoscientific applications: a review","year":2008,"lang":"en","type":"review","venue":"International Journal of Remote Sensing","topic":"Satellite Image Processing and Photogrammetry","field":"Engineering","cited_by":172,"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":"","keywords":"Remote sensing; Advanced Spaceborne Thermal Emission and Reflection Radiometer; Digital elevation model; Geomatics; Stereoscopy; Geospatial analysis; Orthophoto; Digitization; Photogrammetry; Geolocation; Satellite; Georeference; Terrain; Computer science; Earth observation; Satellite imagery; Elevation (ballistics); Geographic information system; Geology; Geography; Cartography; Artificial intelligence; Computer vision","score_opus":0.0351450420129456,"score_gpt":0.33071896089812003,"score_spread":0.2955739188851744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109931483","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000012986427,0.80058545,0.19820607,0.000022688453,0.0007575395,0.0002916286,0.00001585249,0.00002526632,0.00009420306],"genre_scores_gemma":[0.0000015840566,0.9269839,0.07233039,0.000078456345,0.00047860536,6.882846e-7,0.000029532901,0.000047795555,0.00004902333],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99848783,0.00003145631,0.00086895406,0.0001504177,0.0003096336,0.0001517153],"domain_scores_gemma":[0.9986188,0.000236703,0.0004941014,0.00013488795,0.00043673918,0.00007879971],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000324281,0.00023363715,0.00079130393,0.00035477357,0.000049247476,0.00013153686,0.00025577465,0.00010465645,0.0000030243843],"category_scores_gemma":[0.00012618153,0.00019530645,0.00036273777,0.00020801678,0.000047756846,0.00011089948,0.000025636757,0.00029215182,0.000007853635],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011424374,0.0000032933287,4.5430756e-8,0.0139934085,0.00018011924,0.000019649573,0.000016887483,0.0000033277743,0.0000026375915,5.733674e-7,0.0009051818,0.9848737],"study_design_scores_gemma":[0.00009728792,0.000007850383,1.0228599e-7,0.062708326,0.00026807952,0.0059753372,0.0000034795548,0.00323756,0.0000060561215,0.00011635991,0.9274188,0.00016072165],"about_ca_topic_score_codex":0.0000028941017,"about_ca_topic_score_gemma":7.039448e-7,"teacher_disagreement_score":0.984713,"about_ca_system_score_codex":0.0000968975,"about_ca_system_score_gemma":0.00008150102,"threshold_uncertainty_score":0.7964366},"labels":[],"label_agreement":null},{"id":"W2111943769","doi":"10.1080/01431160601105900","title":"On the link between SAR‐derived sea ice melt and development of the summer upper ocean mixed layer in the North Open Water Polynya","year":2007,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","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":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of Manitoba","funders":"ArcticNet; University of Manitoba","keywords":"Mixed layer; Sea ice; Salinity; Stratification (seeds); Geology; Oceanography; Climatology; Temperature salinity diagrams; Environmental science","score_opus":0.031164259274431814,"score_gpt":0.2595411008071663,"score_spread":0.2283768415327345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111943769","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9884709,0.000010685023,0.0012772132,0.009088772,0.00029173555,0.00008300322,0.000004137243,0.0000010654677,0.000772488],"genre_scores_gemma":[0.99344826,0.000010204333,0.0050982255,0.0011874768,0.0002156137,1.2391042e-9,0.000008468585,0.0000029807518,0.000028793882],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.998821,0.00010850605,0.00035764265,0.00008143119,0.00047888028,0.00015253181],"domain_scores_gemma":[0.9991268,0.00044273434,0.00019354622,0.000088682275,0.000113462986,0.00003477172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013255873,0.000086438195,0.00011772102,0.000056131106,0.0001414556,0.00006995873,0.0005195672,0.000031742144,0.000035843997],"category_scores_gemma":[0.00007572462,0.00003535735,0.000044919747,0.00006617822,0.000082775936,0.00009708926,0.00006864092,0.00028241143,0.0000045657707],"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":[0.00014079569,0.0000049287164,0.46279562,0.000003072477,0.000118174634,0.000047950078,0.0064392714,0.00039859547,0.00007670998,0.000014577275,0.00006317083,0.52989715],"study_design_scores_gemma":[0.00036054893,0.0000330934,0.98822886,0.00011225829,0.000017872493,0.00016364649,0.001473917,0.003169143,0.0013612679,0.00050757144,0.004487106,0.00008470395],"about_ca_topic_score_codex":0.00073217775,"about_ca_topic_score_gemma":0.0027146942,"teacher_disagreement_score":0.5298124,"about_ca_system_score_codex":0.000015468639,"about_ca_system_score_gemma":0.00005658087,"threshold_uncertainty_score":0.15148632},"labels":[],"label_agreement":null},{"id":"W2112380459","doi":"10.1080/01431160600857394","title":"Evaluation of the potential of various spectral indices and textural features derived from satellite images for surficial deposits mapping","year":2006,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","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":"Université de Sherbrooke; Université de Moncton","funders":"","keywords":"Normalized Difference Vegetation Index; Geology; Remote sensing; Linear discriminant analysis; Vegetation (pathology); Standard deviation; Soil water; Soil science; Mathematics; Statistics","score_opus":0.008684917899094511,"score_gpt":0.23543749029300848,"score_spread":0.22675257239391397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112380459","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99120957,0.00036194923,0.006578848,0.0005033098,0.00073578284,0.00014279387,0.000008411653,0.0000036519587,0.0004556799],"genre_scores_gemma":[0.9491609,0.000024851137,0.05036305,0.00002870136,0.00039348681,5.3132907e-9,0.0000052186806,0.000007661377,0.000016171814],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9980502,0.00013517418,0.0004336934,0.00013124202,0.0011352915,0.00011435964],"domain_scores_gemma":[0.99864036,0.00009931474,0.00079774973,0.00007492983,0.00036206705,0.00002557696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005409712,0.000111340545,0.00017698442,0.00006084944,0.00006557009,0.000054677734,0.00019649867,0.00006946665,0.0000067013334],"category_scores_gemma":[0.00015728422,0.00007469367,0.00013722474,0.00008952742,0.00014601104,0.00016208133,0.00007316256,0.00014515381,3.6311258e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006291983,0.000013998033,0.0018538794,0.00000285356,0.000088496374,0.000009231056,0.00031994042,0.007100348,0.73811394,0.0000023303373,0.000078357116,0.2523537],"study_design_scores_gemma":[0.0006304015,0.000027928976,0.77626705,0.0001574696,0.00014619697,0.00039722188,0.000137262,0.014491157,0.20435931,0.003253652,0.000048494505,0.000083842475],"about_ca_topic_score_codex":0.0015158589,"about_ca_topic_score_gemma":0.00073786615,"teacher_disagreement_score":0.77441317,"about_ca_system_score_codex":0.00015874287,"about_ca_system_score_gemma":0.000032408116,"threshold_uncertainty_score":0.30459195},"labels":[],"label_agreement":null},{"id":"W2115076670","doi":"10.1080/014311601750038857","title":"Evaluation of C-band SAR data for wetlands mapping","year":2001,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":208,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Glaucoma Research Society of Canada","keywords":"Wetland; Remote sensing; Polarimetry; Bog; Synthetic aperture radar; Environmental science; Vegetation classification; Polarization (electrochemistry); Vegetation (pathology); Peat; Geology; Geography; Scattering; Physics; Ecology","score_opus":0.057042976001375825,"score_gpt":0.321736391645533,"score_spread":0.2646934156441572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115076670","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040705122,0.00038740772,0.95544446,0.0005457963,0.00042710008,0.0001099544,0.000018402894,0.000026887003,0.002334886],"genre_scores_gemma":[0.48397076,0.00021411396,0.51541007,0.000025993862,0.0003390224,1.5685403e-8,0.000019849203,0.0000133568565,0.0000068550034],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988865,0.000021164953,0.00037853824,0.00008254484,0.00055068877,0.000080548656],"domain_scores_gemma":[0.9985137,0.00010847487,0.00018290606,0.0002075738,0.00095906877,0.000028268285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011546534,0.000076532444,0.00013985448,0.00018058682,0.000022054015,0.000023578958,0.00028711875,0.00004698678,0.000012541247],"category_scores_gemma":[0.00023004082,0.00007038683,0.000060417387,0.00007772942,0.000018827053,0.00013669605,0.00002272225,0.00008565027,7.0780334e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011143233,0.0000066027583,0.000013376667,0.000005526921,0.00013256096,0.0000033047083,0.00005435119,0.00016983891,0.0054030097,0.000030022771,0.00073467195,0.99343556],"study_design_scores_gemma":[0.0004063306,0.000015156253,0.00011363622,0.00018286543,0.0000755623,0.0004064644,0.000059981005,0.58842367,0.01289009,0.0031016897,0.39425266,0.000071899165],"about_ca_topic_score_codex":0.000011342424,"about_ca_topic_score_gemma":0.000005075218,"teacher_disagreement_score":0.9933637,"about_ca_system_score_codex":0.00010527969,"about_ca_system_score_gemma":0.00004573694,"threshold_uncertainty_score":0.28702918},"labels":[],"label_agreement":null},{"id":"W2117031873","doi":"10.1080/01431160500238844","title":"Lacunarity analysis to determine optimum extents for sample‐based spatial information extraction from high‐resolution forest imagery","year":2006,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":25,"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":"Natural Sciences and Engineering Research Council of Canada; Canadian Natural Resources Limited; National Geographic Society","keywords":"Lacunarity; Taiga; Sample (material); Temperate rainforest; Temperate climate; Physical geography; Boreal; Cartography; Remote sensing; Geography; Environmental science; Forestry; Mathematics; Ecology; Fractal","score_opus":0.012551177172608967,"score_gpt":0.26618988756420386,"score_spread":0.2536387103915949,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117031873","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4089755,0.000002056748,0.58971083,0.0007001303,0.00032881624,0.00010203117,0.000039749622,0.000012246565,0.00012858964],"genre_scores_gemma":[0.6704479,0.0000014987752,0.32884645,0.00016678561,0.00034171363,6.908067e-8,0.00017283655,0.0000072108915,0.000015529695],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983884,0.000041620235,0.0006154915,0.00016356673,0.0006190207,0.00017186947],"domain_scores_gemma":[0.99866116,0.00023732115,0.0005761133,0.000161483,0.00027483606,0.00008911445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003217322,0.00013340509,0.00019641354,0.0003335493,0.00012233411,0.00015601622,0.00018254243,0.000071296396,0.000050884726],"category_scores_gemma":[0.00023054874,0.00013154214,0.000225537,0.00027620693,0.000043436517,0.00051144743,0.000043048338,0.00012829294,0.000030097339],"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":[0.0003034206,0.000050710023,0.0024607773,0.0000018143804,0.00014881362,0.0000098604705,0.00007657186,0.33518928,0.027624689,0.0000063990888,0.0006434587,0.6334842],"study_design_scores_gemma":[0.00060248235,0.000047269627,0.16421455,0.000028404615,0.00019344398,0.000028974353,0.000021885988,0.8221957,0.0050717858,0.0017719042,0.005678493,0.00014508274],"about_ca_topic_score_codex":0.025428917,"about_ca_topic_score_gemma":0.003000326,"teacher_disagreement_score":0.6333391,"about_ca_system_score_codex":0.00042822908,"about_ca_system_score_gemma":0.000026729462,"threshold_uncertainty_score":0.98106086},"labels":[],"label_agreement":null},{"id":"W2120044765","doi":"10.1080/01431160902821809","title":"Recent extremes in total ozone content over the northern parts of India in view of the Montreal Protocol","year":2009,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Atmospheric Ozone and Climate","field":"Earth and Planetary Sciences","cited_by":3,"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":"Montreal Protocol; Ozone; Environmental science; Latitude; Percentile; Ozone layer; Longitude; Meteorology; Climatology; Geography; Statistics; Geology; Mathematics","score_opus":0.023302908489261042,"score_gpt":0.2566168830060741,"score_spread":0.23331397451681307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120044765","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9959322,0.00021445636,0.00004615114,0.0011486149,0.00021077871,0.0013686729,0.000004259363,0.0000012063848,0.0010736212],"genre_scores_gemma":[0.9990632,0.000115805866,0.0005430195,0.00018205678,0.00007392108,2.1171205e-7,0.0000010486648,0.00000175562,0.000018961735],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9987183,0.00009889653,0.00055242865,0.0000674598,0.00045128955,0.00011159224],"domain_scores_gemma":[0.9991254,0.00007514606,0.0005172018,0.00008255797,0.00017454781,0.000025142239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038677856,0.00007542139,0.00017786681,0.000029057092,0.000018110877,0.000016342587,0.00020361991,0.000029889788,0.00009018317],"category_scores_gemma":[0.00007431689,0.000040398572,0.00008420717,0.00014235784,0.000049565773,0.00010558867,0.000011768927,0.00016757227,0.0000011068131],"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":[0.00020455582,0.00002810573,0.12251009,0.0000037241525,0.000022355405,0.00005501532,0.00057342206,0.0027502673,0.0001834878,0.0000028504394,0.000016158125,0.87364995],"study_design_scores_gemma":[0.00072174636,0.00009948335,0.98826236,0.0003425915,0.0000062675354,0.00014565312,0.00029263037,0.008373348,0.00031520863,0.0004967224,0.0008955616,0.000048434224],"about_ca_topic_score_codex":0.002335319,"about_ca_topic_score_gemma":0.010170554,"teacher_disagreement_score":0.87360156,"about_ca_system_score_codex":0.000019074394,"about_ca_system_score_gemma":0.00007445148,"threshold_uncertainty_score":0.56754076},"labels":[],"label_agreement":null},{"id":"W2120429645","doi":"10.1080/01431160600821010","title":"Validation of chlorophyll fluorescence derived from MERIS on the west coast of Canada","year":2007,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"University of Washington; Canadian Space Agency; National Oceanic and Atmospheric Administration; University of British Columbia; Fisheries and Oceans Canada; San Francisco State University","keywords":"Radiance; Remote sensing; Environmental science; Chlorophyll a; Buoy; Satellite; Chlorophyll; Chlorophyll fluorescence; Absorption (acoustics); Fluorescence; Atmosphere (unit); Chemistry; Geology; Physics; Meteorology; Oceanography; Optics; Astronomy","score_opus":0.0109114118168851,"score_gpt":0.20459650830933948,"score_spread":0.19368509649245438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120429645","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99450076,0.000027542457,0.0022245154,0.0004083758,0.0008891795,0.000029338875,0.00003124244,0.0000013367188,0.0018876996],"genre_scores_gemma":[0.997768,0.000014732317,0.0018650036,0.00009357932,0.00023145512,5.589185e-10,0.000010441083,0.0000018855442,0.0000148902745],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998777,0.000041144907,0.0004179091,0.000060509672,0.0006147638,0.00008871467],"domain_scores_gemma":[0.9986348,0.00031983678,0.00054756936,0.00007245877,0.0003784581,0.00004688235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039907952,0.000062891,0.00013119607,0.00006863479,0.000031406606,0.000017075557,0.00020368626,0.000020856876,0.00015535812],"category_scores_gemma":[0.00015294793,0.000042702715,0.000052613163,0.000071824084,0.000029985584,0.000072840834,0.000011287564,0.00010763983,0.0000016499729],"study_design_candidate":"design_other","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":[0.0006186677,0.000015881,0.024989905,0.000011830054,0.00021939274,0.00025181865,0.0006382887,0.003923607,0.042495344,0.000021847538,0.00033475616,0.9264787],"study_design_scores_gemma":[0.0007227823,0.0003065454,0.40868214,0.0006943202,0.000041386258,0.00032740302,0.0023674725,0.042247117,0.54151946,0.00072900264,0.0021369772,0.0002253794],"about_ca_topic_score_codex":0.5078731,"about_ca_topic_score_gemma":0.5030568,"teacher_disagreement_score":0.92625326,"about_ca_system_score_codex":0.00001515023,"about_ca_system_score_gemma":0.00016115434,"threshold_uncertainty_score":0.5060111},"labels":[],"label_agreement":null},{"id":"W2120888261","doi":"10.1080/01431161003777197","title":"The effects of combining classifiers with the same training statistics using Bayesian decision rules","year":2011,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote-Sensing Image Classification","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":"Toronto Metropolitan University","funders":"","keywords":"Classifier (UML); Artificial intelligence; Quadratic classifier; Pattern recognition (psychology); Margin classifier; Computer science; Probabilistic classification; Random subspace method; Machine learning; Naive Bayes classifier; Bayes classifier; Support vector machine","score_opus":0.026964945614490816,"score_gpt":0.2552951824018456,"score_spread":0.2283302367873548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120888261","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2829975,0.00013529036,0.7151842,0.00007771604,0.0012168097,0.00006383266,0.0000018476177,0.000018270066,0.00030453372],"genre_scores_gemma":[0.6760346,0.000054644406,0.32371834,0.00002691215,0.00012352206,8.537305e-9,9.424193e-7,0.000031680465,0.000009305833],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984581,0.000106129664,0.00052591856,0.00009725362,0.0006184815,0.00019412175],"domain_scores_gemma":[0.9972675,0.0013562866,0.0004993165,0.00019107833,0.00062402757,0.00006180859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000645351,0.00015778765,0.00021369968,0.0001671181,0.00013634712,0.0001054311,0.00032539843,0.00005718738,0.0000017602342],"category_scores_gemma":[0.0006368684,0.00009716635,0.000081107915,0.0001244893,0.00020213524,0.00017713515,0.000028185765,0.00035522666,0.0000012319034],"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":[0.00017566366,0.000007125892,0.000026930555,0.000020226067,0.00034345416,0.00016676317,0.004283324,0.0034775643,0.034349244,0.0002736869,0.000121242425,0.95675474],"study_design_scores_gemma":[0.0010228037,0.00015613603,0.0028767914,0.0016574775,0.00017910141,0.0016862882,0.0026139435,0.95841575,0.02368383,0.0066047637,0.00085469155,0.00024842945],"about_ca_topic_score_codex":0.000021940252,"about_ca_topic_score_gemma":0.000022524247,"teacher_disagreement_score":0.9565064,"about_ca_system_score_codex":0.00016129976,"about_ca_system_score_gemma":0.000083518964,"threshold_uncertainty_score":0.39623287},"labels":[],"label_agreement":null},{"id":"W2121825012","doi":"10.1080/01431160110082444","title":"Spectral and spatial artifacts from the use of desktop scanners for remote sensing","year":2001,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Scanner; Computer science; Remote sensing; Image quality; Computer vision; Computer graphics (images); Artificial intelligence; Image (mathematics); Geology","score_opus":0.04566458276960258,"score_gpt":0.26617463572715927,"score_spread":0.22051005295755668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121825012","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4926555,0.00006566516,0.50527114,0.0009286712,0.0009151243,0.00007175204,0.000005453147,0.000020322517,0.000066382665],"genre_scores_gemma":[0.73411,0.00015034298,0.26485077,0.000102287624,0.0007267454,1.3124193e-9,0.000006623425,0.000032921595,0.00002032014],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985742,0.000056786917,0.00060990563,0.00014492885,0.0004161089,0.00019805817],"domain_scores_gemma":[0.9981975,0.0005390253,0.00037032377,0.0001839516,0.00062931125,0.00007986905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002900799,0.00016542265,0.00024921328,0.00016566305,0.000057989942,0.00016663606,0.00014421016,0.000079424695,0.000002948097],"category_scores_gemma":[0.0006523202,0.00014265593,0.00013800492,0.00010029454,0.00010198002,0.00028720417,0.000027189653,0.0002481948,0.0000016697004],"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":[0.00014784254,0.0000032780654,0.00005250588,0.000005854241,0.00017890871,0.000083338025,0.00035952582,0.012446717,0.11587093,0.000002485462,0.0003174194,0.8705312],"study_design_scores_gemma":[0.00058435695,0.00003209911,0.0036763884,0.00037984338,0.00006627607,0.0009917476,0.00009751127,0.9529211,0.033779725,0.0010185923,0.006306233,0.00014614515],"about_ca_topic_score_codex":0.00079115253,"about_ca_topic_score_gemma":0.00031512158,"teacher_disagreement_score":0.9404744,"about_ca_system_score_codex":0.00017146442,"about_ca_system_score_gemma":0.00004621512,"threshold_uncertainty_score":0.581734},"labels":[],"label_agreement":null},{"id":"W2127211078","doi":"10.1080/01431160050144965","title":"Satellite-based mapping of Canadian boreal forest fires: Evaluation and comparison of algorithms","year":2000,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":90,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Forest Service; Environment and Climate Change Canada","funders":"","keywords":"Taiga; Remote sensing; Normalized Difference Vegetation Index; Boreal; Satellite; Environmental science; Compositing; Vegetation (pathology); Meteorology; Algorithm; Sampling (signal processing); Land cover; Pixel; Boreal ecosystem; Physical geography; Geography; Computer science; Forestry; Geology; Land use; Climate change","score_opus":0.021564727615026202,"score_gpt":0.2763516655805064,"score_spread":0.2547869379654802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127211078","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99319905,0.00014458524,0.001514024,0.00038183475,0.00026274638,0.00010451269,0.0000045617535,0.000002608277,0.004386097],"genre_scores_gemma":[0.98170066,0.000032175332,0.018125268,0.00004227604,0.00007406522,1.5680257e-8,0.0000055056553,0.000007960341,0.000012048489],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984346,0.00009659923,0.00051529956,0.00009805197,0.00073734764,0.00011812412],"domain_scores_gemma":[0.999136,0.00011582221,0.00041787577,0.000080541235,0.00015011044,0.00009969119],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077495276,0.00008187533,0.00019795609,0.0002997224,0.000029083662,0.000020809392,0.00014318201,0.000047381247,0.000089975416],"category_scores_gemma":[0.000113675604,0.00007777207,0.00006039583,0.00017732987,0.00008124232,0.00013832733,0.000013711789,0.000102307014,0.000005397728],"study_design_candidate":"design_other","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":[0.000037790975,0.000013466801,0.025422955,0.000008528495,0.00003407132,0.000018168092,0.00059565506,0.004609838,0.0031872536,0.0000011468734,0.000072442475,0.9659987],"study_design_scores_gemma":[0.0006043646,0.00008990346,0.21192722,0.00035005016,0.000021803102,0.00011341368,0.00009180261,0.7778634,0.0052166255,0.00013899617,0.0035041876,0.000078220524],"about_ca_topic_score_codex":0.111155,"about_ca_topic_score_gemma":0.07698551,"teacher_disagreement_score":0.96592045,"about_ca_system_score_codex":0.00029437768,"about_ca_system_score_gemma":0.000074651296,"threshold_uncertainty_score":0.9398571},"labels":[],"label_agreement":null},{"id":"W2132099602","doi":"10.1080/01431160902755346","title":"The impact of imperfect ground reference data on the accuracy of land cover change estimation","year":2009,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":63,"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","keywords":"Change detection; Land cover; Ground truth; Reference data; Data set; Remote sensing; Computer science; Estimation; Cover (algebra); Set (abstract data type); Environmental science; Land use; Statistics; Data mining; Mathematics; Geography; Artificial intelligence; Ecology","score_opus":0.049585614243971024,"score_gpt":0.3291342431927547,"score_spread":0.2795486289487837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132099602","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99377054,0.000049606017,0.0015230986,0.0024794687,0.00026125548,0.00010443207,0.000010694922,0.0000039309707,0.0017969612],"genre_scores_gemma":[0.9956546,0.00014463575,0.0038600182,0.00013154164,0.0001734288,2.3495155e-9,0.000007319644,0.0000051591514,0.000023340235],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9985343,0.00010798674,0.00038672634,0.000116602685,0.0007349821,0.00011939396],"domain_scores_gemma":[0.9980791,0.00057258416,0.0008342923,0.0003321783,0.00014572602,0.000036091864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007099502,0.000110162924,0.00014608585,0.00003956309,0.00007591173,0.00006856837,0.0006935802,0.00004591369,0.000026234276],"category_scores_gemma":[0.00089333137,0.00005065329,0.00009617483,0.00012745762,0.00011169502,0.0003663759,0.000117190146,0.00024400196,0.000011896382],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027597693,0.000045189794,0.00050353643,0.0000019608656,0.00013237481,0.000034852117,0.0005960848,0.012038055,0.051682703,0.000034001347,0.003021232,0.931634],"study_design_scores_gemma":[0.00082446495,0.0007327056,0.50597155,0.0007454339,0.000066918125,0.0016344301,0.00011875754,0.4731751,0.010226071,0.0042654676,0.0019904412,0.0002486646],"about_ca_topic_score_codex":0.00082941836,"about_ca_topic_score_gemma":0.000042951888,"teacher_disagreement_score":0.9313854,"about_ca_system_score_codex":0.00018925268,"about_ca_system_score_gemma":0.000025555239,"threshold_uncertainty_score":0.20655812},"labels":[],"label_agreement":null},{"id":"W2132344819","doi":"10.1080/01431160903475357","title":"Prediction of summer grain crop yield with a process-based ecosystem model and remote sensing data for the northern area of the Jiangsu Province, China","year":2010,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":42,"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":"Environmental science; Leaf area index; Land cover; Crop yield; Growing season; Agriculture; Ecosystem; Crop; Remote sensing; Agricultural engineering; Land use; Geography; Agronomy; Forestry; Ecology","score_opus":0.02048901796293762,"score_gpt":0.23956806150051588,"score_spread":0.21907904353757826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132344819","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85792714,0.000018286582,0.13881268,0.002111487,0.00047115798,0.00032657007,0.00007072316,0.00000841192,0.00025352623],"genre_scores_gemma":[0.9342428,0.000006356364,0.065411724,0.000085862725,0.00016453015,5.3920117e-9,0.0000064419023,0.000019913663,0.00006236004],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982157,0.00004672856,0.00050858833,0.0002379834,0.0008334856,0.0001575157],"domain_scores_gemma":[0.997974,0.00017483855,0.0010212593,0.00043014466,0.00034535406,0.00005444365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007206492,0.00016803159,0.00022305296,0.000058952657,0.0001314836,0.000057356643,0.0005653385,0.000089750654,0.0000026167759],"category_scores_gemma":[0.00043229113,0.0000858293,0.00009373059,0.00014689028,0.00024243708,0.00024148833,0.0001463839,0.00039580098,2.9118561e-7],"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":[0.00079742225,0.000063247615,0.003009708,0.00011358001,0.0003664872,0.000040949406,0.0015852533,0.1709656,0.29166427,0.000005826808,0.00082949013,0.53055817],"study_design_scores_gemma":[0.00050398626,0.00006509713,0.004613308,0.0006468609,0.00009282229,0.0008264551,0.0001290235,0.98237175,0.009890324,0.0002891127,0.00047472966,0.00009652815],"about_ca_topic_score_codex":0.0004258456,"about_ca_topic_score_gemma":0.0072586117,"teacher_disagreement_score":0.81140614,"about_ca_system_score_codex":0.00008144667,"about_ca_system_score_gemma":0.00012202168,"threshold_uncertainty_score":0.40504757},"labels":[],"label_agreement":null},{"id":"W2133160971","doi":"10.1080/01431160701736489","title":"Review of methods of small‐footprint airborne laser scanning for extracting forest inventory data in boreal forests","year":2008,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":632,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Forest Service; Canadian Sport Centre Pacific","funders":"","keywords":"Remote sensing; Laser scanning; Taiga; Forest inventory; Lidar; Environmental science; Tree canopy; Photogrammetry; Terrain; Canopy; Footprint; Tree (set theory); Geography; Forestry; Agroforestry; Forest management; Laser; Cartography; Mathematics","score_opus":0.0809476343659227,"score_gpt":0.37335214187075544,"score_spread":0.2924045075048327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133160971","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6676974,0.0013894272,0.32781237,0.00080675044,0.0003678941,0.00023778618,0.000009258075,0.000007643459,0.0016714417],"genre_scores_gemma":[0.5156239,0.0010858614,0.48303577,0.00010502146,0.00010747244,2.4644505e-8,0.0000121999465,0.000013577902,0.000016161475],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827456,0.00011397781,0.0009096056,0.00019117368,0.00035909264,0.00015161296],"domain_scores_gemma":[0.9980565,0.00030100395,0.0010363252,0.0003246204,0.00021437732,0.00006721085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019109506,0.00011095175,0.0003146873,0.00014496993,0.000041213338,0.000008543903,0.0004955335,0.00005459986,0.000006583555],"category_scores_gemma":[0.0011026055,0.00010372989,0.00013198763,0.00018093227,0.00014628885,0.00015152131,0.00020198071,0.00022417493,9.760694e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000876754,0.00006653681,0.00793427,0.00020479488,0.000078126344,0.000038895654,0.00032171834,0.0027270333,0.008162711,0.000016196718,0.0006003144,0.9797617],"study_design_scores_gemma":[0.0033392552,0.00031953084,0.34744853,0.028564172,0.00025297244,0.0047933105,0.0004969925,0.4972812,0.05476326,0.0035371964,0.058423087,0.0007805138],"about_ca_topic_score_codex":0.00087325706,"about_ca_topic_score_gemma":0.00035768928,"teacher_disagreement_score":0.9789812,"about_ca_system_score_codex":0.00015060353,"about_ca_system_score_gemma":0.00007749134,"threshold_uncertainty_score":0.42299822},"labels":[],"label_agreement":null},{"id":"W2134392312","doi":"10.1080/01431160903464146","title":"Modelling the vegetation–climate relationship in a boreal mixedwood forest of Alberta using normalized difference and enhanced vegetation indices","year":2011,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","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":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Environmental science; Evapotranspiration; Advanced very-high-resolution radiometer; Vegetation (pathology); Normalized Difference Vegetation Index; Enhanced vegetation index; Moderate-resolution imaging spectroradiometer; Precipitation; Climatology; Boreal; Taiga; Aridity index; Arid; Climate change; Remote sensing; Meteorology; Ecology; Geography; Vegetation Index; Forestry; Geology","score_opus":0.02608409228529886,"score_gpt":0.24615482233585712,"score_spread":0.22007073005055824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134392312","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93185884,0.00006481646,0.06559979,0.000093900126,0.00024515318,0.000090045716,3.8276482e-7,0.0000037439493,0.0020433066],"genre_scores_gemma":[0.91670257,0.00005398424,0.08313706,0.00003344455,0.000053256445,1.349586e-8,0.0000018881849,0.000009619605,0.000008161056],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984814,0.00012051769,0.00059195154,0.00014617445,0.000501744,0.00015824325],"domain_scores_gemma":[0.99867463,0.00026449663,0.00078667415,0.00009754608,0.0001290665,0.00004757797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042484413,0.0001279822,0.00018145004,0.00013515902,0.00007718075,0.000041535743,0.0002060537,0.0000736111,0.0000036855297],"category_scores_gemma":[0.00015577207,0.00009020886,0.00006625004,0.00019444399,0.00014718696,0.00036134315,0.00007767036,0.00025030886,0.0000022056513],"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":[0.00050470186,0.00010858986,0.08903117,0.00006175173,0.00016792477,0.00009771115,0.049567815,0.69275117,0.058053467,0.00038180867,0.000004168811,0.10926973],"study_design_scores_gemma":[0.0005195462,0.000035606583,0.25509092,0.00047844328,0.000036035995,0.00030880564,0.00030142596,0.7339908,0.0053903,0.003733014,0.000004919667,0.00011017902],"about_ca_topic_score_codex":0.003567911,"about_ca_topic_score_gemma":0.0020356984,"teacher_disagreement_score":0.16605978,"about_ca_system_score_codex":0.00012769809,"about_ca_system_score_gemma":0.000021214788,"threshold_uncertainty_score":0.5393638},"labels":[],"label_agreement":null},{"id":"W2134415173","doi":"10.1080/01431160110113971","title":"Evidential reasoning with Landsat TM, DEM and GIS data for landcover classification in support of grizzly bear habitat mapping","year":2002,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":71,"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":"Ursus; Habitat; Satellite imagery; Remote sensing; Cartography; Classifier (UML); Thematic Mapper; Grizzly Bears; Geography; Classification scheme; Computer science; Ecology; Artificial intelligence; Machine learning; Population","score_opus":0.040877736128426274,"score_gpt":0.2652109522293063,"score_spread":0.22433321610088003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134415173","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9740799,0.00002366624,0.021916728,0.0033797582,0.00017003188,0.00006106691,0.000006685901,0.0000026440102,0.00035950134],"genre_scores_gemma":[0.95910984,0.00004594713,0.040410146,0.00025761797,0.0000976816,2.8470394e-8,0.000013076625,0.0000056051667,0.00006008245],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992688,0.000031370524,0.00027496155,0.00011851433,0.0002252338,0.000081136954],"domain_scores_gemma":[0.9994238,0.00009153335,0.00031098706,0.00009042553,0.00005628497,0.000026983424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041422652,0.00005523188,0.000106103136,0.00007921275,0.000031444815,0.000023784472,0.00015679195,0.000040325016,0.000038909413],"category_scores_gemma":[0.00018026817,0.000048272093,0.000020185009,0.0000636928,0.000056497793,0.0004503247,0.000059530386,0.00009149997,0.0000030407275],"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":[0.00028506387,0.000039457456,0.8436502,0.0000122569645,0.00009628259,0.00009174926,0.0008523804,0.0003248895,0.004464369,0.000020859303,0.004338872,0.14582361],"study_design_scores_gemma":[0.001171743,0.00009315617,0.90074235,0.00018807905,0.000028987002,0.0006226146,0.00019013954,0.08922218,0.00016381325,0.00025800313,0.007223261,0.00009569885],"about_ca_topic_score_codex":0.00006733199,"about_ca_topic_score_gemma":0.00039992374,"teacher_disagreement_score":0.14572792,"about_ca_system_score_codex":0.00006604367,"about_ca_system_score_gemma":0.000011704702,"threshold_uncertainty_score":0.19684789},"labels":[],"label_agreement":null},{"id":"W2139161099","doi":"10.1080/01431160500419303","title":"Mutual information spectra for comparing categorical maps","year":2006,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","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":false,"ca_institutions":"University of Toronto; General Electric (Canada)","funders":"Canadian Forest Service","keywords":"Categorical variable; Coincidence; Series (stratigraphy); Mutual information; Variable (mathematics); Computer science; Mathematics; Data mining; Spatial analysis; Pattern recognition (psychology); Artificial intelligence; Statistics","score_opus":0.008289718093008106,"score_gpt":0.22737193771455474,"score_spread":0.21908221962154664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139161099","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49700305,0.000021087557,0.4742406,0.0021730736,0.0024459772,0.0001594682,0.0000037427935,0.000035215882,0.023917802],"genre_scores_gemma":[0.8400795,0.0000047999692,0.15871991,0.00017489742,0.0008555243,4.7702327e-9,0.000019167566,0.000008281654,0.00013791921],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985377,0.000025009234,0.0005197228,0.00009470666,0.00064401515,0.00017881773],"domain_scores_gemma":[0.99917364,0.00006955269,0.00044858872,0.00007554863,0.0001752257,0.00005744732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027125847,0.000118333206,0.00016513352,0.00009479601,0.00006774377,0.00013251574,0.00022690823,0.000065714084,0.00001955803],"category_scores_gemma":[0.00010748643,0.00009590525,0.00013768344,0.000107402215,0.000058431888,0.00056985894,0.000059867518,0.0001946974,0.00006926481],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046976088,0.00008436068,0.0021906742,0.0000143038405,0.0001637131,0.00027320927,0.0010696017,0.06503713,0.058197957,0.00092228514,0.09236225,0.77921474],"study_design_scores_gemma":[0.0037656177,0.00025566152,0.043964054,0.00020936335,0.000106214145,0.010517231,0.00047489099,0.45315778,0.029762065,0.03503216,0.4219608,0.0007941597],"about_ca_topic_score_codex":0.00034046537,"about_ca_topic_score_gemma":0.00009689334,"teacher_disagreement_score":0.7784206,"about_ca_system_score_codex":0.00046054233,"about_ca_system_score_gemma":0.000016455433,"threshold_uncertainty_score":0.39109024},"labels":[],"label_agreement":null},{"id":"W2144362041","doi":"10.1080/01431160310001618464","title":"Examining the effect of spatial resolution and texture window size on classification accuracy: an urban environment case","year":2004,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":277,"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":"Land cover; Remote sensing; Image resolution; Contextual image classification; Geography; Window (computing); Cartography; Classifier (UML); Computer science; Texture (cosmology); Artificial intelligence; Land use; Image (mathematics)","score_opus":0.01328333869139886,"score_gpt":0.24321401907190948,"score_spread":0.22993068038051062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144362041","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98880494,0.000032149095,0.009078177,0.001157424,0.00037857762,0.00014007279,0.0000021093442,0.000007926052,0.00039862806],"genre_scores_gemma":[0.98928547,0.000028217379,0.010168822,0.0001152471,0.0003700169,1.26416095e-8,0.0000024367773,0.000012450065,0.00001730013],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99834836,0.00020976498,0.00038782475,0.00020112218,0.00071816205,0.00013474606],"domain_scores_gemma":[0.9986826,0.00039441077,0.0006183455,0.00018375226,0.000041159765,0.00007971254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006531996,0.00015673057,0.00017019128,0.000050637304,0.000119562035,0.00006101626,0.00018696228,0.000091571244,0.000015244397],"category_scores_gemma":[0.00046526926,0.000096908414,0.00006821234,0.00006600971,0.00018900791,0.00023823527,0.000072318886,0.00033967372,0.0000076602855],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039588296,0.000037146438,0.0017359679,0.0000051576762,0.00007746374,0.0009940601,0.001573038,0.03397312,0.2740766,0.0000106292255,0.000101196405,0.6870197],"study_design_scores_gemma":[0.007271865,0.004977615,0.6061,0.0012758208,0.00036018537,0.070758976,0.0017952508,0.1283956,0.17190263,0.0011951819,0.0050609577,0.0009058866],"about_ca_topic_score_codex":0.0004193842,"about_ca_topic_score_gemma":0.00007780236,"teacher_disagreement_score":0.68611383,"about_ca_system_score_codex":0.000411113,"about_ca_system_score_gemma":0.000011857947,"threshold_uncertainty_score":0.39518103},"labels":[],"label_agreement":null},{"id":"W2146831490","doi":"10.1080/01431161.2013.810825","title":"A hybrid pansharpening approach and multiscale object-based image analysis for mapping diseased pine and oak trees","year":2013,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":89,"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":"University of British Columbia","keywords":"Oversampling; Computer science; Artificial intelligence; Smoothing; Remote sensing; Multispectral image; Tree (set theory); Pattern recognition (psychology); Cartography; Computer vision; Geography; Mathematics","score_opus":0.009457026919022348,"score_gpt":0.24087820235144167,"score_spread":0.23142117543241933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146831490","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23048274,0.00030711363,0.7687216,0.0001580235,0.00007404767,0.00011952229,0.0000146147695,0.000060444123,0.00006193674],"genre_scores_gemma":[0.48941243,0.000039982988,0.5103909,0.000037751448,0.000079716825,2.1017219e-7,0.000009787837,0.000017031634,0.000012242005],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908066,0.00002120875,0.00037895504,0.00013977142,0.00023148123,0.00014795052],"domain_scores_gemma":[0.999125,0.00014052624,0.00017361327,0.00008574204,0.00037362057,0.00010152111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015635205,0.00014620736,0.00026950033,0.00050823274,0.000049799728,0.00015223469,0.00010691314,0.000029801047,0.000009177268],"category_scores_gemma":[0.00015757697,0.0001373217,0.00013356884,0.00011340791,0.00004982699,0.0003682623,0.0000334217,0.0001415023,4.532007e-7],"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":[0.00005957131,0.000019950305,0.0004647392,0.00006983378,0.00074280024,0.000067349756,0.00024004508,0.008217247,0.35069326,0.0000027651004,0.0003060632,0.6391164],"study_design_scores_gemma":[0.0007101015,0.000021842603,0.0031433487,0.00015357135,0.000101100646,0.00013422617,0.00012614335,0.9639486,0.030743489,0.00032750604,0.00043745953,0.0001526028],"about_ca_topic_score_codex":0.00003741225,"about_ca_topic_score_gemma":0.0000037487082,"teacher_disagreement_score":0.95573133,"about_ca_system_score_codex":0.00006709233,"about_ca_system_score_gemma":0.000012394239,"threshold_uncertainty_score":0.55998164},"labels":[],"label_agreement":null},{"id":"W2151372553","doi":"10.1080/01431161.2013.779041","title":"Forest inventory stand height estimates from very high spatial resolution satellite imagery calibrated with lidar plots","year":2013,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":27,"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; University of British Columbia; Canadian Forest Service","funders":"Canadian Space Agency; University of Otago","keywords":"Panchromatic film; Lidar; Remote sensing; Satellite imagery; Forest inventory; Mean squared error; Multispectral image; Environmental science; Calibration; Thematic Mapper; Random forest; Satellite; Geography; Forest management; Statistics; Computer science; Mathematics","score_opus":0.008864156834485498,"score_gpt":0.21908141918996582,"score_spread":0.21021726235548033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151372553","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9189874,0.00011752191,0.07760053,0.0011736562,0.00046657163,0.00011444877,0.0000063797524,0.000034854245,0.0014986574],"genre_scores_gemma":[0.90599936,0.00007354401,0.09320829,0.00017223408,0.00040064307,3.1428687e-8,0.000023016006,0.00002851627,0.00009435628],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99829334,0.000068659334,0.00046617197,0.00022914601,0.00072617957,0.00021648419],"domain_scores_gemma":[0.9988812,0.00011923468,0.00045595792,0.0001888822,0.00020225429,0.00015245551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017157847,0.0001795,0.0002094381,0.00011504973,0.00010763862,0.00020771477,0.00023336647,0.000077911856,0.00014935473],"category_scores_gemma":[0.00007179247,0.00014464755,0.00008150867,0.00013909202,0.00020215362,0.00055091706,0.00008190253,0.00025980215,0.00015080237],"study_design_candidate":"design_other","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":[0.0005839648,0.00013268068,0.017087352,0.000009663291,0.00046804224,0.0005790648,0.0010976584,0.014203965,0.19377632,0.000027328908,0.004166576,0.7678674],"study_design_scores_gemma":[0.0027047787,0.00038941062,0.37876326,0.00124227,0.00020909941,0.0016059139,0.0004098381,0.50310326,0.065010555,0.015753696,0.029793764,0.0010141582],"about_ca_topic_score_codex":0.010881732,"about_ca_topic_score_gemma":0.00044347587,"teacher_disagreement_score":0.7668532,"about_ca_system_score_codex":0.00031866034,"about_ca_system_score_gemma":0.000053909593,"threshold_uncertainty_score":0.9957049},"labels":[],"label_agreement":null},{"id":"W2152334070","doi":"10.1080/01431160903571791","title":"Comparison of pixel- and object-based classification in land cover change mapping","year":2011,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":230,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Carleton University","funders":"Vlaamse regering","keywords":"Thematic Mapper; Thematic map; Land cover; Change detection; Cartography; Remote sensing; Pixel; Object (grammar); Land use; Geography; Computer science; Data mining; Artificial intelligence; Satellite imagery; Ecology","score_opus":0.0803817342266771,"score_gpt":0.29139542189550666,"score_spread":0.21101368766882955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152334070","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99555,0.00005246435,0.0026966834,0.00015746066,0.00020736076,0.00003563057,9.277628e-7,0.0000022321046,0.0012972566],"genre_scores_gemma":[0.9935783,0.000019929317,0.006258378,0.00007701937,0.00005920678,1.28871065e-8,0.00000102092,0.0000036485906,0.0000024612966],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99933076,0.0000288998,0.0002884931,0.00006565397,0.00022193948,0.00006423475],"domain_scores_gemma":[0.99954647,0.000028332292,0.0003170955,0.000044658602,0.000034858484,0.00002858485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022519018,0.000048803882,0.000121506375,0.00009206652,0.000014222351,0.000012392443,0.00009192078,0.000030199437,0.00005433707],"category_scores_gemma":[0.000014083425,0.0000396468,0.000026150454,0.00006017832,0.000013431895,0.00017712558,0.00002837915,0.00006748827,0.000008186224],"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":[0.0001187396,0.000051904142,0.8791658,0.000019602667,0.000028444687,0.000029789042,0.0037311416,0.00027001294,0.012673932,0.0000035891092,0.000021393284,0.103885636],"study_design_scores_gemma":[0.0006325651,0.00005636507,0.7618076,0.00033585806,0.0000094608695,0.000062980784,0.0002852704,0.23094627,0.004631076,0.00016804853,0.0009851491,0.00007932717],"about_ca_topic_score_codex":0.00074067764,"about_ca_topic_score_gemma":0.0005652542,"teacher_disagreement_score":0.23067625,"about_ca_system_score_codex":0.00005891596,"about_ca_system_score_gemma":0.000006330688,"threshold_uncertainty_score":0.16167496},"labels":[],"label_agreement":null},{"id":"W2154464101","doi":"10.1080/01431160802275890","title":"The suitability of decadal image data sets for mapping tropical forest cover change in the Democratic Republic of Congo: implications for the global land survey","year":2008,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":49,"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":"Department of Family and Community Medicine, University of Toronto; National Aeronautics and Space Administration","keywords":"Compositing; Thematic Mapper; Land cover; Remote sensing; Cloud cover; Pixel; Thematic map; Shadow (psychology); Tropics; Environmental science; Change detection; Geography; Physical geography; Satellite imagery; Cartography; Computer science; Land use; Cloud computing; Image (mathematics); Computer vision","score_opus":0.0883812866087391,"score_gpt":0.3409535497770719,"score_spread":0.25257226316833276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154464101","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9393032,0.00008941544,0.050355952,0.009140597,0.00032949194,0.00050969823,0.00016905145,0.0000030029273,0.00009957612],"genre_scores_gemma":[0.9812172,0.00006784995,0.018363686,0.00017114544,0.00013854918,1.4627514e-7,0.000027282567,0.000006237344,0.000007905713],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983671,0.00020791961,0.00060758076,0.00016592104,0.0004731342,0.00017840252],"domain_scores_gemma":[0.99620414,0.0024056016,0.0006018592,0.00041016107,0.00034383993,0.000034373577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017493047,0.00010394976,0.00018559479,0.000024663523,0.00018218643,0.00005205882,0.000989272,0.000052876738,0.0000022829338],"category_scores_gemma":[0.0024419362,0.000051376872,0.000116212446,0.00020471006,0.00035821888,0.00024947006,0.0001797277,0.00015389871,5.339335e-7],"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":[0.0004682633,0.00013039281,0.8740056,0.000021299726,0.00024479415,0.000014908864,0.0013067037,0.0009557884,0.0030142197,0.00013539115,0.006734794,0.11296781],"study_design_scores_gemma":[0.0004526134,0.000044199343,0.9564908,0.000037438385,0.000018792874,0.0005678909,0.000084510255,0.038784686,0.000063485546,0.001737549,0.0016630932,0.000054904496],"about_ca_topic_score_codex":0.0011835163,"about_ca_topic_score_gemma":0.005978834,"teacher_disagreement_score":0.11291291,"about_ca_system_score_codex":0.0001809078,"about_ca_system_score_gemma":0.000054499957,"threshold_uncertainty_score":0.33363295},"labels":[],"label_agreement":null},{"id":"W2155284257","doi":"10.1080/01431160310001642304","title":"Relationship between airborne multispectral image texture and aspen defoliation","year":2004,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","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":"University of Saskatchewan","funders":"Canadian Forest Service; Natural Sciences and Engineering Research Council of Canada","keywords":"Multispectral image; Normalized Difference Vegetation Index; Remote sensing; Vegetation (pathology); Multispectral pattern recognition; Thematic Mapper; Foothills; Environmental science; Image resolution; Leaf area index; Satellite imagery; Geography; Forestry; Cartography; Agronomy; Computer science; Artificial intelligence; Biology","score_opus":0.012867869626387482,"score_gpt":0.2614617774355038,"score_spread":0.24859390780911633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155284257","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93775904,0.000041360294,0.055980455,0.003666486,0.00045315683,0.00007638952,0.000002359212,0.000020396934,0.0020003822],"genre_scores_gemma":[0.8167385,0.000010655549,0.18257752,0.000120639525,0.00047686018,2.0292743e-9,0.0000048314473,0.000011859892,0.000059128448],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99857885,0.000051857078,0.00039562877,0.00017104192,0.0006412783,0.00016136369],"domain_scores_gemma":[0.99915534,0.00011750988,0.00040048824,0.00009529142,0.00011939638,0.00011194913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030502453,0.00013826137,0.0001610783,0.000095612035,0.00009161034,0.00011602107,0.00018534034,0.000103680904,0.000019762869],"category_scores_gemma":[0.00042208913,0.000112730806,0.00008880087,0.00014301193,0.00011462121,0.00048010002,0.000080998965,0.00038688804,0.00005373311],"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":[0.00015316052,0.000065097585,0.09070829,0.000012555677,0.0002122407,0.001053443,0.0035507772,0.010782051,0.075365655,0.00033643417,0.0012124869,0.8165478],"study_design_scores_gemma":[0.0010512228,0.000061789804,0.97593725,0.00014512162,0.00004747967,0.0021150988,0.00010984903,0.0018422911,0.0053221006,0.01196959,0.001199933,0.00019827097],"about_ca_topic_score_codex":0.00025906667,"about_ca_topic_score_gemma":0.00011766012,"teacher_disagreement_score":0.88522893,"about_ca_system_score_codex":0.000491916,"about_ca_system_score_gemma":0.000022428792,"threshold_uncertainty_score":0.45970288},"labels":[],"label_agreement":null},{"id":"W2155508917","doi":"10.1080/01431160903401361","title":"Forest structure without ground data: Adaptive Full-Blind Multiple Forward-Mode reflectance model inversion in a mountain pine beetle damaged forest","year":2010,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":8,"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 Lethbridge","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Endmember; Remote sensing; Canopy; Reflectivity; Computer science; Mode (computer interface); Environmental science; Pixel; Geography; Artificial intelligence","score_opus":0.02365906102576664,"score_gpt":0.29940167140124296,"score_spread":0.2757426103754763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155508917","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87702173,0.000009057042,0.120981604,0.0007120728,0.00040905928,0.00014736173,0.00002798476,0.000013892464,0.00067726016],"genre_scores_gemma":[0.7877485,0.000012617259,0.21170767,0.00013811237,0.00026041965,1.9195623e-8,0.00004078298,0.00002344534,0.00006843354],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99808186,0.00005638827,0.0005115843,0.00035604867,0.00073826726,0.0002558283],"domain_scores_gemma":[0.998691,0.00010243944,0.0004750955,0.00044351583,0.00015398637,0.0001339343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042078196,0.00019833467,0.00024206267,0.00019658766,0.00012617194,0.00010399393,0.0006943114,0.0001337589,0.000016226886],"category_scores_gemma":[0.00028994965,0.00018133162,0.00007489052,0.00022498505,0.00017718045,0.0006867251,0.00029487998,0.00080176553,0.000013438339],"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":[0.0012853025,0.000087919325,0.008688189,0.000005889573,0.0000967462,0.00014330389,0.0013505006,0.24786152,0.64325285,0.00009249964,0.0007558574,0.096379444],"study_design_scores_gemma":[0.0013068345,0.000048529717,0.005015254,0.00008437076,0.000021465728,0.0004790808,0.00017982216,0.98452854,0.0013864537,0.0057391874,0.0010239058,0.00018653549],"about_ca_topic_score_codex":0.0013558673,"about_ca_topic_score_gemma":0.04284008,"teacher_disagreement_score":0.73666704,"about_ca_system_score_codex":0.00032973423,"about_ca_system_score_gemma":0.00007815897,"threshold_uncertainty_score":0.9746256},"labels":[],"label_agreement":null},{"id":"W2157735115","doi":"10.1080/01431160600784291","title":"Sensitivity of Landsat/IKONOS accuracy comparison to errors in photointerpreted reference data and variations in test point sets","year":2006,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","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":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reference data; Homogeneity (statistics); Remote sensing; Land cover; Computer science; Statistics; Cartography; Geography; Mathematics; Data mining; Land use","score_opus":0.021306196340423435,"score_gpt":0.29538314198976157,"score_spread":0.27407694564933816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157735115","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9867254,0.000013721767,0.009868083,0.001779372,0.00022028452,0.000120056786,0.000028089693,0.000006927635,0.0012380636],"genre_scores_gemma":[0.93949175,0.00000866175,0.060284052,0.00011221434,0.00006368703,5.4150338e-9,0.000019768138,0.000008196252,0.000011666637],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9982054,0.00015167547,0.0007311161,0.0002464608,0.0005005063,0.0001648126],"domain_scores_gemma":[0.9985303,0.0005751207,0.00050362066,0.0002198975,0.000107506705,0.0000635403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086992857,0.00013566385,0.00028320614,0.00021288612,0.000025686051,0.000049740232,0.000278625,0.0000719286,0.000012574257],"category_scores_gemma":[0.0013064375,0.00011747983,0.000032781136,0.0002755921,0.00006876325,0.0003688897,0.0003290041,0.00032312557,0.000006825451],"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":[0.00040737068,0.00034610642,0.2176225,0.000015053031,0.000058031877,0.0010407472,0.0030028347,0.037969682,0.5080352,0.00001444076,0.002901086,0.22858693],"study_design_scores_gemma":[0.00066973903,0.000065488886,0.5506898,0.0005099164,0.00001242975,0.0009212693,0.00023765484,0.4394948,0.0061594816,0.0004240995,0.00064571673,0.00016960238],"about_ca_topic_score_codex":0.0077343746,"about_ca_topic_score_gemma":0.014824081,"teacher_disagreement_score":0.50187576,"about_ca_system_score_codex":0.00029205927,"about_ca_system_score_gemma":0.000028458764,"threshold_uncertainty_score":0.99887323},"labels":[],"label_agreement":null},{"id":"W2158065679","doi":"10.1080/01431160701281064","title":"Evaluation of segment‐based gap‐filled Landsat ETM+ SLC‐off satellite data for land cover classification in southern Saskatchewan, Canada","year":2008,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Statistics Canada; Agricultural Institute of Canada","funders":"U.S. Geological Survey","keywords":"Thematic Mapper; Land cover; Remote sensing; Contextual image classification; Thematic map; Satellite; Decision tree; Satellite imagery; Geography; Cartography; Land use; Computer science; Data mining; Image (mathematics); Artificial intelligence","score_opus":0.08175275359171674,"score_gpt":0.29446747636650583,"score_spread":0.2127147227747891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158065679","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6551621,0.00036791954,0.3351097,0.0065793637,0.0010089964,0.00024072667,0.000051924682,0.000011960036,0.0014673047],"genre_scores_gemma":[0.9682898,0.000017982327,0.031208893,0.00015390222,0.00013879225,5.474807e-8,0.000050553313,0.0000030101583,0.00013701327],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981155,0.00011457735,0.0004747531,0.00018366854,0.000989538,0.00012199177],"domain_scores_gemma":[0.99725825,0.00019272286,0.00056232675,0.00028608943,0.0016582963,0.000042321477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016299896,0.00009001795,0.00016602874,0.00010671261,0.000038222744,0.000029608631,0.0005892537,0.000048771613,0.00000802848],"category_scores_gemma":[0.0005310722,0.000081713966,0.000046600533,0.00011076658,0.000024555413,0.00018870225,0.000075155695,0.000117328535,8.0341675e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003491983,0.000104341205,0.006691538,0.00005139849,0.00030740257,0.00020805381,0.003217337,0.07221698,0.034382027,0.000021317166,0.0019669584,0.88048345],"study_design_scores_gemma":[0.001622056,0.000021431777,0.0022548116,0.00015577376,0.00002420186,0.00020112003,0.00034338204,0.98395115,0.0042071077,0.0005311873,0.0065875067,0.00010027571],"about_ca_topic_score_codex":0.009069274,"about_ca_topic_score_gemma":0.05562302,"teacher_disagreement_score":0.91173416,"about_ca_system_score_codex":0.00021650929,"about_ca_system_score_gemma":0.001224318,"threshold_uncertainty_score":0.99752945},"labels":[],"label_agreement":null},{"id":"W2158848000","doi":"10.1080/01431160512331314029","title":"A practical approach for estimating the red edge position of plant leaf reflectance","year":2005,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":98,"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; University of Waterloo","funders":"","keywords":"Red edge; Herbaceous plant; Remote sensing; Reflectivity; Inversion (geology); Vegetation (pathology); Environmental science; Chlorophyll; Wavelength; Spectral line; Position (finance); Mathematics; Botany; Hyperspectral imaging; Geology; Optics; Biology; Physics","score_opus":0.023719086683555598,"score_gpt":0.2995959007938599,"score_spread":0.2758768141103043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158848000","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12449658,0.000023099945,0.8591127,0.011190015,0.00065084983,0.00018271054,0.0000050197596,0.000012034562,0.0043269983],"genre_scores_gemma":[0.37300107,0.0000047062886,0.6261628,0.00020583854,0.00056889286,9.805724e-9,0.0000043383384,0.000007444453,0.000044878023],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985609,0.0000715134,0.00046193603,0.0001362529,0.00062575634,0.00014362762],"domain_scores_gemma":[0.99877477,0.00020190999,0.0006913512,0.00011046393,0.00017516916,0.00004634118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000586191,0.00010474165,0.00015598134,0.00004360433,0.000082197934,0.000049254053,0.00021871678,0.000061809966,0.000008990776],"category_scores_gemma":[0.00048502788,0.00006744525,0.00012549784,0.00009883776,0.0000984986,0.00025512307,0.000057542937,0.00025419594,0.0000038097683],"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":[0.00061673555,0.0001121708,0.000040356146,0.000014751738,0.00018218083,0.00007065107,0.001453644,0.09995594,0.36009473,0.00008783488,0.020780448,0.51659054],"study_design_scores_gemma":[0.00051647954,0.00008053353,0.00039504335,0.00015976687,0.000041173582,0.0053189346,0.0001245318,0.9680399,0.018633354,0.00057593663,0.00600489,0.00010946033],"about_ca_topic_score_codex":0.000022886003,"about_ca_topic_score_gemma":0.000017374143,"teacher_disagreement_score":0.86808395,"about_ca_system_score_codex":0.00028601402,"about_ca_system_score_gemma":0.000025138383,"threshold_uncertainty_score":0.27503377},"labels":[],"label_agreement":null},{"id":"W2160152622","doi":"10.1080/01431160050021312","title":"Comparison of three different methods to select feature for discriminating forest cover types using SAR imagery","year":2000,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":20,"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":"Pattern recognition (psychology); Principal component analysis; Artificial intelligence; Computer science; Feature selection; Land cover; Partition (number theory); Artificial neural network; Feature (linguistics); Data mining; Mathematics; Land use","score_opus":0.0487945942552737,"score_gpt":0.3579496431276622,"score_spread":0.3091550488723885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160152622","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7876973,0.00021934786,0.21023689,0.00042127408,0.0007466613,0.00007445343,0.0000128220145,0.000006660887,0.00058458955],"genre_scores_gemma":[0.63528514,0.000012466648,0.364166,0.00008803435,0.00036248335,5.326418e-10,0.000009046432,0.000005667838,0.00007114788],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875844,0.00009095116,0.0004433251,0.00013657474,0.00037829735,0.0001924107],"domain_scores_gemma":[0.998697,0.00039704508,0.00033342224,0.00008872518,0.0003867905,0.00009703561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042841784,0.00013904383,0.00034962615,0.00018487676,0.00008271253,0.00008825692,0.00018230703,0.000061935585,0.00006901249],"category_scores_gemma":[0.00023204084,0.00010066192,0.00017639245,0.00010459725,0.000033233777,0.00013890998,0.000009131925,0.00019389462,0.0000036390106],"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":[0.0003236494,0.000011007562,0.00881222,0.000011726343,0.000103898084,0.000017805503,0.00035266206,0.050488926,0.0031169916,0.000001385168,0.00021385653,0.93654585],"study_design_scores_gemma":[0.00044212662,0.00020940861,0.031022204,0.00039054445,0.00008494199,0.00036624054,0.00009353181,0.95398355,0.008444071,0.00081402936,0.0039852806,0.00016406247],"about_ca_topic_score_codex":0.00046332163,"about_ca_topic_score_gemma":0.00050162035,"teacher_disagreement_score":0.9363818,"about_ca_system_score_codex":0.000022471131,"about_ca_system_score_gemma":0.000048076392,"threshold_uncertainty_score":0.41048738},"labels":[],"label_agreement":null},{"id":"W2164146430","doi":"10.1080/0143116031000139845","title":"Detection of lines, line junctions and line terminations","year":2004,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Image and Object Detection Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Detector; Line (geometry); Computer science; Edge detection; Canny edge detector; Filter (signal processing); Curvature; Algorithm; Infinite impulse response; Artificial intelligence; Computer vision; Measure (data warehouse); Topology (electrical circuits); Mathematics; Image (mathematics); Image processing; Digital filter; Geometry; Telecommunications; Data mining","score_opus":0.011721511963353207,"score_gpt":0.27376919036395436,"score_spread":0.26204767840060117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164146430","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052951247,0.00009831101,0.9440311,0.0014397829,0.001250243,0.000035797395,8.877985e-7,0.00003965922,0.00015297809],"genre_scores_gemma":[0.7801442,0.00011339956,0.219283,0.00011378302,0.00029118508,1.5752578e-8,3.9025258e-7,0.0000049279106,0.000049082286],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990881,0.000024372575,0.00040063373,0.00010037892,0.0003111597,0.0000753584],"domain_scores_gemma":[0.99837416,0.00003612519,0.0003738075,0.00011004177,0.0010627159,0.000043174245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025531935,0.00007655616,0.00012362168,0.00048293167,0.000060188693,0.00007456326,0.00020866698,0.0000476395,0.0000010685518],"category_scores_gemma":[0.00019414732,0.000070578266,0.00008177527,0.0002128908,0.000041685787,0.00049287383,0.000065773136,0.00017469251,0.000001142639],"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":[0.000013741558,0.000020410402,0.000002136227,0.0000034301893,0.000032758584,0.00004878512,0.00017883208,0.00016848285,0.10107727,0.00010954238,0.000010081176,0.8983345],"study_design_scores_gemma":[0.0005560585,0.00025718714,0.00029702447,0.00016501675,0.000016716267,0.0030671572,0.000039856623,0.022162618,0.9591337,0.012224571,0.0019892454,0.00009085896],"about_ca_topic_score_codex":0.00009331651,"about_ca_topic_score_gemma":0.000040931194,"teacher_disagreement_score":0.89824367,"about_ca_system_score_codex":0.00009312356,"about_ca_system_score_gemma":0.000072448165,"threshold_uncertainty_score":0.28780982},"labels":[],"label_agreement":null},{"id":"W2166232596","doi":"10.1080/01431161.2013.768362","title":"Comparison of atmospheric CO<sub>2</sub> observed by GOSAT and two ground stations in China","year":2013,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Atmospheric and Environmental Gas Dynamics","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":"University of New Brunswick","funders":"","keywords":"SCIAMACHY; Environmental science; Atmospheric sciences; Satellite; Northern Hemisphere; Southern Hemisphere; Atmospheric Infrared Sounder; Greenhouse gas; Climatology; Troposphere; Geology; Physics","score_opus":0.011796827614114665,"score_gpt":0.25786199259990694,"score_spread":0.24606516498579228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166232596","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9602504,0.00007877101,0.038816575,0.00029517073,0.0001600843,0.00007335144,0.0000015814136,0.0000039845063,0.0003200937],"genre_scores_gemma":[0.9256995,0.00011650383,0.074029654,0.00007933892,0.000026810209,3.61056e-8,0.000003571314,0.000011800975,0.00003278739],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99876356,0.000048003767,0.0004891663,0.00012444308,0.00043437094,0.00014046296],"domain_scores_gemma":[0.9993632,0.00005399095,0.0004089616,0.000071938026,0.000021175045,0.00008076293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015005708,0.000114288225,0.00020492429,0.000007914034,0.000036596117,0.00003803801,0.00015326064,0.000040018855,0.00007121645],"category_scores_gemma":[0.000028467006,0.00010755373,0.00005259621,0.00009234948,0.00017672197,0.00036352212,0.000081612045,0.00018051954,0.000012563767],"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":[0.000044999037,0.00011100819,0.14751229,0.0000045472516,0.000058864534,0.000025656404,0.0011690282,0.06274438,0.2578836,0.0000065624117,0.000439336,0.52999973],"study_design_scores_gemma":[0.0009878189,0.00011777879,0.55065835,0.00007882553,0.000017223672,0.00015137314,0.00077656563,0.43972388,0.0059613,0.001021639,0.00033050016,0.00017474976],"about_ca_topic_score_codex":0.0012975447,"about_ca_topic_score_gemma":0.00011549457,"teacher_disagreement_score":0.529825,"about_ca_system_score_codex":0.00028433578,"about_ca_system_score_gemma":0.0000083223385,"threshold_uncertainty_score":0.43859136},"labels":[],"label_agreement":null},{"id":"W2166808775","doi":"10.1080/01431160110070753","title":"Providing crop information using RADARSAT-1 and satellite optical imagery","year":2002,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":107,"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":"Remote sensing; Environmental science; Backscatter (email); Satellite imagery; Crop; Satellite; Radar; Geography; Computer science; Forestry","score_opus":0.013400359001647794,"score_gpt":0.2284540665426503,"score_spread":0.2150537075410025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166808775","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.948387,0.00013445049,0.042095743,0.0015566805,0.00094039063,0.00007822443,9.181919e-7,0.000019458319,0.006787131],"genre_scores_gemma":[0.68590736,0.00015781862,0.31322706,0.00034188546,0.0002932313,6.6132655e-10,0.0000010972625,0.000009612996,0.00006192097],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984764,0.000043785963,0.00048167878,0.000112830014,0.00070800923,0.00017728636],"domain_scores_gemma":[0.9992025,0.00006365413,0.00040279565,0.00008341372,0.00013980157,0.00010784216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030126655,0.0001329531,0.00015644947,0.0001156223,0.00008393964,0.00023320386,0.00015284681,0.000073381765,0.000051922918],"category_scores_gemma":[0.00023927331,0.00010714977,0.000079931415,0.00012615397,0.00012905539,0.0011794663,0.00011893544,0.00026740058,0.000046148758],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002598313,0.000009343107,0.00029051607,0.0000040755112,0.000037401776,0.00017616602,0.0006965566,0.0015635416,0.0563639,0.00000685911,0.00037851758,0.94044715],"study_design_scores_gemma":[0.001650295,0.0001337854,0.011903609,0.0006948681,0.00010950351,0.028917272,0.0006056008,0.8263232,0.03601704,0.0009261873,0.09202326,0.0006953678],"about_ca_topic_score_codex":0.000078701334,"about_ca_topic_score_gemma":0.000005534912,"teacher_disagreement_score":0.93975174,"about_ca_system_score_codex":0.00031538305,"about_ca_system_score_gemma":0.00000848164,"threshold_uncertainty_score":0.4369441},"labels":[],"label_agreement":null},{"id":"W2167029631","doi":"10.1080/0143116031000115247","title":"Relationships between Radarsat SAR data and surface moisture content of agricultural organic soils","year":2003,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Soil Moisture and Remote Sensing","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":true,"ca_institutions":"Agriculture and Agri-Food Canada; Centre de Géomatique du Québec; Université Laval","funders":"","keywords":"Soil water; Water content; Environmental science; Vegetation (pathology); Soil science; Moisture; Backscatter (email); Hydrology (agriculture); Geology; Geography; Meteorology","score_opus":0.048696912119741406,"score_gpt":0.2572778046941605,"score_spread":0.20858089257441909,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167029631","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98926365,0.00031323245,0.0063260105,0.0009809767,0.00052311335,0.000057263598,0.0000045060124,0.0000070899796,0.0025241438],"genre_scores_gemma":[0.941884,0.00008805675,0.05767994,0.000055059787,0.00015951827,9.655268e-11,0.000009431507,0.0000110880255,0.00011292039],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99837786,0.00017095961,0.0005073487,0.00018904856,0.0006130125,0.00014179984],"domain_scores_gemma":[0.99882925,0.00019940073,0.0005131931,0.00020038006,0.00015206684,0.00010572919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007212536,0.00013383794,0.00023954794,0.000049817598,0.00008493529,0.00004635568,0.00028774244,0.00009401944,0.00001371188],"category_scores_gemma":[0.0005667535,0.000098994075,0.00006514847,0.00013200927,0.00013530669,0.00032238135,0.0001552188,0.0003780264,0.000007415008],"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":[0.000093716575,0.00006704099,0.1709477,0.000020175139,0.0008021681,0.0002459689,0.0035556136,0.0021835612,0.38973802,0.00006394807,0.0026174202,0.42966464],"study_design_scores_gemma":[0.001427526,0.00008928465,0.9325407,0.0003831338,0.00021373929,0.003284712,0.0026173603,0.0018031663,0.046075497,0.0011795314,0.01001135,0.0003739571],"about_ca_topic_score_codex":0.0002706555,"about_ca_topic_score_gemma":0.00016928471,"teacher_disagreement_score":0.76159304,"about_ca_system_score_codex":0.00013524471,"about_ca_system_score_gemma":0.000028262195,"threshold_uncertainty_score":0.4036861},"labels":[],"label_agreement":null},{"id":"W2167753478","doi":"10.1080/01431161.2013.788261","title":"Integration of orthoimagery and lidar data for object-based urban thematic mapping using random forests","year":2013,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":176,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"","keywords":"Computer science; Multispectral image; Artificial intelligence; Random forest; Lidar; Orthophoto; Thematic Mapper; Segmentation; Aerial image; Pattern recognition (psychology); Thematic map; Image segmentation; Feature (linguistics); Feature selection; Classifier (UML); Pixel; Remote sensing; Satellite imagery; Image (mathematics); Geography; Cartography","score_opus":0.03133330515750703,"score_gpt":0.272343004543332,"score_spread":0.24100969938582495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167753478","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5935456,0.00003653086,0.40547517,0.00033339445,0.000293929,0.00017121018,0.0000038793532,0.0000056278036,0.00013465418],"genre_scores_gemma":[0.6469221,0.00000602773,0.35279647,0.00007918645,0.00015350812,1.0123532e-8,0.000012307918,0.000010760511,0.000019606856],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984786,0.00007277834,0.00058801414,0.0001855461,0.00053121324,0.0001438653],"domain_scores_gemma":[0.9983652,0.0003215805,0.00075845944,0.00021961183,0.00026990168,0.00006523046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064228266,0.00013856895,0.00026039197,0.000128176,0.00006128508,0.000109981724,0.00031940956,0.00007045167,0.000016667746],"category_scores_gemma":[0.00077874295,0.00010122502,0.000095753356,0.00011832458,0.00011705621,0.0005642539,0.00013208864,0.00016853714,0.000003908785],"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":[0.0001370215,0.00003349598,0.0016877566,0.000036962472,0.0001310408,0.00003061996,0.0007429143,0.0037411929,0.6422661,0.0000035975957,0.0015059612,0.34968337],"study_design_scores_gemma":[0.0014741044,0.000052806685,0.011485611,0.0009348886,0.00006138635,0.0006599606,0.00038143707,0.9683613,0.014727368,0.0011830751,0.00050318794,0.00017485837],"about_ca_topic_score_codex":0.0002120988,"about_ca_topic_score_gemma":0.000089118344,"teacher_disagreement_score":0.9646201,"about_ca_system_score_codex":0.00013475202,"about_ca_system_score_gemma":0.000032550182,"threshold_uncertainty_score":0.41278368},"labels":[],"label_agreement":null},{"id":"W2169745972","doi":"10.1080/01431160500219364","title":"Use of GIS and remotely sensed data for<i>a priori</i>identification of reference areas for Great Lakes coastal ecosystems","year":2005,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"U.S. Army Corps of Engineers; Minnesota Department of Natural Resources; U.S. Department of Agriculture; National Oceanic and Atmospheric Administration; U.S. Environmental Protection Agency","keywords":"Ecoregion; Wetland; Shore; Environmental science; Habitat; Disturbance (geology); Watershed; Ecosystem; Geography; Identification (biology); Ecology; Physical geography; Environmental resource management; Geology; Oceanography; Computer science","score_opus":0.0575128800949301,"score_gpt":0.28800645762786925,"score_spread":0.23049357753293914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169745972","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93223375,0.000025217454,0.06471069,0.0019916114,0.00040036236,0.00023107565,0.00015711157,0.000005255391,0.00024493536],"genre_scores_gemma":[0.94629115,0.000120972036,0.05306075,0.0001040697,0.00010227848,4.1344965e-8,0.00003097136,0.000007009928,0.0002827732],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989915,0.000028845772,0.0005018485,0.00014640801,0.00024288161,0.00008850412],"domain_scores_gemma":[0.9986852,0.00023432444,0.0007140929,0.00015314022,0.00018257524,0.00003065714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045083283,0.000074641146,0.00017904982,0.00007302429,0.000051451676,0.000022907458,0.00020334873,0.0000381252,0.000007515769],"category_scores_gemma":[0.0005727701,0.000068246656,0.000041841417,0.0000383443,0.00010372871,0.00041985838,0.00016949882,0.00005213004,9.317447e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018643928,0.00017887009,0.022745969,0.00022229757,0.0011739715,0.00003649041,0.0013775234,0.0034734618,0.17188866,0.00027794437,0.05399013,0.7427703],"study_design_scores_gemma":[0.004346245,0.0005708025,0.16549577,0.00078920106,0.0005215796,0.00071918254,0.00075906486,0.5148119,0.04142824,0.00270134,0.2673055,0.00055121264],"about_ca_topic_score_codex":0.00010992098,"about_ca_topic_score_gemma":0.0033745058,"teacher_disagreement_score":0.7422191,"about_ca_system_score_codex":0.00004818861,"about_ca_system_score_gemma":0.000013747606,"threshold_uncertainty_score":0.27830178},"labels":[],"label_agreement":null},{"id":"W2172113778","doi":"10.1080/01431160110113917","title":"A multivariate approach to vegetation mapping of Manitoba's Hudson Bay Lowlands","year":2002,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Rangeland and Wildlife Management","field":"Environmental Science","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Churchill Northern Studies Centre; National Park Service; Natural Sciences and Engineering Research Council of Canada; Fisheries and Oceans Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Parks Canada","keywords":"Vegetation (pathology); Vegetation classification; Bay; Remote sensing; Geography; Physical geography; Principal component analysis; Multivariate statistics; Cartography; Environmental science; Computer science","score_opus":0.02332417593305457,"score_gpt":0.23362452711531778,"score_spread":0.2103003511822632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2172113778","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7169321,0.000034120923,0.25668797,0.0008392237,0.0005720943,0.00008001141,8.113391e-7,0.000007394198,0.024846232],"genre_scores_gemma":[0.9023158,0.000017166294,0.09710841,0.0002381688,0.00019157688,1.5430308e-8,8.485756e-7,0.0000069909984,0.00012099899],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989089,0.00003366158,0.0003267332,0.0001061944,0.00051876536,0.00010577827],"domain_scores_gemma":[0.9995308,0.000024550085,0.00025763884,0.00007174966,0.000055905977,0.00005933357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026179894,0.000078312405,0.00012393428,0.00013737961,0.000029312496,0.00003346721,0.00018620602,0.000025702248,0.000033849326],"category_scores_gemma":[0.0000506046,0.00006634489,0.000077207114,0.000118214455,0.00002279903,0.00014713677,0.00007418933,0.00008894326,0.00004081239],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006276668,0.00012815827,0.008640622,0.000019065956,0.00016473467,0.000102427526,0.0031618776,0.028200455,0.015611576,0.00003479827,0.0019161208,0.9419574],"study_design_scores_gemma":[0.0018434201,0.0001473233,0.13508402,0.00056741806,0.000047854293,0.00042565528,0.00084711425,0.8313731,0.0022658268,0.00064373063,0.026437758,0.0003167559],"about_ca_topic_score_codex":0.00013777337,"about_ca_topic_score_gemma":0.00001915543,"teacher_disagreement_score":0.9416406,"about_ca_system_score_codex":0.00011616597,"about_ca_system_score_gemma":0.0000021918934,"threshold_uncertainty_score":0.2705466},"labels":[],"label_agreement":null},{"id":"W2215770970","doi":"10.1080/2150704x.2015.1126375","title":"Forest recovery trends derived from Landsat time series for North American boreal forests","year":2015,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":186,"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; U.S. Geological Survey","keywords":"Normalized Difference Vegetation Index; Taiga; Pixel; Disturbance (geology); Vegetation (pathology); Environmental science; Boreal; Physical geography; Remote sensing; Spectral bands; Forestry; Geography; Leaf area index; Ecology; Geology; Biology; Physics","score_opus":0.01119393416384829,"score_gpt":0.2381872794125363,"score_spread":0.22699334524868803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2215770970","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98349935,0.000015547073,0.011564055,0.0019620904,0.0009608692,0.0000596483,0.000034461395,0.000023809167,0.0018801932],"genre_scores_gemma":[0.82318604,0.000015171187,0.17500094,0.00026819357,0.0009811453,1.3592363e-8,0.000098278186,0.000025063544,0.0004251791],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9983698,0.000056619745,0.000416058,0.00020446505,0.00072674884,0.00022629218],"domain_scores_gemma":[0.9987059,0.00011926695,0.0005990438,0.00013588923,0.0002441312,0.00019573595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021642155,0.0001808823,0.00027616505,0.00010630988,0.00006544551,0.00014945176,0.00030952485,0.000057705616,0.000016509972],"category_scores_gemma":[0.00029030046,0.00014032667,0.00017985288,0.0001904711,0.00018864534,0.00042342514,0.00010299255,0.00019853398,0.000037306432],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009101098,0.000029563582,0.026303,0.0000012690548,0.0002185584,0.00026409328,0.00085488113,0.010692092,0.004568675,0.0000011412111,0.020698007,0.9354586],"study_design_scores_gemma":[0.0020868513,0.00083396514,0.8743367,0.00015847354,0.00012535647,0.0021601028,0.00034166384,0.057720292,0.0052403985,0.002936628,0.05344535,0.0006142003],"about_ca_topic_score_codex":0.0014014815,"about_ca_topic_score_gemma":0.009200341,"teacher_disagreement_score":0.93484443,"about_ca_system_score_codex":0.0004205808,"about_ca_system_score_gemma":0.000043600652,"threshold_uncertainty_score":0.5722355},"labels":[],"label_agreement":null},{"id":"W2283858015","doi":"10.1080/01431161.2015.1129561","title":"Mapping urban land cover based on spatial-spectral classification of hyperspectral remote-sensing data","year":2016,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":18,"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 Ottawa","funders":"Purdue University","keywords":"Hyperspectral imaging; Support vector machine; Land cover; Cohen's kappa; Computer science; Remote sensing; Pattern recognition (psychology); Artificial intelligence; Pixel; Spatial analysis; Data mining; Land use; Geography; Machine learning","score_opus":0.035632988090821945,"score_gpt":0.2653119973228587,"score_spread":0.22967900923203677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2283858015","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17677785,0.00006350571,0.8168243,0.0024171036,0.0019721936,0.00010336995,0.000025978743,0.000082231236,0.0017334514],"genre_scores_gemma":[0.8459147,0.00007071982,0.15276514,0.00009049425,0.0010244768,2.284793e-9,0.0000238656,0.00006153998,0.000049032635],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973864,0.000116579504,0.0009400799,0.0003203318,0.00094237406,0.00029426202],"domain_scores_gemma":[0.99738574,0.00040002153,0.0006531591,0.00068867253,0.00075442076,0.00011796389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007216404,0.0002669444,0.00037789252,0.0006533271,0.000050845174,0.00009716332,0.0005420136,0.00013858684,0.00001589859],"category_scores_gemma":[0.0007827487,0.00022253838,0.000161833,0.00021718336,0.00012143537,0.00050947233,0.000052753636,0.00033756107,0.000026083108],"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":[0.0001374215,0.000014633825,0.00008505182,0.000018701601,0.00013952472,0.00010521037,0.00012840833,0.0038869795,0.4115805,0.000013511646,0.00078423147,0.58310586],"study_design_scores_gemma":[0.001165447,0.000060585873,0.004515548,0.0011607851,0.00003790735,0.00035382362,0.00005356683,0.9469364,0.041445654,0.00026463083,0.0037516318,0.00025396142],"about_ca_topic_score_codex":0.000075581556,"about_ca_topic_score_gemma":0.000020205089,"teacher_disagreement_score":0.9430495,"about_ca_system_score_codex":0.0005705952,"about_ca_system_score_gemma":0.0001263331,"threshold_uncertainty_score":0.9074852},"labels":[],"label_agreement":null},{"id":"W2290948620","doi":"10.1080/01431161.2016.1151574","title":"Modelling and mapping permafrost at high spatial resolution using Landsat and Radarsat-2 images in Northern Ontario, Canada: Part 2 – regional mapping","year":2016,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Climate change and permafrost","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":"Ministry of Natural Resources and Forestry; Canadian Forest Service; University of New Brunswick","funders":"Canadian Forest Service; Canadian Space Agency; Ministry of Environment; Ontario Ministry of Natural Resources and Forestry","keywords":"Permafrost; Land cover; Landform; Physical geography; Remote sensing; Vegetation (pathology); Geology; Latitude; Soil map; Spatial distribution; Environmental science; Land use; Geomorphology; Soil water; Soil science; Geography; Geodesy; Ecology","score_opus":0.04278474972757812,"score_gpt":0.22051759654273664,"score_spread":0.17773284681515852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2290948620","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99008465,0.00048253022,0.0067039113,0.0017796968,0.00067802,0.000041056406,0.00016028358,0.0000032112412,0.00006661254],"genre_scores_gemma":[0.9943117,0.0004058334,0.0043730764,0.00017347805,0.00058446324,2.404536e-9,0.00008452167,0.000005923793,0.00006100389],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987737,0.000050054387,0.00037823804,0.00016868905,0.00042147338,0.00020784211],"domain_scores_gemma":[0.9992657,0.0001452025,0.00026484486,0.00005412487,0.00017122935,0.000098949015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002947888,0.00012664155,0.00018902356,0.00018402135,0.0001151854,0.00006317438,0.000084738924,0.000050717423,0.000100881894],"category_scores_gemma":[0.000025083513,0.000096794545,0.000034319957,0.000049181857,0.000060274833,0.00026408094,0.00003156483,0.00014266574,0.0000010489666],"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":[0.00033926996,0.0000043088335,0.8895614,0.000012978736,0.000060671766,0.0007421685,0.0015490103,0.0098490305,0.00481575,9.795674e-7,0.00020045356,0.09286398],"study_design_scores_gemma":[0.0024258061,0.000060934457,0.67330205,0.0020604841,0.000031202093,0.005660052,0.0007493578,0.29257143,0.00036150668,0.0005087885,0.021772478,0.00049588946],"about_ca_topic_score_codex":0.8981343,"about_ca_topic_score_gemma":0.99441105,"teacher_disagreement_score":0.2827224,"about_ca_system_score_codex":0.00024062514,"about_ca_system_score_gemma":0.0002131538,"threshold_uncertainty_score":0.39471668},"labels":[],"label_agreement":null},{"id":"W2346651140","doi":"10.1080/01431161.2016.1175804","title":"Ocean remote sensing for sustainable resources","year":2016,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Oil Spill Detection and Mitigation","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Fisheries and Oceans Canada","funders":"National Aeronautics and Space Administration","keywords":"Remote sensing; Environmental science; Computer science; Oceanography; Environmental resource management; Geography; Geology","score_opus":0.008277882002318234,"score_gpt":0.2425852383086242,"score_spread":0.23430735630630595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2346651140","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6537917,0.000019329469,0.33734152,0.0036656507,0.00073869905,0.000083637155,0.0000012739823,0.000022792303,0.004335396],"genre_scores_gemma":[0.9185832,0.000030958687,0.0766351,0.00043306747,0.00056371157,9.646168e-10,6.299769e-7,0.000019663186,0.0037336592],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987798,0.000044970744,0.0003524533,0.00014580865,0.00046839556,0.00020858414],"domain_scores_gemma":[0.99904346,0.00012314916,0.00036886544,0.00009229765,0.0002800215,0.00009218319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005510818,0.000100237696,0.00012151754,0.00013053756,0.000110155604,0.00007814309,0.00015482181,0.000053282816,0.00006825441],"category_scores_gemma":[0.0005324904,0.00007338703,0.00013302341,0.00009080354,0.00008599841,0.00037346358,0.0000689566,0.00008891533,0.00002725477],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000121831414,0.0000036671233,0.000038793627,0.0000029335938,0.000030991556,0.000102955164,0.00021797027,0.0001618836,0.04058638,0.000014696174,0.0017650654,0.9569528],"study_design_scores_gemma":[0.003623474,0.00037532116,0.002085021,0.0006837343,0.00007024648,0.0033445347,0.0014980012,0.06073803,0.14235236,0.034985475,0.7497134,0.00053040177],"about_ca_topic_score_codex":0.00019890412,"about_ca_topic_score_gemma":0.00003236907,"teacher_disagreement_score":0.95642245,"about_ca_system_score_codex":0.00044147176,"about_ca_system_score_gemma":0.00001926836,"threshold_uncertainty_score":0.29926363},"labels":[],"label_agreement":null},{"id":"W2349491124","doi":"10.1080/01431161.2016.1182664","title":"Reflectance properties of grey-scale Spectralon® as a function of viewing angle, wavelength, and polarization","year":2016,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Planetary Science and Exploration","field":"Physics and Astronomy","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Ottawa Mental Health Centre; Royal Ontario Museum; University of Winnipeg; York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency; York University","keywords":"Reflectivity; Wavelength; Optics; Grey scale; Viewing angle; Polarization (electrochemistry); Scale (ratio); Materials science; Range (aeronautics); Ray; Angle of incidence (optics); Nadir; Remote sensing; Physics; Geology; Chemistry; Satellite","score_opus":0.017486236114906566,"score_gpt":0.23277730566030272,"score_spread":0.21529106954539615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2349491124","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94398177,0.00006577782,0.05497876,0.00043527468,0.0002631735,0.000028342298,0.0000029824132,0.0000022149977,0.00024167786],"genre_scores_gemma":[0.9957381,0.000029781619,0.0039382055,0.000021231363,0.00021707315,5.7922307e-9,0.0000034288062,0.0000036216966,0.000048554593],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99930906,0.00002493226,0.00028685038,0.00006741477,0.00024783317,0.000063916916],"domain_scores_gemma":[0.9991834,0.00001956789,0.0003670944,0.000043033822,0.00036179647,0.000025109637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016184265,0.00005485698,0.00011332671,0.000108532346,0.000023966739,0.000017351116,0.00006386697,0.000016097803,0.00000760962],"category_scores_gemma":[0.000021355027,0.0000373057,0.00004179151,0.000066429806,0.00004228461,0.00054223795,0.000011560973,0.00004888277,0.0000024296069],"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":[0.000092036644,0.000008610503,0.0018009291,0.0000038430967,0.00004150369,0.0000016860573,0.00035806143,0.000021722972,0.731219,0.00012678183,0.000008355007,0.26631752],"study_design_scores_gemma":[0.0019121153,0.0006026648,0.029181968,0.0027863071,0.00010665064,0.00025665647,0.0011778295,0.01595068,0.9234442,0.023267848,0.0009968209,0.00031627025],"about_ca_topic_score_codex":0.000051904026,"about_ca_topic_score_gemma":0.0000066646517,"teacher_disagreement_score":0.26600122,"about_ca_system_score_codex":0.00001719877,"about_ca_system_score_gemma":0.000043359683,"threshold_uncertainty_score":0.15212823},"labels":[],"label_agreement":null},{"id":"W2405481739","doi":"10.1080/01431161.2016.1183178","title":"Potential signatures of heavy metal complexes in lichen reflectance spectra","year":2016,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Lichen and fungal ecology","field":"Agricultural and Biological Sciences","cited_by":11,"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 Winnipeg","funders":"Canada Foundation for Innovation; University of Winnipeg; Canadian Space Agency; Brown University","keywords":"Lichen; Oxalate; Metal; Spectral line; Cadmium; Copper; Chemistry; Reflectivity; Diffuse reflectance infrared fourier transform; Nickel; Smelting; Environmental chemistry; Analytical Chemistry (journal); Photocatalysis; Inorganic chemistry; Catalysis","score_opus":0.016555486829240254,"score_gpt":0.2590553658824184,"score_spread":0.24249987905317816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2405481739","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99314076,0.00017088768,0.00044105598,0.005085838,0.00055983115,0.000029625233,0.000006774092,0.0000050134227,0.000560215],"genre_scores_gemma":[0.9957757,0.0000691936,0.0033561005,0.00017681613,0.000565012,5.4178333e-9,0.0000012189671,8.042989e-7,0.000055162174],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990682,0.00006821796,0.00036166908,0.00009488345,0.0002806247,0.00012640744],"domain_scores_gemma":[0.9992011,0.00017409817,0.00029555155,0.000021947184,0.00026690034,0.00004038366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024834505,0.0000729943,0.00018649884,0.000047397505,0.000019792853,0.000015780928,0.00022571186,0.000060952596,0.0000931513],"category_scores_gemma":[0.00013533862,0.000025594334,0.00011651606,0.00009307345,0.000058463032,0.00012493577,0.000038431463,0.00012486453,0.0000034690318],"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":[0.00016939544,0.000020256764,0.0003028245,7.2127506e-7,0.000036706486,0.0000826831,0.000038690407,0.000009189036,0.83741695,0.00010003763,0.00005553074,0.16176704],"study_design_scores_gemma":[0.001993591,0.0011542215,0.2704916,0.00059807464,0.000048117297,0.0016134657,0.00034139195,0.0016348489,0.6906261,0.023060393,0.008008832,0.0004293921],"about_ca_topic_score_codex":0.00009869182,"about_ca_topic_score_gemma":0.0004676501,"teacher_disagreement_score":0.27018878,"about_ca_system_score_codex":0.000054564385,"about_ca_system_score_gemma":0.000015367232,"threshold_uncertainty_score":0.10437066},"labels":[],"label_agreement":null},{"id":"W2463336507","doi":"10.1080/01431161.2016.1194545","title":"Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity","year":2016,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":123,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Russian Science Foundation; European Commission","keywords":"Moderate-resolution imaging spectroradiometer; Remote sensing; Pairwise comparison; Environmental science; Data set; Computer science; Thematic map; Baseline (sea); Calibration; Image resolution; Satellite; Statistics; Cartography; Geography; Mathematics; Artificial intelligence","score_opus":0.02811484508570678,"score_gpt":0.29704905684898025,"score_spread":0.2689342117632735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2463336507","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70914364,0.00007108097,0.28081566,0.004346163,0.0018713177,0.00012262487,0.000027081027,0.0000117202935,0.003590719],"genre_scores_gemma":[0.853736,0.00004750699,0.14539756,0.00020410762,0.0004679359,4.351359e-9,9.045048e-7,0.000008359427,0.00013761861],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9978872,0.00024954596,0.0004891119,0.00020017658,0.000968413,0.0002055663],"domain_scores_gemma":[0.9986959,0.00018418983,0.000546208,0.00019470771,0.00024901354,0.00012994157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000984509,0.00015766518,0.00025687326,0.000065614375,0.00014393181,0.000073044335,0.00062076416,0.00007736897,0.000060254453],"category_scores_gemma":[0.00025765377,0.00008104337,0.00016788999,0.00023031283,0.0001586571,0.00018698999,0.0006406616,0.00014964816,0.00003045157],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022012388,0.000013611431,0.0046490594,0.0000063743896,0.00018689557,0.00028223064,0.001851593,0.00078401156,0.123694696,0.000019251083,0.006620498,0.8616716],"study_design_scores_gemma":[0.004509578,0.000386941,0.44842568,0.0037629458,0.00017762752,0.01992901,0.0025032016,0.004095218,0.09094223,0.005537855,0.41853353,0.0011962026],"about_ca_topic_score_codex":0.00039237653,"about_ca_topic_score_gemma":0.00010673992,"teacher_disagreement_score":0.8604754,"about_ca_system_score_codex":0.00059723516,"about_ca_system_score_gemma":0.000022598892,"threshold_uncertainty_score":0.33048528},"labels":[],"label_agreement":null},{"id":"W2474948341","doi":"10.1080/01431161.2016.1201230","title":"The effects of topographic correction and gap filling in imagery on the detection of tropical forest disturbances using a Landsat time series in Myanmar","year":2016,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trent University","funders":"Japan Society for the Promotion of Science; Japan Student Services Organization; Trent University","keywords":"Change detection; Thematic Mapper; Remote sensing; Terrain; Pixel; Thematic map; Disturbance (geology); Trajectory; Geology; Environmental science; Satellite imagery; Computer science; Cartography; Artificial intelligence; Geography; Geomorphology; Physics","score_opus":0.005109372862703172,"score_gpt":0.20609602395619728,"score_spread":0.20098665109349412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2474948341","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99451447,0.00008037565,0.003984392,0.00064424466,0.00064206176,0.000066717366,3.2627597e-7,0.0000021732494,0.000065248205],"genre_scores_gemma":[0.99819016,0.00014030396,0.0015419028,0.000016748714,0.00008071658,8.811123e-9,8.29747e-8,0.000004732332,0.0000253322],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9990592,0.00012580778,0.00030631642,0.00008731311,0.00032976244,0.00009158135],"domain_scores_gemma":[0.9987011,0.00083547033,0.00033445325,0.00006314178,0.000048357673,0.000017497061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028298338,0.00007614262,0.00012739521,0.00008332303,0.000052099116,0.000028239285,0.000097894,0.000040947903,0.0000016019702],"category_scores_gemma":[0.0006586477,0.000035220528,0.00005757065,0.00014556003,0.00022397115,0.0001511896,0.000037110614,0.00014564981,5.4034075e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004691152,0.000021497806,0.030433953,0.000009070867,0.00003448467,0.00005026481,0.00052975956,0.0012006973,0.68269485,0.000011370709,0.000023832981,0.28452113],"study_design_scores_gemma":[0.0008111286,0.000301362,0.79079247,0.002999572,0.000023566392,0.0008684422,0.00027260365,0.042515017,0.15713513,0.00385215,0.00027436117,0.0001541788],"about_ca_topic_score_codex":0.0001910101,"about_ca_topic_score_gemma":0.0005919391,"teacher_disagreement_score":0.7603585,"about_ca_system_score_codex":0.0001294801,"about_ca_system_score_gemma":0.0000075838525,"threshold_uncertainty_score":0.14362514},"labels":[],"label_agreement":null},{"id":"W2486602464","doi":"10.1080/01431161.2016.1189746","title":"RADARSAT-2: applications","year":2016,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","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":"University of New Brunswick","funders":"","keywords":"Remote sensing; Environmental science; Computer science; Geology","score_opus":0.007083996430641413,"score_gpt":0.23834101951332212,"score_spread":0.2312570230826807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2486602464","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027982981,0.0001893687,0.9902507,0.0017921062,0.00034142734,0.00005983684,0.0000042607344,0.000097046584,0.004466933],"genre_scores_gemma":[0.23959014,0.00028637776,0.75951433,0.000070344824,0.0004616018,2.879635e-8,6.3697235e-7,0.000018493327,0.00005805595],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992682,0.000009327132,0.0003016443,0.00006888452,0.00025725274,0.000094737254],"domain_scores_gemma":[0.9993255,0.00010165997,0.000105473126,0.00013220593,0.0002823064,0.00005284644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013142121,0.000085407686,0.00011099071,0.00015145674,0.00002385372,0.000023781844,0.00022464008,0.00004730555,0.0000276076],"category_scores_gemma":[0.0000317918,0.000059746093,0.000085926,0.000069969145,0.00003607165,0.000102772814,0.000018269197,0.00008846433,0.000020396345],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003988402,0.000004026338,0.0000057788916,0.0000015380554,0.000055357566,0.000010215787,0.000021990894,0.0000012576885,0.0071846545,0.00050390506,0.00081747613,0.9913898],"study_design_scores_gemma":[0.0001720305,0.000008579249,0.000052848784,0.00010139821,0.000011200125,0.0005975079,0.000014314023,0.0016881461,0.044211045,0.006270427,0.9467906,0.000081906655],"about_ca_topic_score_codex":0.0000051171196,"about_ca_topic_score_gemma":0.0000016519843,"teacher_disagreement_score":0.9913079,"about_ca_system_score_codex":0.00012523774,"about_ca_system_score_gemma":0.000021068148,"threshold_uncertainty_score":0.2436375},"labels":[],"label_agreement":null},{"id":"W2523609223","doi":"10.1080/01431161.2016.1219425","title":"Updating residual stem volume estimates using ALS- and UAV-acquired stereo-photogrammetric point clouds","year":2016,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Point cloud; Forest inventory; Photogrammetry; Environmental science; Residual; Volume (thermodynamics); Remote sensing; Mean squared error; Laser scanning; Diameter at breast height; Lidar; Forest management; Forestry; Computer science; Mathematics; Statistics; Geography; Agroforestry; Computer vision; Algorithm","score_opus":0.020753468623528865,"score_gpt":0.2713253923537878,"score_spread":0.25057192373025894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2523609223","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87531096,0.00005870786,0.12175779,0.001472936,0.0004546167,0.00007335502,0.0000042310485,0.000024034769,0.00084336643],"genre_scores_gemma":[0.8337612,0.00003606788,0.16563436,0.00013586498,0.0002826289,7.1304855e-9,8.526342e-7,0.000022646385,0.00012634476],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818367,0.00007829103,0.0005974714,0.00024357461,0.0006476417,0.0002493786],"domain_scores_gemma":[0.9987319,0.00020432293,0.0005875959,0.000176003,0.00015056807,0.00014961365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061756134,0.00017086808,0.00022791637,0.0002056016,0.00013581973,0.00014912878,0.00023766687,0.000071099115,0.000071048395],"category_scores_gemma":[0.00020686387,0.00012794718,0.00009352354,0.00022475087,0.00019066113,0.00034343213,0.00018039679,0.00011478549,0.000034742367],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005023736,0.000014483125,0.0025808653,0.0000031208192,0.00007071484,0.000081784674,0.00026460158,0.00025497624,0.18384291,0.0000110058945,0.00030679163,0.81251854],"study_design_scores_gemma":[0.00722743,0.00072710274,0.084918864,0.0036267,0.000520959,0.030289587,0.0032888022,0.6262963,0.18969446,0.011644996,0.039389636,0.0023751552],"about_ca_topic_score_codex":0.0005433675,"about_ca_topic_score_gemma":0.000030808205,"teacher_disagreement_score":0.81014335,"about_ca_system_score_codex":0.0003195945,"about_ca_system_score_gemma":0.000033787524,"threshold_uncertainty_score":0.52175343},"labels":[],"label_agreement":null},{"id":"W2529940551","doi":"10.1080/01431161.2016.1244364","title":"Multilayer semantic segmentation of remote-sensing imagery using a hybrid object-based Markov random field model","year":2016,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Wuhan University; National Natural Science Foundation of China","keywords":"Computer science; Markov random field; Segmentation; Artificial intelligence; Markov chain; Pattern recognition (psychology); Probabilistic logic; Cut; Graph; Field (mathematics); Image segmentation; Inference; Random field; Stochastic matrix; Data mining; Machine learning; Theoretical computer science; Mathematics","score_opus":0.020273454756604456,"score_gpt":0.271200313074044,"score_spread":0.25092685831743955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2529940551","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35465476,0.00005824887,0.64350015,0.00042339248,0.0010293965,0.00010022682,0.000005630543,0.000048312893,0.00017987197],"genre_scores_gemma":[0.60936695,0.00004698671,0.3901765,0.00009032936,0.00024078564,3.1855316e-9,0.0000029885912,0.000049785634,0.000025663201],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973998,0.00010980405,0.0011339211,0.00023487511,0.0008294462,0.0002921276],"domain_scores_gemma":[0.9971702,0.0005279629,0.0007370322,0.00029041435,0.0011666623,0.00010773294],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00063654117,0.00028609755,0.0004572824,0.0006570985,0.00005887928,0.00009474961,0.00022473533,0.00010879386,0.000009482363],"category_scores_gemma":[0.00075109105,0.00024576348,0.00030656575,0.00016113982,0.00008105452,0.00046798686,0.000038192553,0.00027895058,0.0000059101403],"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":[0.00021917878,0.000006362388,0.0000051306606,0.000026729473,0.00010502787,0.00011977442,0.00008370953,0.032056175,0.53969467,3.472047e-7,0.00014300203,0.42753989],"study_design_scores_gemma":[0.0015085508,0.000019497422,0.000021967631,0.00094407506,0.000057672103,0.0005229338,0.000031008683,0.60395795,0.39254346,0.00019752256,0.00003982461,0.00015549822],"about_ca_topic_score_codex":0.00007029023,"about_ca_topic_score_gemma":0.000011375058,"teacher_disagreement_score":0.5719018,"about_ca_system_score_codex":0.0004955013,"about_ca_system_score_gemma":0.00015224236,"threshold_uncertainty_score":0.99999946},"labels":[],"label_agreement":null},{"id":"W2600004689","doi":"10.1080/01431161.2017.1302107","title":"Segmentation parameter selection for object-based land-cover mapping from ultra high resolution spectral and elevation data","year":2017,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Segmentation; Computer science; Elevation (ballistics); Artificial intelligence; Image segmentation; Land cover; Scale-space segmentation; Segmentation-based object categorization; Minimum spanning tree-based segmentation; Pattern recognition (psychology); Aerial image; Computer vision; Object (grammar); Remote sensing; Image (mathematics); Mathematics; Geography; Land use","score_opus":0.041557390753503044,"score_gpt":0.2877838556931378,"score_spread":0.24622646493963476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2600004689","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4157056,0.000024240695,0.58245313,0.0005658759,0.0010464106,0.00009178364,0.000024048337,0.00002882748,0.00006007854],"genre_scores_gemma":[0.72609866,0.00004199015,0.27291664,0.000049065668,0.0007074658,3.3821323e-8,0.00014846394,0.000022880824,0.000014791897],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988615,0.000037861268,0.0004192034,0.00019971417,0.00034228826,0.00013946119],"domain_scores_gemma":[0.9985691,0.00021024606,0.0004894532,0.00027764545,0.00040754332,0.00004604338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041159848,0.0001335846,0.00016585263,0.00020784081,0.00015161818,0.00041498503,0.00024960132,0.000084771156,0.00000515316],"category_scores_gemma":[0.00064788794,0.00014070365,0.000053008673,0.000040815747,0.00004156222,0.0010725011,0.000021456122,0.00018265103,0.0000045348816],"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":[0.0002255321,0.000013211565,0.0008699329,0.000026664073,0.00026004144,0.000014208727,0.00016570165,0.023281513,0.5668123,0.0000111128675,0.0007542341,0.4075656],"study_design_scores_gemma":[0.0010452978,0.000024185,0.017852735,0.00020799166,0.000050500334,0.00008121938,0.000021978074,0.92101353,0.057794906,0.00079731166,0.0009758623,0.00013450667],"about_ca_topic_score_codex":0.00024189103,"about_ca_topic_score_gemma":0.000057135218,"teacher_disagreement_score":0.897732,"about_ca_system_score_codex":0.0002790237,"about_ca_system_score_gemma":0.000043804415,"threshold_uncertainty_score":0.5737728},"labels":[],"label_agreement":null},{"id":"W2612601779","doi":"10.1080/01431161.2017.1317933","title":"Accurate crop-type classification using multi-temporal optical and multi-polarization SAR data in an object-based image analysis framework","year":2017,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote-Sensing Image Classification","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":"Agriculture and Agri-Food Canada","funders":"Canadian Space Agency","keywords":"Remote sensing; Computer science; Object based; Pattern recognition (psychology); Contextual image classification; Artificial intelligence; Optical image; Object (grammar); Data mining; Computer vision; Image (mathematics); Geology","score_opus":0.10495525452031046,"score_gpt":0.37848541891255827,"score_spread":0.2735301643922478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612601779","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4319741,0.000047866426,0.56714624,0.0001864692,0.0005418852,0.000059779435,0.0000073818205,0.000025888434,0.000010368028],"genre_scores_gemma":[0.5355372,0.000024189112,0.46419168,0.000018669862,0.00014323856,2.867573e-9,0.00005657457,0.000025751355,0.0000026964142],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99810195,0.00011458758,0.00075955776,0.00032943318,0.00048032746,0.00021416992],"domain_scores_gemma":[0.9973693,0.00011450051,0.0006788774,0.0008262072,0.0008816047,0.00012950785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007738193,0.00021816065,0.0003474373,0.000722501,0.00015746737,0.0008444867,0.00065003603,0.0001899704,0.0000033068407],"category_scores_gemma":[0.0015969275,0.00023060864,0.000080403915,0.0002742575,0.00015435116,0.0016688001,0.000102677026,0.0005074826,0.0000030946978],"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":[0.00022704041,0.00012266688,0.027631389,0.000041746185,0.000649376,0.0004239884,0.00047769662,0.06504251,0.7242537,0.000021382642,0.000005798421,0.18110271],"study_design_scores_gemma":[0.0006344479,0.000016957178,0.12441089,0.00020202404,0.00015451759,0.000077389646,0.00007019044,0.8696861,0.0044806935,0.000041439183,0.000037008584,0.00018831603],"about_ca_topic_score_codex":0.0001661153,"about_ca_topic_score_gemma":0.00019632452,"teacher_disagreement_score":0.80464363,"about_ca_system_score_codex":0.00029404185,"about_ca_system_score_gemma":0.000108102424,"threshold_uncertainty_score":0.9403947},"labels":[],"label_agreement":null},{"id":"W2617128359","doi":"10.1080/01431161.2017.1328145","title":"Improving UAV imaging quality by optical sensor fusion: an initial study","year":2017,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"University of New Brunswick","funders":"Atlantic Canada Opportunities Agency","keywords":"Image resolution; Panchromatic film; Pixel; Artificial intelligence; Computer science; Computer vision; Image sensor; Remote sensing; Filter (signal processing); Image fusion; Noise (video); Image (mathematics); Geology","score_opus":0.02148270447447478,"score_gpt":0.359570464563805,"score_spread":0.3380877600893302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2617128359","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.552438,0.00005690848,0.44431162,0.00024544058,0.0014776463,0.00008610601,0.0000055825312,0.000119159806,0.0012595358],"genre_scores_gemma":[0.8302168,0.000022378335,0.16888623,0.00006730536,0.00074950565,2.491181e-8,0.0000021729352,0.000036034773,0.000019549809],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982414,0.00006390758,0.000654478,0.00016845501,0.00066754303,0.00020417475],"domain_scores_gemma":[0.9983372,0.00007839903,0.000454559,0.00034750634,0.00064641703,0.00013591153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006678379,0.00018008993,0.00026432736,0.00016410144,0.0001847169,0.00038337128,0.00054283056,0.000050347775,0.000017861359],"category_scores_gemma":[0.00065018696,0.00017659484,0.00010216763,0.000028260236,0.0000784749,0.0010263401,0.00016446882,0.0004579319,0.0000040647988],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006625465,0.000042298736,0.00022576904,0.000008297952,0.000056757286,0.0007299163,0.00027805395,0.00018789302,0.1763342,0.000003711452,0.00014174338,0.8219251],"study_design_scores_gemma":[0.0047929324,0.0004811014,0.008585212,0.0007883522,0.00012416343,0.004845704,0.0038616047,0.59065425,0.37849322,0.0019633395,0.004063204,0.0013469442],"about_ca_topic_score_codex":0.000116915355,"about_ca_topic_score_gemma":0.00001891871,"teacher_disagreement_score":0.82057816,"about_ca_system_score_codex":0.00017778676,"about_ca_system_score_gemma":0.000030457153,"threshold_uncertainty_score":0.7201329},"labels":[],"label_agreement":null},{"id":"W2626876192","doi":"10.1080/01431161.2017.1339920","title":"Dynamic response of NDVI to soil moisture variations during different hydrological regimes in the Sahel region","year":2017,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":53,"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":"Erasmus+; National Oceanic and Atmospheric Administration; National Aeronautics and Space Administration","keywords":"Shrubland; Normalized Difference Vegetation Index; Environmental science; Deciduous; Vegetation (pathology); Grassland; Water content; Arid; Enhanced vegetation index; Climate change; Hydrology (agriculture); Physical geography; Geography; Ecosystem; Ecology; Vegetation Index; Geology","score_opus":0.009881271481866304,"score_gpt":0.24534779008382207,"score_spread":0.23546651860195578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2626876192","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9847405,0.000007682125,0.008552206,0.0054907,0.00024850303,0.000056572633,0.0000029370492,0.0000030383185,0.0008978499],"genre_scores_gemma":[0.996586,0.000022844328,0.0030010436,0.0001088655,0.00003935502,3.3882138e-8,0.0000013027493,0.000005020017,0.00023553529],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99891114,0.00012457065,0.00031834096,0.00010507049,0.00043378057,0.00010708757],"domain_scores_gemma":[0.99920636,0.00010413428,0.0004002627,0.00021054917,0.00003988637,0.00003879516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045503266,0.000082298146,0.00012759246,0.000107135595,0.000113201095,0.00007479253,0.0004967389,0.0000543219,0.0000074976374],"category_scores_gemma":[0.00028540293,0.00005457383,0.00008111953,0.00004699054,0.00007053047,0.00014805765,0.00010850929,0.00020912803,0.0000039481706],"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":[0.007777938,0.0005452577,0.12328786,0.000020683763,0.000433421,0.005216975,0.015583853,0.3867071,0.3174452,0.00082155736,0.00043857246,0.1417216],"study_design_scores_gemma":[0.0005082438,0.00006751938,0.88072187,0.00013810558,0.00001713883,0.0013347144,0.000082248116,0.11320889,0.00060830085,0.0030122555,0.00020658762,0.00009412356],"about_ca_topic_score_codex":0.00020266954,"about_ca_topic_score_gemma":0.00033215026,"teacher_disagreement_score":0.757434,"about_ca_system_score_codex":0.00019298086,"about_ca_system_score_gemma":0.000010993727,"threshold_uncertainty_score":0.22254561},"labels":[],"label_agreement":null},{"id":"W2715069849","doi":"10.1080/01431161.2017.1325530","title":"The application of discriminant analysis for mapping cereals and pasture using object-based features","year":2017,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":5,"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","funders":"Agriculture and Agri-Food Canada; Canadian Space Agency","keywords":"Pasture; Linear discriminant analysis; Object (grammar); Discriminant; Computer science; Pattern recognition (psychology); Artificial intelligence; Remote sensing; Environmental science; Agronomy; Geography; Biology","score_opus":0.026754621926231136,"score_gpt":0.2893967097947528,"score_spread":0.26264208786852167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2715069849","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8074161,0.00032714175,0.19026922,0.0012075006,0.0004768151,0.00007088434,0.000015685457,0.0000030596336,0.00021360209],"genre_scores_gemma":[0.96388155,0.00007211978,0.035699893,0.00004788748,0.0002714832,1.3279994e-9,0.000008164201,0.000002713096,0.000016186837],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991677,0.000037453465,0.00029514873,0.000099971294,0.000288398,0.0001113366],"domain_scores_gemma":[0.9983675,0.00025419082,0.00080624066,0.00016074187,0.0003642445,0.00004710586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004923173,0.00008101486,0.00018839513,0.00015384838,0.00037198002,0.0002301065,0.00022249708,0.000042269978,0.0000011086873],"category_scores_gemma":[0.00023022137,0.00004874151,0.00015574564,0.000055151908,0.00009421969,0.00010912646,0.000010217815,0.000099065706,1.4622124e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017556349,0.0000031502436,0.026423972,0.000009209146,0.00035170378,0.000012036135,0.00020108564,0.0076292027,0.001627621,0.000008932965,0.000020011117,0.9635375],"study_design_scores_gemma":[0.00033572502,0.00003223142,0.44435441,0.000093519935,0.00017528907,0.0001162355,0.00019574216,0.5513678,0.00086427387,0.0005921412,0.0018026078,0.00007001226],"about_ca_topic_score_codex":0.0024985943,"about_ca_topic_score_gemma":0.002629648,"teacher_disagreement_score":0.9634675,"about_ca_system_score_codex":0.00000806238,"about_ca_system_score_gemma":0.00004538931,"threshold_uncertainty_score":0.3777144},"labels":[],"label_agreement":null},{"id":"W2737021924","doi":"10.1080/01431161.2017.1354266","title":"Sub-pixel vs. super-pixel-based greenspace mapping along the urban–rural gradient using high spatial resolution Gaofen-2 satellite imagery: a case study of Haidian District, Beijing, China","year":2017,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Beijing; Pixel; Remote sensing; Geography; Multispectral image; Image resolution; China; Environmental science; Cartography; Physical geography; Computer science; Artificial intelligence","score_opus":0.0172504218676299,"score_gpt":0.2473442623300153,"score_spread":0.2300938404623854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2737021924","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98986983,0.000084238614,0.007967591,0.0007959943,0.0009867728,0.00019905501,0.000009152406,0.000010313289,0.00007702344],"genre_scores_gemma":[0.9969693,0.000031589643,0.0024684174,0.000051281102,0.0004482079,5.977505e-8,0.0000041137364,0.000020931637,0.000006098399],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99775755,0.00018677262,0.000695357,0.00022289946,0.0008480197,0.00028941527],"domain_scores_gemma":[0.9979963,0.000086116925,0.0012713827,0.00038199968,0.00014327819,0.000120938275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008241006,0.00021403797,0.00031811275,0.00014643272,0.00063793093,0.00028233082,0.0005587568,0.000061816434,0.000017660052],"category_scores_gemma":[0.00008155979,0.00015328004,0.00017541127,0.0001022525,0.00008976049,0.000580993,0.00023473345,0.00027333613,0.000005891851],"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":[0.0008877159,0.0004922701,0.6818595,0.000079220394,0.00071699946,0.012336727,0.011521168,0.022291016,0.04467372,0.000005605727,0.00011376606,0.22502229],"study_design_scores_gemma":[0.0032002102,0.0004281545,0.5670207,0.0010677004,0.00024318871,0.01005458,0.0037375106,0.40973234,0.0030297455,0.000075006574,0.0009208567,0.0004899705],"about_ca_topic_score_codex":0.0645524,"about_ca_topic_score_gemma":0.027110603,"teacher_disagreement_score":0.38744134,"about_ca_system_score_codex":0.00037765777,"about_ca_system_score_gemma":0.00004087194,"threshold_uncertainty_score":0.9906421},"labels":[],"label_agreement":null},{"id":"W2738076904","doi":"10.1080/01431161.2017.1356488","title":"A comparative assessment of multi-temporal Landsat 8 and machine learning algorithms for estimating aboveground carbon stock in coppice oak forests","year":2017,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":75,"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":"Tarbiat Modares University; Universidade de São Paulo; University of British Columbia","keywords":"Random forest; Support vector machine; Remote sensing; Environmental science; Understory; Multispectral image; Multivariate adaptive regression splines; Carbon stock; Biomass (ecology); Computer science; Climate change; Regression analysis; Machine learning; Bayesian multivariate linear regression; Canopy; Ecology; Geography","score_opus":0.04740792611913073,"score_gpt":0.3532038238105832,"score_spread":0.30579589769145243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2738076904","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89696914,0.000037231697,0.10143494,0.00043916464,0.0005176555,0.00015500942,0.000004325609,0.0000052313544,0.00043730193],"genre_scores_gemma":[0.605785,0.0000058082687,0.3940533,0.000014436172,0.000095906355,1.355041e-8,0.0000035147652,0.000006695862,0.0000352991],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986518,0.000073083516,0.00046715705,0.00017243194,0.0004812193,0.0001543005],"domain_scores_gemma":[0.99841005,0.00016682521,0.0010976382,0.000106013846,0.00015545014,0.00006402419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006009049,0.00014338561,0.00032347234,0.000083035244,0.00012387408,0.00012361325,0.00023910972,0.00006848283,0.0000026404825],"category_scores_gemma":[0.00028578474,0.00011545549,0.000072194656,0.000040983155,0.00013241652,0.00025662073,0.0001452907,0.00035766518,3.2679895e-7],"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":[0.00028608704,0.00013675634,0.3611317,0.000045497593,0.0002863676,0.00035605783,0.0039757397,0.06826356,0.054100584,0.000009864382,0.00007288265,0.5113349],"study_design_scores_gemma":[0.0011415455,0.000090049165,0.22978535,0.00027493355,0.000014553473,0.0002987452,0.00012674069,0.7671487,0.00072616944,0.00011607254,0.0001829084,0.00009428415],"about_ca_topic_score_codex":0.0022868474,"about_ca_topic_score_gemma":0.0025410578,"teacher_disagreement_score":0.6988851,"about_ca_system_score_codex":0.00025847016,"about_ca_system_score_gemma":0.000025528974,"threshold_uncertainty_score":0.4708138},"labels":[],"label_agreement":null},{"id":"W2752105008","doi":"10.1080/01431161.2017.1372863","title":"Enhanced decision tree ensembles for land-cover mapping from fully polarimetric SAR data","year":2017,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":29,"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 Ottawa","funders":"Agriculture and Agri-Food Canada; Jet Propulsion Laboratory; Iran National Science Foundation; European Space Agency","keywords":"Random forest; Land cover; Computer science; Decision tree; Polarimetry; Tree (set theory); Synthetic aperture radar; Filter (signal processing); Feature (linguistics); Data mining; Remote sensing; Artificial intelligence; Pattern recognition (psychology); Mathematics; Land use; Geography; Computer vision","score_opus":0.031244780955813612,"score_gpt":0.2977821103411442,"score_spread":0.2665373293853306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2752105008","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06502235,0.00043481996,0.93195826,0.00043795962,0.0008568805,0.00008456396,0.00006056145,0.000043910582,0.0011007026],"genre_scores_gemma":[0.41098848,0.00018768523,0.5881777,0.000043212214,0.00055242475,9.277512e-9,0.000017808961,0.000018934063,0.000013734193],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989658,0.000009941091,0.00039433417,0.00015775822,0.00034289306,0.00012925218],"domain_scores_gemma":[0.9984337,0.0003579617,0.00030957,0.00053148647,0.00031402387,0.000053274314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029473033,0.00012845169,0.00021048539,0.00024050105,0.00011690344,0.00020474763,0.00086255546,0.000086166816,0.000011656633],"category_scores_gemma":[0.0005497212,0.000114633076,0.0001002636,0.000047368412,0.000026403883,0.00039359287,0.00012306312,0.00016106247,0.0000067420374],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003458806,0.0000064246756,0.000023483846,0.000003042332,0.00013306813,0.000013490086,0.000036600974,0.00002535199,0.01281218,0.00002132578,0.0007068502,0.9861836],"study_design_scores_gemma":[0.0009696016,0.000032102278,0.0024828664,0.0006207416,0.00006770157,0.00022368177,0.0000344849,0.2658506,0.08562011,0.008057891,0.6357624,0.00027780872],"about_ca_topic_score_codex":0.00018248083,"about_ca_topic_score_gemma":0.000021452002,"teacher_disagreement_score":0.98590577,"about_ca_system_score_codex":0.000099562894,"about_ca_system_score_gemma":0.000031702206,"threshold_uncertainty_score":0.46746013},"labels":[],"label_agreement":null},{"id":"W2767307300","doi":"10.1080/01431161.2017.1395973","title":"On the use of temporal vegetation indices in support of eligibility controls for EU aids in agriculture","year":2017,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":6,"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":"Università degli Studi della Basilicata; Regione Basilicata; Concordia University of Edmonton","keywords":"Arable land; Thematic Mapper; Context (archaeology); Orthophoto; Remote sensing; Vegetation (pathology); Identification (biology); Agricultural land; Land use; Satellite imagery; Common Agricultural Policy; Computer science; Agriculture; Cartography; Geography; Ecology","score_opus":0.030293493475819826,"score_gpt":0.2996066246372549,"score_spread":0.26931313116143507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767307300","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9957966,0.000007730661,0.0010512352,0.00206826,0.0003639934,0.0001954736,0.000005841704,0.0000018414241,0.0005090258],"genre_scores_gemma":[0.98135084,0.000011331492,0.01828183,0.00024367,0.00007371567,1.7378321e-8,0.0000029738724,0.0000054646607,0.000030134244],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99842846,0.00009132552,0.000631529,0.00013517983,0.0005948067,0.00011868795],"domain_scores_gemma":[0.99787,0.00038987535,0.0013474576,0.00017887943,0.00018299815,0.000030775805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081952894,0.000107367334,0.00024952771,0.00009139927,0.000045550638,0.00006610216,0.00036469076,0.00008332637,0.0000073748975],"category_scores_gemma":[0.0014629469,0.00006342293,0.00013121257,0.00007438908,0.00016011664,0.00037695956,0.000062866704,0.00023024948,0.0000015745103],"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":[0.0019240237,0.00043565637,0.13710032,0.00006144665,0.00028489405,0.0002767807,0.004718048,0.07190049,0.49315032,0.00043701136,0.005060724,0.2846503],"study_design_scores_gemma":[0.002833826,0.00047382945,0.9098672,0.0009696177,0.000038748494,0.00023007303,0.00024191852,0.019610103,0.05439883,0.006357313,0.0047303797,0.00024814083],"about_ca_topic_score_codex":0.0008468042,"about_ca_topic_score_gemma":0.0014014152,"teacher_disagreement_score":0.7727669,"about_ca_system_score_codex":0.00018446053,"about_ca_system_score_gemma":0.000023790974,"threshold_uncertainty_score":0.2586312},"labels":[],"label_agreement":null},{"id":"W2781910890","doi":"10.1080/01431161.2017.1420940","title":"Combining image processing and machine learning to identify invasive plants in high-resolution images","year":2018,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thompson Rivers University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Random forest; Thresholding; Computer science; Pattern recognition (psychology); Classifier (UML); Image processing; Contextual image classification; Feature extraction; Computer vision; Feature selection; Image (mathematics)","score_opus":0.011469236994357496,"score_gpt":0.2680761452632907,"score_spread":0.25660690826893323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2781910890","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98259354,0.00005437616,0.0142776435,0.0013869469,0.00053840876,0.00006970067,0.0000015076141,0.000015788353,0.0010620693],"genre_scores_gemma":[0.900924,0.000042339165,0.098417856,0.00022195793,0.00029127148,4.8639532e-9,0.0000029423065,0.000014125735,0.00008545148],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9984605,0.00010176717,0.00041469195,0.00021374691,0.00059797947,0.00021131373],"domain_scores_gemma":[0.9992071,0.00008405554,0.00037787692,0.00006424354,0.00016331923,0.00010342466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005156886,0.00014266047,0.00018915866,0.00020114267,0.000115638475,0.00018748369,0.0002002349,0.00006161661,0.00002279988],"category_scores_gemma":[0.0005022552,0.00012174461,0.000038592665,0.00017995728,0.00014885238,0.0004892937,0.00021369963,0.00036453552,0.000040786985],"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":[0.00014487261,0.000018374918,0.003381543,0.000005998071,0.000027133825,0.0005258969,0.0028583023,0.0019375081,0.6015242,0.0000013268822,0.0005014766,0.3890734],"study_design_scores_gemma":[0.004027526,0.0007210136,0.54855114,0.0042483388,0.000065226515,0.0119295865,0.0026770912,0.17268708,0.24585739,0.0029668692,0.0051863925,0.0010823603],"about_ca_topic_score_codex":0.0006489796,"about_ca_topic_score_gemma":0.00053299783,"teacher_disagreement_score":0.5451696,"about_ca_system_score_codex":0.00029223954,"about_ca_system_score_gemma":0.00001960521,"threshold_uncertainty_score":0.49646014},"labels":[],"label_agreement":null},{"id":"W2783616714","doi":"10.1080/01431161.2018.1425564","title":"MSMD: maximum separability and minimum dependency feature selection for cropland classification from optical and radar data","year":2018,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","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":"University of Ottawa","funders":"National Oceanic and Atmospheric Administration; National Aeronautics and Space Administration","keywords":"Feature selection; Classifier (UML); Synthetic aperture radar; Computer science; Radar; Remote sensing; Filter (signal processing); Dependency (UML); Artificial intelligence; Feature (linguistics); Random forest; Data mining; Selection (genetic algorithm); Pattern recognition (psychology); Geography; Computer vision","score_opus":0.022957560729329852,"score_gpt":0.2890158317423346,"score_spread":0.2660582710130047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2783616714","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19486415,0.0002324196,0.802943,0.0012584422,0.00029577687,0.00009684283,0.000033717944,0.000036941063,0.0002387358],"genre_scores_gemma":[0.44041467,0.00010269231,0.55895436,0.000032895547,0.0004599344,3.8456232e-8,0.000018899193,0.000010816165,0.0000056710714],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992906,0.000015498395,0.0002445481,0.00017251258,0.00018570112,0.00009116193],"domain_scores_gemma":[0.99919957,0.00017457418,0.00010282841,0.000156522,0.00030844088,0.00005804476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023949576,0.00010179587,0.00013882847,0.00008331475,0.000060408805,0.00007893354,0.00015756287,0.000102647966,0.0000045705738],"category_scores_gemma":[0.00012872247,0.00009105844,0.000027451139,0.00004492017,0.00007749178,0.00018473943,0.000041076117,0.00015179627,6.4357886e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006301632,0.000006766436,0.00012431195,0.0000071856884,0.00008364145,0.0000024266649,0.000069434805,4.232648e-7,0.015422459,0.00009839102,0.0008931887,0.98322874],"study_design_scores_gemma":[0.00091607723,0.00013425542,0.007171051,0.00020841983,0.00013935445,0.0016175568,0.0001418102,0.644575,0.036816005,0.021697603,0.28628406,0.00029881878],"about_ca_topic_score_codex":0.00002580468,"about_ca_topic_score_gemma":0.000043876826,"teacher_disagreement_score":0.98292994,"about_ca_system_score_codex":0.00006282934,"about_ca_system_score_gemma":0.000024288094,"threshold_uncertainty_score":0.37132555},"labels":[],"label_agreement":null},{"id":"W2784920166","doi":"10.1080/01431161.2018.1423741","title":"Earthquakes from space: Earth observation for quantifying earthquake risks","year":2017,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Earthquake Detection and Analysis","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute on Governance","funders":"Royal Society; Royal Geographical Society; World Bank Group","keywords":"Geology; Seismology; Earth observation; Remote sensing; Space (punctuation); Earth (classical element); Computer science; Satellite; Physics; Aerospace engineering; Engineering; Astronomy","score_opus":0.10779795815265034,"score_gpt":0.3284022241060234,"score_spread":0.22060426595337304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2784920166","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93435174,0.00016052487,0.059237335,0.0021968246,0.003321315,0.00006949198,0.000056021672,0.000017844715,0.00058888056],"genre_scores_gemma":[0.929385,0.00012421755,0.06866482,0.0002056486,0.0013197687,6.650005e-9,0.000036853944,0.000007093389,0.00025659084],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9984261,0.00006643851,0.0005047365,0.00019864192,0.0006023614,0.0002017657],"domain_scores_gemma":[0.9976416,0.00026049162,0.0010225311,0.0002408577,0.00070861686,0.00012588012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060801755,0.00014742425,0.00026931646,0.00021971724,0.00045540155,0.0006612942,0.0004184153,0.00008560724,0.00024027862],"category_scores_gemma":[0.0007751755,0.00013008031,0.0003117372,0.00005974715,0.00007584378,0.00069519074,0.000015730733,0.00023036166,0.00005331098],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015039506,0.0000054618026,0.044072993,0.0000032321407,0.00020650422,0.00006403748,0.00013267218,0.0019011842,0.0012622804,0.0000115063995,0.000093679075,0.95209605],"study_design_scores_gemma":[0.00096488034,0.00010086312,0.8117926,0.0001693748,0.000072605195,0.00010328364,0.00021360703,0.15494055,0.003959307,0.0015619696,0.025922734,0.00019819276],"about_ca_topic_score_codex":0.0034775848,"about_ca_topic_score_gemma":0.00802103,"teacher_disagreement_score":0.95189786,"about_ca_system_score_codex":0.0000082976185,"about_ca_system_score_gemma":0.00006677027,"threshold_uncertainty_score":0.63768756},"labels":[],"label_agreement":null},{"id":"W2791989032","doi":"10.1080/01431161.2018.1433889","title":"Sea surface wind speed and sea state retrievals from dual-frequency altimeter and its preliminary application in global view of wind-sea and swell distributions","year":2018,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Ocean Waves and Remote Sensing","field":"Earth and Planetary Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bedford Institute of Oceanography","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Swell; Altimeter; Significant wave height; Buoy; Sea state; Radar altimeter; Wind speed; Wind wave; Remote sensing; Wave height; Geology; Sea-surface height; Wind wave model; Meteorology; Environmental science; Geodesy; Geography; Oceanography","score_opus":0.012575254385019706,"score_gpt":0.253830343471306,"score_spread":0.2412550890862863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791989032","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9952809,0.002187802,0.0006123969,0.0008899426,0.00036262107,0.00011333012,0.0003072645,0.0000051691495,0.0002405425],"genre_scores_gemma":[0.98872393,0.0009114329,0.0100191105,0.00006719135,0.0002063751,3.4643014e-11,0.000053471373,0.0000045222087,0.000013962515],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99862987,0.00010337457,0.00050020794,0.00021415573,0.00037644967,0.00017593757],"domain_scores_gemma":[0.99884313,0.00016349986,0.00039287194,0.000082651764,0.00038248306,0.00013537616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004341899,0.00014096535,0.00025676095,0.00008950268,0.00007314493,0.0000827631,0.00008541213,0.000073633906,0.000014259748],"category_scores_gemma":[0.00010822094,0.000120963814,0.000042276042,0.00016123078,0.00017424105,0.00030496,0.000036082987,0.00016950798,0.0000036027952],"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":[0.0009500492,0.000043530774,0.2093266,0.000074728014,0.00034597478,0.00043772676,0.00106943,0.0022504046,0.009537437,0.000016027774,0.00010162538,0.7758465],"study_design_scores_gemma":[0.00088811637,0.00030119566,0.5695127,0.000550913,0.000059130543,0.0013033344,0.00011616143,0.419332,0.0030034594,0.004401575,0.00032628945,0.00020506584],"about_ca_topic_score_codex":0.0024528564,"about_ca_topic_score_gemma":0.0004296694,"teacher_disagreement_score":0.7756414,"about_ca_system_score_codex":0.000027481921,"about_ca_system_score_gemma":0.000060149785,"threshold_uncertainty_score":0.49327612},"labels":[],"label_agreement":null},{"id":"W2794891691","doi":"10.1080/01431161.2018.1452075","title":"Land cover 2.0","year":2018,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":401,"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; University of British Columbia; Canadian Forest Service","funders":"Canadian Space Agency; National Aeronautics and Space Administration","keywords":"Land cover; Remote sensing; Advanced very-high-resolution radiometer; Earth observation; Context (archaeology); Thematic Mapper; Land information system; Computer science; Cover (algebra); Earth system science; Thematic map; Satellite; Data science; Land use; Satellite imagery; Geography; Land management; Cartography; Geology; Engineering","score_opus":0.008421781294441356,"score_gpt":0.24543678342712186,"score_spread":0.2370150021326805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794891691","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91939884,0.000017661549,0.028832292,0.0019554894,0.0029404329,0.00003731748,9.3976087e-7,0.000016600616,0.046800435],"genre_scores_gemma":[0.9047297,0.00001552715,0.09221204,0.0007351668,0.0015395815,3.723651e-10,8.475517e-7,0.000010288553,0.00075684907],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987923,0.00003634659,0.00027382336,0.00011050786,0.0006548542,0.0001321843],"domain_scores_gemma":[0.99932504,0.00003891691,0.0002784476,0.00009385885,0.0001894263,0.00007431527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023913918,0.00009074212,0.00011209536,0.00005313244,0.00004994731,0.00006580314,0.00024410247,0.00005293957,0.00036976192],"category_scores_gemma":[0.00015247137,0.00006658219,0.00008525023,0.00010368966,0.00014793196,0.00019844586,0.00009539485,0.00016428542,0.00057633326],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001412257,0.00002455979,0.0028594106,0.0000013178145,0.00012416647,0.0005559087,0.00076859817,0.0012630248,0.079923876,0.000008107224,0.03423301,0.8800968],"study_design_scores_gemma":[0.0018278119,0.00037104776,0.053039543,0.0003802809,0.00006677617,0.014446193,0.00011115085,0.07639305,0.05902618,0.004408848,0.7893702,0.00055893115],"about_ca_topic_score_codex":0.0001274947,"about_ca_topic_score_gemma":0.000052271527,"teacher_disagreement_score":0.8795379,"about_ca_system_score_codex":0.00019610414,"about_ca_system_score_gemma":0.000012909714,"threshold_uncertainty_score":0.74077904},"labels":[],"label_agreement":null},{"id":"W2796084042","doi":"10.1080/01431161.2018.1460506","title":"A novel algorithm of cloud detection for water quality studies using 250 m downscaled MODIS imagery","year":2018,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Water Quality Monitoring and Analysis","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Haze; Moderate-resolution imaging spectroradiometer; Remote sensing; Environmental science; Cloud computing; Algorithm; Masking (illustration); Cloud cover; Image resolution; Computer science; Satellite; Meteorology; Artificial intelligence; Geology; Geography","score_opus":0.07141682989205046,"score_gpt":0.36331685806063435,"score_spread":0.2919000281685839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2796084042","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58749473,0.00001187586,0.4112825,0.00017730363,0.0009842573,0.0000255655,0.0000043175896,0.000004350705,0.000015107887],"genre_scores_gemma":[0.81750405,0.000007489549,0.18123825,0.000028560507,0.0011636206,1.542449e-8,9.824228e-7,0.000008422743,0.000048606227],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984759,0.000069693415,0.0006242491,0.00014343887,0.00053223723,0.00015448859],"domain_scores_gemma":[0.9988771,0.00009205746,0.0004299472,0.0000989876,0.00045440177,0.000047479174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011235556,0.000104769046,0.00025875756,0.00011252573,0.000100677615,0.00003564417,0.00015483101,0.000042663723,0.000010788175],"category_scores_gemma":[0.0001848205,0.00007627055,0.00022475941,0.0000777505,0.00019762281,0.00021667375,0.000098003744,0.000093303504,0.00000485336],"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":[0.00008422613,0.000024251482,0.000057915735,0.000006754649,0.0002549589,0.000004981013,0.0010496074,0.0006248431,0.84217083,4.986652e-7,0.000015196333,0.15570594],"study_design_scores_gemma":[0.00056132796,0.000078426434,0.00032569468,0.000115996714,0.00008734715,0.00015684713,0.00051992,0.06593902,0.93062437,0.0011746689,0.0002984275,0.00011794486],"about_ca_topic_score_codex":0.00077044236,"about_ca_topic_score_gemma":0.000017050803,"teacher_disagreement_score":0.23004426,"about_ca_system_score_codex":0.00026725358,"about_ca_system_score_gemma":0.000008813792,"threshold_uncertainty_score":0.31102228},"labels":[],"label_agreement":null},{"id":"W2796192399","doi":"10.1080/01431161.2018.1460502","title":"Change detection using remote sensing in a reef environment of the UAE during the extreme event of El Niño 2015–2016","year":2018,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Coral and Marine Ecosystems Studies","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Utah Agricultural Experiment Station","keywords":"Reef; Coral bleaching; Coral reef; Coral; Oceanography; Habitat; Symbiodinium; Sea surface temperature; Resilience of coral reefs; Ecology; Climate change; Geography; Environmental science; Biology; Geology; Symbiosis; Paleontology","score_opus":0.02780025072050011,"score_gpt":0.2569180632471934,"score_spread":0.2291178125266933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2796192399","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9895026,0.00009827896,0.008158817,0.00092601025,0.0009743065,0.00013296999,0.0000013795348,0.0000029457099,0.00020267822],"genre_scores_gemma":[0.9955395,0.000081891536,0.0037857452,0.000043667493,0.00044248826,8.9773335e-9,1.03168375e-7,0.000010752271,0.00009587382],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9983628,0.00011614763,0.00059107045,0.00013785329,0.000630356,0.00016178146],"domain_scores_gemma":[0.9988652,0.000052872027,0.0008079649,0.00017340716,0.00006999485,0.00003060128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005931725,0.00012072477,0.00019690781,0.00009756687,0.0001104286,0.000013118616,0.00022773088,0.000039003753,0.000021442564],"category_scores_gemma":[0.00009656222,0.000076026015,0.00014060501,0.00014534897,0.00018517063,0.00015812795,0.00041367777,0.00017266518,0.0000050528793],"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":[0.0001703122,0.00002257056,0.003990804,0.000013893438,0.00008871054,0.000034729925,0.0017967168,0.0004577245,0.40983897,8.833281e-7,0.000018858873,0.58356583],"study_design_scores_gemma":[0.0017460734,0.00025370714,0.566538,0.0026882417,0.000111366906,0.0026325465,0.0011531165,0.24184538,0.175256,0.0018411407,0.0055320864,0.00040236837],"about_ca_topic_score_codex":0.0033412082,"about_ca_topic_score_gemma":0.0015994785,"teacher_disagreement_score":0.58316344,"about_ca_system_score_codex":0.00048554325,"about_ca_system_score_gemma":0.000014102854,"threshold_uncertainty_score":0.505093},"labels":[],"label_agreement":null},{"id":"W2799600431","doi":"10.1080/01431161.2018.1468105","title":"Non-photosynthetic vegetation biomass estimation in semiarid Canadian mixed grasslands using ground hyperspectral data, Landsat 8 OLI, and Sentinel-2 images","year":2018,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multispectral image; Red edge; Hyperspectral imaging; Environmental science; Remote sensing; Biomass (ecology); Vegetation (pathology); Leaf area index; Multispectral pattern recognition; Enhanced vegetation index; Spectral bands; Canopy; Normalized Difference Vegetation Index; Vegetation Index; Ecology; Geology; Biology","score_opus":0.012986359566433457,"score_gpt":0.260564199175537,"score_spread":0.24757783960910357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799600431","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9895094,0.000029662406,0.007631153,0.0009968528,0.00082347315,0.00007288773,0.000004313511,0.00000774916,0.0009245021],"genre_scores_gemma":[0.93303096,0.00001666744,0.066492446,0.00010894454,0.00030157855,3.3422713e-9,0.000017668432,0.000014164699,0.00001757927],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858177,0.000058099304,0.00038665984,0.00024906738,0.0004856568,0.00023876032],"domain_scores_gemma":[0.9992188,0.000048964128,0.0003023976,0.00016585483,0.00012615851,0.00013782339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004843097,0.00015250493,0.00017503787,0.000267343,0.00009960085,0.00017619588,0.00027158944,0.000088229055,0.000013852025],"category_scores_gemma":[0.00014921764,0.00013138494,0.00004005385,0.00021350328,0.00015710526,0.0005710459,0.00010766288,0.0002081277,0.000014273706],"study_design_candidate":"bench_or_experimental","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":[0.00010673285,0.000038238504,0.014006431,0.000017613225,0.00014384188,0.0009634055,0.0019852791,0.0037512584,0.842156,0.0000031449958,0.001223192,0.13560486],"study_design_scores_gemma":[0.0007114576,0.000037908914,0.13042946,0.00033618198,0.0000448677,0.0038626143,0.00018034881,0.8405347,0.022711039,0.00046992055,0.0004478664,0.00023364084],"about_ca_topic_score_codex":0.030719975,"about_ca_topic_score_gemma":0.03709051,"teacher_disagreement_score":0.8367834,"about_ca_system_score_codex":0.0005790725,"about_ca_system_score_gemma":0.00006089042,"threshold_uncertainty_score":0.9804801},"labels":[],"label_agreement":null},{"id":"W2799750502","doi":"10.1080/01431161.2018.1468108","title":"Global DEMs to tackle RPC biases and the overfitting phenomenon in high-resolution satellite imagery","year":2018,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Satellite Image Processing and Photogrammetry","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":"University of Ottawa","funders":"Chinese Academy of Sciences","keywords":"Overfitting; Computer science; Subpixel rendering; Satellite; Algorithm; Pixel; Data mining; Real-time computing; Artificial intelligence; Artificial neural network","score_opus":0.012614532201274236,"score_gpt":0.264197081228265,"score_spread":0.2515825490269908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799750502","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90307504,0.0024293729,0.09079235,0.0006024018,0.0017136878,0.00006151332,0.000004145569,0.000034516703,0.0012869447],"genre_scores_gemma":[0.9550285,0.00034294653,0.04345011,0.0002960796,0.00085781043,2.2364729e-8,0.0000012333702,0.000014282676,0.000009065198],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989677,0.000050915092,0.00039897315,0.00010309282,0.00029671835,0.00018259526],"domain_scores_gemma":[0.9992053,0.0002300452,0.0001261254,0.00007579435,0.00029624507,0.00006650089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006084479,0.00011684984,0.0001735045,0.00019201114,0.000050409642,0.00019390993,0.00014763698,0.000042466596,0.000003844963],"category_scores_gemma":[0.00045204072,0.00009240536,0.00005095563,0.0002659064,0.0001012842,0.00019083206,0.000043188982,0.00015971504,0.0000058201235],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026602496,0.000007089995,0.00067061564,0.000011154394,0.0000775216,0.000074013726,0.0007549693,0.0030819478,0.005033405,0.000030512363,0.00010650488,0.9898862],"study_design_scores_gemma":[0.0058541466,0.00023278632,0.027487444,0.0040773624,0.00011889831,0.0036215098,0.0012641647,0.893775,0.029682485,0.011499337,0.021531895,0.00085501204],"about_ca_topic_score_codex":0.00030696357,"about_ca_topic_score_gemma":0.00008416223,"teacher_disagreement_score":0.98903126,"about_ca_system_score_codex":0.00016815582,"about_ca_system_score_gemma":0.000023590444,"threshold_uncertainty_score":0.37681812},"labels":[],"label_agreement":null},{"id":"W2800235907","doi":"10.1080/01431161.2018.1464101","title":"Enabling earth observations in support of global, coastal, ocean, and climate change research and monitoring","year":2018,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Geology and Paleoclimatology Research","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":"Fisheries and Oceans Canada","funders":"National Aeronautics and Space Administration","keywords":"Climate change; Environmental science; Earth (classical element); Earth observation; Global change; Remote sensing; Earth system science; Oceanography; Climatology; Environmental resource management; Geology; Satellite","score_opus":0.1342249924894976,"score_gpt":0.365366045569617,"score_spread":0.2311410530801194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800235907","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9971043,0.00037346236,0.000041116095,0.0014253925,0.00041981033,0.000045930316,0.000012851762,0.0000026322482,0.0005745015],"genre_scores_gemma":[0.9928977,0.0009964027,0.0057501704,0.000051336745,0.00029019816,3.3624994e-9,0.0000036127783,0.00000159234,0.000008969565],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99889225,0.00011422769,0.00029416996,0.000110446555,0.00035941022,0.00022947286],"domain_scores_gemma":[0.9987809,0.00025846506,0.00013211883,0.000053959557,0.00070064026,0.00007388059],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001300743,0.00005947773,0.00014440462,0.00023734264,0.00010122407,0.000040880677,0.00012783536,0.00006484068,0.000022140835],"category_scores_gemma":[0.000228114,0.000053172862,0.000018934694,0.00020205733,0.00037057526,0.00026109896,0.000047482325,0.00025021445,0.0000044425237],"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":[0.00011978855,0.000005224628,0.9761752,0.00000935994,0.000018539602,0.00015228614,0.00031001132,0.0000073233678,0.00008922014,0.000034673078,0.000011367134,0.023067007],"study_design_scores_gemma":[0.00035924485,0.0002510055,0.9930693,0.00015104089,0.000003911745,0.001110232,0.0003948949,0.0024753083,0.00034272674,0.0014563802,0.00033833494,0.00004760808],"about_ca_topic_score_codex":0.0005072915,"about_ca_topic_score_gemma":0.004038019,"teacher_disagreement_score":0.0230194,"about_ca_system_score_codex":0.0000027840904,"about_ca_system_score_gemma":0.000052924523,"threshold_uncertainty_score":0.22533095},"labels":[],"label_agreement":null},{"id":"W2809499189","doi":"10.1080/01431161.2018.1482024","title":"Comparison of seasonal surface temperature trend, spatial variability, and elevation dependency from satellite-derived products and numerical simulations over the Tibetan Plateau from 2003 to 2011","year":2018,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","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":"Queen's University","funders":"Technische Universität Berlin; China Scholarship Council; National Natural Science Foundation of China; European Space Agency","keywords":"Environmental science; Plateau (mathematics); Climatology; Elevation (ballistics); Satellite; Radiometer; Global warming; Climate change; Sea surface temperature; Surface air temperature; Mean radiant temperature; Air temperature; Atmospheric sciences; Meteorology; Precipitation; Geography; Remote sensing; Geology","score_opus":0.014420593043857836,"score_gpt":0.27336776079539277,"score_spread":0.2589471677515349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809499189","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98530877,0.00006327171,0.012890041,0.0010287132,0.0004634709,0.00012065946,0.000052618343,0.0000051724514,0.00006725849],"genre_scores_gemma":[0.9742335,0.000010839752,0.025226297,0.000099201185,0.00037479852,1.4520026e-8,0.00002837082,0.000009011372,0.00001797686],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99863535,0.00013497658,0.00039031974,0.0002055022,0.0005307693,0.0001030746],"domain_scores_gemma":[0.9990845,0.00023263844,0.0002740763,0.000113894064,0.00021904803,0.00007587876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002460268,0.00011131451,0.00017581518,0.00003701417,0.00008301374,0.000062378684,0.00012274497,0.000066910994,0.00019181115],"category_scores_gemma":[0.0002894431,0.000085061,0.00002475273,0.00011595952,0.00012818768,0.00022838621,0.000087021224,0.00017856844,0.000007763131],"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":[0.00028604714,0.00005524056,0.2755601,0.0000022045624,0.000111160916,0.000006931342,0.004107038,0.0018246468,0.60650903,0.000007809106,0.00042793003,0.11110186],"study_design_scores_gemma":[0.000579485,0.00012414197,0.83392245,0.00007526444,0.000059238144,0.000033084078,0.00011213394,0.09427815,0.06839422,0.0013871638,0.00089171005,0.00014298178],"about_ca_topic_score_codex":0.0017177153,"about_ca_topic_score_gemma":0.0007292895,"teacher_disagreement_score":0.5583623,"about_ca_system_score_codex":0.00010758734,"about_ca_system_score_gemma":0.000032305295,"threshold_uncertainty_score":0.34686872},"labels":[],"label_agreement":null},{"id":"W2813092426","doi":"10.1080/01431161.2018.1492176","title":"Aboveground forest biomass derived using multiple dates of WorldView-2 stereo-imagery: quantifying the improvement in estimation accuracy","year":2018,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","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":"Natural Resources Canada; Canadian Forest Service","funders":"Academy of Finland","keywords":"Environmental science; Satellite imagery; Panchromatic film; Remote sensing; Mean squared error; Percentile; Elevation (ballistics); Canopy; Forest inventory; Mathematics; Statistics; Geography; Forest management; Multispectral image","score_opus":0.041586480259857075,"score_gpt":0.32228042089983694,"score_spread":0.2806939406399799,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2813092426","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7854649,0.000027463264,0.21323924,0.00051708263,0.00037444854,0.00011019856,0.000001968711,0.000005738141,0.00025898733],"genre_scores_gemma":[0.881816,0.000017468477,0.117883295,0.00009915713,0.00015930801,1.5628773e-8,0.0000031347608,0.0000131378765,0.000008451282],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840957,0.00007295634,0.0006613173,0.00016207554,0.0005213904,0.00017269583],"domain_scores_gemma":[0.9984969,0.0002746219,0.0007982428,0.00020348543,0.00018113073,0.000045642584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063886977,0.00012665227,0.0001795776,0.00015848404,0.00011343857,0.000099483506,0.00032198365,0.000040529667,0.00002014722],"category_scores_gemma":[0.00036395967,0.00009576612,0.00009207705,0.00026732555,0.00024946098,0.0003620357,0.00013567493,0.00016226957,0.000012666428],"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":[0.00006056942,0.000028846747,0.0028194217,0.0000051430998,0.00004249623,0.000013694642,0.0007154286,0.003085372,0.45153043,0.000007758725,0.00003423275,0.5416566],"study_design_scores_gemma":[0.0006451435,0.00006179244,0.02379337,0.0003667966,0.00003066166,0.00025757373,0.00040207172,0.8860912,0.08608978,0.001183013,0.00093396235,0.00014466095],"about_ca_topic_score_codex":0.0022610165,"about_ca_topic_score_gemma":0.0013150254,"teacher_disagreement_score":0.8830058,"about_ca_system_score_codex":0.00026139777,"about_ca_system_score_gemma":0.000036492893,"threshold_uncertainty_score":0.3905229},"labels":[],"label_agreement":null},{"id":"W2888289639","doi":"10.1080/01431161.2018.1512768","title":"Full-polarimetric burn scar mapping – the differences of active fire and post-fire situations","year":2018,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":7,"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":"Indian Space Research Organisation; Japan Aerospace Exploration Agency; National Aeronautics and Space Administration","keywords":"Remote sensing; Synthetic aperture radar; Environmental science; Polarimetry; Backscatter (email); Satellite; Smoke; Computer science; Meteorology; Geology; Scattering; Geography; Engineering","score_opus":0.015348287130512709,"score_gpt":0.24246215241452923,"score_spread":0.22711386528401653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888289639","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6174445,0.00042506593,0.38005844,0.0011111003,0.00024669568,0.000056347533,0.0000067954074,0.000023697294,0.00062734936],"genre_scores_gemma":[0.8033236,0.00021004977,0.19613545,0.000041228745,0.0002690957,2.2246708e-8,0.0000011387833,0.000009493038,0.000009877575],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925816,0.000022115346,0.00029058717,0.00007135289,0.000273255,0.00008451769],"domain_scores_gemma":[0.99884486,0.00020775384,0.00018900103,0.00010235472,0.00062228757,0.000033755972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001599978,0.00009200291,0.00014066207,0.00018019833,0.000078070414,0.000042026935,0.000191151,0.000051270734,0.000011060402],"category_scores_gemma":[0.00014135346,0.00006560198,0.00006645921,0.00018503281,0.000113337854,0.00010483235,0.000036483172,0.00016219735,0.0000014025903],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012176949,0.0000058126957,0.000035021494,0.0000037887419,0.00012606764,0.0000032043579,0.00065553846,0.000002523806,0.01029448,0.000078114586,0.00005754607,0.9887257],"study_design_scores_gemma":[0.0013101416,0.0006691976,0.088673286,0.0019803955,0.00034188532,0.0054923296,0.006671275,0.63879657,0.13897175,0.018860498,0.09733832,0.00089437206],"about_ca_topic_score_codex":0.00011175861,"about_ca_topic_score_gemma":0.0000139496815,"teacher_disagreement_score":0.98783135,"about_ca_system_score_codex":0.000055137127,"about_ca_system_score_gemma":0.00002534154,"threshold_uncertainty_score":0.2675171},"labels":[],"label_agreement":null},{"id":"W2903748728","doi":"10.1080/01431161.2017.1381351","title":"Multiple kernel representation and classification of multivariate satellite-image time-series for crop mapping","year":2017,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"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 Ottawa","funders":"University of Tehran","keywords":"Kernel (algebra); Multiple kernel learning; Computer science; Pattern recognition (psychology); Contextual image classification; Artificial intelligence; Relevance (law); Multivariate statistics; Basis (linear algebra); Data mining; Kernel method; Representation (politics); Mathematics; Machine learning; Support vector machine; Image (mathematics)","score_opus":0.026676729352548965,"score_gpt":0.29155097952498993,"score_spread":0.264874250172441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903748728","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90520984,0.000030118843,0.08885676,0.0022386892,0.00079821673,0.00019040241,0.0000064219244,0.000011952314,0.0026576219],"genre_scores_gemma":[0.68992466,0.000055442193,0.30942976,0.000023897062,0.00018736756,1.2001801e-8,0.000005575511,0.000011066204,0.00036222444],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99878997,0.000047638143,0.00045432552,0.00018123334,0.0004080981,0.000118712705],"domain_scores_gemma":[0.99811697,0.00013501696,0.0011994815,0.00019794592,0.000295018,0.00005558235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039124026,0.000112266884,0.00018931994,0.000066714005,0.00016304369,0.00017576304,0.00026262816,0.00006926077,0.000009598792],"category_scores_gemma":[0.0011378373,0.00009372794,0.00010133263,0.000038366143,0.00022459397,0.00066422985,0.00011569392,0.00011775499,0.000010296928],"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":[0.00012592123,0.000011640773,0.0012980325,0.000006343499,0.000050543655,0.000019815658,0.00062080065,0.00020513605,0.7261479,0.000010230229,0.0001583518,0.2713453],"study_design_scores_gemma":[0.0019056088,0.0000983005,0.61983913,0.00044811357,0.00005691508,0.0009811617,0.0004604267,0.24131319,0.121069215,0.002684969,0.010851125,0.00029184888],"about_ca_topic_score_codex":0.00019495856,"about_ca_topic_score_gemma":0.000048826598,"teacher_disagreement_score":0.6185411,"about_ca_system_score_codex":0.000116141644,"about_ca_system_score_gemma":0.000011862062,"threshold_uncertainty_score":0.38221145},"labels":[],"label_agreement":null},{"id":"W2913691145","doi":"10.1080/01431161.2019.1579387","title":"Modelling spatial distribution of fine-scale populations based on residential properties","year":2019,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Impact of Light on Environment and Health","field":"Environmental Science","cited_by":28,"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":"National Key Research and Development Program of China; Chinese Academy of Sciences Key Project; National Natural Science Foundation of China","keywords":"Spatialization; Beijing; Population; Scale (ratio); Environmental science; Resource (disambiguation); Spatial ecology; Population model; Computer science; Occupancy; Statistics; Physical geography; Geography; Cartography; Mathematics; Ecology; Civil engineering; Engineering; China","score_opus":0.028184063487836492,"score_gpt":0.2607372162508361,"score_spread":0.23255315276299962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913691145","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.786011,0.0000066356365,0.21162853,0.0010968029,0.0005601474,0.000059688577,0.0000043864043,0.0000037870093,0.0006290205],"genre_scores_gemma":[0.984919,0.00000625965,0.014706016,0.00007715189,0.00017070018,7.47728e-9,0.0000107335145,0.000008222948,0.00010187558],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985872,0.00004224386,0.0003896626,0.000094725656,0.0007525558,0.00013362948],"domain_scores_gemma":[0.9994049,0.000021166914,0.00037472835,0.000093716364,0.000044335215,0.00006112942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002976932,0.0000812184,0.00013327191,0.00007010152,0.000042837764,0.000021666589,0.00013759403,0.00004137972,0.00021282537],"category_scores_gemma":[0.000030588326,0.00006798069,0.00009387946,0.000047899568,0.000056389268,0.00018765061,0.000033448196,0.0001387466,0.000043094635],"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":[0.0005222451,0.00007728048,0.014402446,0.000008810848,0.000020476391,0.0000146457505,0.00025501443,0.9225187,0.01907286,0.000017871549,0.00016179887,0.042927857],"study_design_scores_gemma":[0.0006253378,0.00017225633,0.04288555,0.00020946043,0.000015103203,0.000029033477,0.000029287858,0.9444183,0.010415112,0.00035524537,0.0007516199,0.000093700546],"about_ca_topic_score_codex":0.0008242145,"about_ca_topic_score_gemma":0.00006305058,"teacher_disagreement_score":0.19890803,"about_ca_system_score_codex":0.00028514388,"about_ca_system_score_gemma":0.000027685399,"threshold_uncertainty_score":0.27721724},"labels":[],"label_agreement":null},{"id":"W2914595906","doi":"10.1080/01431161.2019.1579390","title":"Near real-time deforestation detection in Malaysia and Indonesia using change vector analysis with three sensors","year":2019,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Conservation, Biodiversity, and Resource Management","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Deforestation (computer science); Remote sensing; Environmental science; Satellite imagery; Cloud cover; Climate change; Clearing; Cloud computing; Physical geography; Geography; Computer science; Geology","score_opus":0.012390664823303527,"score_gpt":0.2146487212507502,"score_spread":0.20225805642744668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914595906","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9956514,0.000005413873,0.0037985493,0.00023143011,0.00008892629,0.00009568858,8.671811e-7,0.0000053895938,0.00012232708],"genre_scores_gemma":[0.9914761,0.000016764696,0.008391659,0.000043337834,0.000046577912,1.0600794e-8,0.0000019991232,0.000005319735,0.000018217685],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991563,0.0000398426,0.00018934955,0.00012639082,0.00039374316,0.00009438266],"domain_scores_gemma":[0.9995593,0.000026416534,0.00025369122,0.00006404739,0.000059040227,0.00003754076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022895016,0.000078571946,0.00012766842,0.00026053993,0.00004999917,0.00006473136,0.00007648797,0.00003289696,0.000044214],"category_scores_gemma":[0.00000924911,0.00006704109,0.000047701196,0.00032634253,0.000050346625,0.0002357841,0.00006177086,0.00008058,0.00001187452],"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":[0.00015278948,0.000010517985,0.90634185,0.0000037110995,0.00016221861,0.00008160743,0.0008717935,0.038374227,0.007629518,3.4915e-7,7.155772e-7,0.046370722],"study_design_scores_gemma":[0.00027493967,0.000030652318,0.6118952,0.000027645432,0.000066113884,0.00006158204,0.000114406714,0.38729587,0.0001332578,0.000014243777,0.00003477506,0.000051317948],"about_ca_topic_score_codex":0.0028632388,"about_ca_topic_score_gemma":0.0012208904,"teacher_disagreement_score":0.34892166,"about_ca_system_score_codex":0.00025152945,"about_ca_system_score_gemma":0.0000075447397,"threshold_uncertainty_score":0.43283796},"labels":[],"label_agreement":null},{"id":"W2923244310","doi":"10.1080/01431161.2019.1597294","title":"Streambank topography: an accuracy assessment of UAV-based and traditional 3D reconstructions","year":2019,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":35,"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":"","keywords":"Riparian zone; Photogrammetry; Orthophoto; Transect; Environmental science; Elevation (ballistics); Hydrology (agriculture); Remote sensing; Digital elevation model; Vegetation (pathology); Erosion; Geology; Geomorphology; Ecology","score_opus":0.017339558594058946,"score_gpt":0.2846283072048813,"score_spread":0.26728874861082236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2923244310","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9637672,0.00000968943,0.029031893,0.0006550608,0.0004824723,0.00007455553,0.000010926467,0.000009177136,0.0059590735],"genre_scores_gemma":[0.77012336,0.0000146341745,0.22963253,0.00009399406,0.000098771,7.472783e-9,0.000008104922,0.000007340767,0.0000212304],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878436,0.000057601865,0.0003919121,0.00015437933,0.0005113597,0.00010038922],"domain_scores_gemma":[0.99907494,0.00013057748,0.0004297243,0.00014576357,0.00012952075,0.000089504974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027212946,0.00009640805,0.00015601897,0.00015904696,0.000055494813,0.000046669673,0.0001544179,0.000046955753,0.00021006394],"category_scores_gemma":[0.000039610804,0.00009034811,0.000098105476,0.00012390775,0.0001496344,0.0002801818,0.000026728421,0.00018825036,0.0000058163014],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039758866,0.0000852229,0.0077917855,0.0000051404113,0.00009246539,0.00001602345,0.00014733197,0.0069495537,0.05041732,0.00021821911,0.000104420134,0.93413275],"study_design_scores_gemma":[0.0024450573,0.000536652,0.37423357,0.00039724863,0.000119975106,0.0029187028,0.0006653617,0.588035,0.010435508,0.00887917,0.010842619,0.0004911404],"about_ca_topic_score_codex":0.00027551636,"about_ca_topic_score_gemma":0.000038581853,"teacher_disagreement_score":0.9336416,"about_ca_system_score_codex":0.000105904524,"about_ca_system_score_gemma":0.00006170176,"threshold_uncertainty_score":0.3684289},"labels":[],"label_agreement":null},{"id":"W2924060730","doi":"10.1080/01431161.2019.1594436","title":"Crop biomass estimation using multi regression analysis and neural networks from multitemporal L-band polarimetric synthetic aperture radar data","year":2019,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Agriculture and Agri-Food Canada; Carleton University; Institut National de la Recherche Scientifique","funders":"National Aeronautics and Space Administration","keywords":"Synthetic aperture radar; Remote sensing; Environmental science; Biomass (ecology); Canola; Polarimetry; Radar; Mean squared error; Correlation coefficient; Computer science; Mathematics; Machine learning; Agronomy; Geography; Statistics","score_opus":0.019434282151760466,"score_gpt":0.2806536742209527,"score_spread":0.26121939206919226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2924060730","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27055964,0.0010997336,0.72765166,0.00015482027,0.0003696759,0.000073686155,0.000027046493,0.000044337045,0.000019378811],"genre_scores_gemma":[0.52813953,0.000057570047,0.47159982,0.000030227126,0.0001026932,6.5661805e-9,0.000049402603,0.000017567489,0.0000031758818],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987386,0.000048475096,0.0004728391,0.00022848227,0.00037035445,0.00014128497],"domain_scores_gemma":[0.99878,0.00027455974,0.00030000982,0.00036434748,0.00020276147,0.000078285804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025973225,0.00018789242,0.0003209907,0.0006163657,0.000054547272,0.00013221405,0.00034646157,0.0001360094,0.00001454818],"category_scores_gemma":[0.0001309477,0.00015487632,0.00010907279,0.00042401123,0.000039037448,0.00040275053,0.00008438938,0.00027550012,0.0000012203575],"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":[0.000037184313,0.000018159635,0.0017596461,0.000009697166,0.00076887204,0.00004510481,0.00006630627,0.0029403195,0.02062202,0.0000029999096,0.00003056679,0.97369915],"study_design_scores_gemma":[0.0003268952,0.000011162367,0.002413307,0.00016937654,0.0002451179,0.00023082097,0.000024870731,0.98974234,0.003541568,0.000045058037,0.0030905765,0.00015887959],"about_ca_topic_score_codex":0.00067341974,"about_ca_topic_score_gemma":0.000021040596,"teacher_disagreement_score":0.98680204,"about_ca_system_score_codex":0.000113192626,"about_ca_system_score_gemma":0.000017041139,"threshold_uncertainty_score":0.63156736},"labels":[],"label_agreement":null},{"id":"W2940368062","doi":"10.1080/01431161.2019.1602792","title":"Unsupervised change detection of VHR remote sensing images based on multi-resolution Markov Random Field in wavelet domain","year":2019,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Wavelet; Change detection; Markov random field; Artificial intelligence; Pattern recognition (psychology); Computer science; Wavelet transform; Scale (ratio); Robustness (evolution); Feature (linguistics); Remote sensing; Computer vision; Image (mathematics); Geography; Image segmentation","score_opus":0.01769889526432237,"score_gpt":0.25048266273721864,"score_spread":0.23278376747289628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2940368062","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42909408,0.00008739939,0.56803274,0.0005568495,0.0016209476,0.00019919718,0.000002993389,0.000040508825,0.0003652815],"genre_scores_gemma":[0.82600796,0.00006492473,0.17345019,0.00014988969,0.00025740944,9.021425e-9,0.000006283362,0.00004502347,0.000018340927],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978525,0.00018259932,0.00083744543,0.00021569931,0.0006600694,0.00025166475],"domain_scores_gemma":[0.9983084,0.00038304157,0.00042398335,0.00026389485,0.0005602477,0.000060408787],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00081241515,0.00023892411,0.0004007041,0.00090993725,0.00003162832,0.00006617521,0.00017860661,0.00017100501,0.000008380766],"category_scores_gemma":[0.000424729,0.00024605717,0.00020355744,0.00029463915,0.00003809249,0.00030647506,0.000025289391,0.000505777,0.000011572674],"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":[0.00051327585,0.0000133441235,0.000022861297,0.0000385263,0.00005084756,0.000103037324,0.00023263176,0.013375408,0.38933852,4.5359465e-7,0.000015501382,0.5962956],"study_design_scores_gemma":[0.0032664991,0.00008444795,0.0024554268,0.0010920615,0.000017854216,0.0002168281,0.00009354782,0.86237925,0.12984808,0.000076104756,0.0002851547,0.0001847221],"about_ca_topic_score_codex":0.0002232369,"about_ca_topic_score_gemma":0.00007054586,"teacher_disagreement_score":0.84900385,"about_ca_system_score_codex":0.00048049752,"about_ca_system_score_gemma":0.000041246672,"threshold_uncertainty_score":0.99999917},"labels":[],"label_agreement":null},{"id":"W2971351124","doi":"10.1080/01431161.2019.1658238","title":"Subglacial controls on dynamic thinning at Trinity-Wykeham Glacier, Prince of Wales Ice Field, Canadian Arctic","year":2019,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":7,"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":"Natural Environment Research Council; Sight Research UK; California Institute of Technology; National Aeronautics and Space Administration","keywords":"Glacier; Geology; Glacier ice accumulation; Glacier morphology; Thinning; Arctic; Bedrock; Meltwater; Glacier terminus; Accumulation zone; Ice stream; Elevation (ballistics); Glacier mass balance; Physical geography; Tidewater glacier cycle; Surge; Rock glacier; Geomorphology; Oceanography; Climatology; Cryosphere; Sea ice; Geography; Ice calving","score_opus":0.011821353721934337,"score_gpt":0.23796337658276728,"score_spread":0.22614202286083296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2971351124","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.993496,0.00021856272,0.00052714173,0.0019015745,0.0018285129,0.00007183918,0.00001386332,0.0000043418127,0.0019381804],"genre_scores_gemma":[0.99526787,0.00009257796,0.0034302592,0.00074871496,0.00018450417,2.9750415e-9,0.000008380088,0.0000032317698,0.00026444322],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9989045,0.000039971055,0.0003605815,0.00010477646,0.00042303643,0.00016708122],"domain_scores_gemma":[0.99864274,0.00044396924,0.0003360244,0.00008566329,0.00039453607,0.00009704791],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028009826,0.000092209906,0.00019775264,0.00010813551,0.00009333282,0.000037592992,0.00019911962,0.000045186298,0.00025941705],"category_scores_gemma":[0.00031112102,0.00007804559,0.00011063782,0.0000970513,0.000032390093,0.00011860716,0.000014039706,0.0001954125,0.000032135653],"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":[0.0007738336,0.000017769406,0.46713433,0.000026332826,0.0003813574,0.00028957182,0.0012089658,0.014143153,0.0009240567,0.00009444408,0.0002345117,0.5147717],"study_design_scores_gemma":[0.001290313,0.0003724562,0.9084,0.00039975482,0.00003867845,0.000375332,0.0005716534,0.067966096,0.00027852898,0.0005815988,0.01952021,0.00020538647],"about_ca_topic_score_codex":0.03424999,"about_ca_topic_score_gemma":0.2731558,"teacher_disagreement_score":0.5145663,"about_ca_system_score_codex":0.00008972854,"about_ca_system_score_gemma":0.00014796815,"threshold_uncertainty_score":0.972181},"labels":[],"label_agreement":null},{"id":"W2988349362","doi":"10.1080/01431161.2019.1685715","title":"Impact of temporal variations in vegetation optical depth and vegetation temperature on L-band passive soil moisture retrievals over a tropical forest using <i>in-situ</i> information","year":2019,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Environmental science; Vegetation (pathology); Water content; Radiometer; Remote sensing; Precipitation; In situ; Biosphere; Atmospheric sciences; Brightness temperature; Brightness; Meteorology; Geology; Geography","score_opus":0.00665196512106291,"score_gpt":0.2577822058978346,"score_spread":0.2511302407767717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2988349362","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99451685,0.0000286709,0.0023076865,0.00020028064,0.00039408688,0.00013879141,5.888222e-7,0.0000039851748,0.002409079],"genre_scores_gemma":[0.9930894,0.00001829145,0.0066393497,0.000119356395,0.000112552225,9.732028e-9,0.0000073343012,0.000009738348,0.000003962718],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99838585,0.000079304205,0.0006151054,0.00013851756,0.00062772515,0.00015352218],"domain_scores_gemma":[0.9990643,0.00013327361,0.00050924445,0.000089473084,0.00014011783,0.00006354375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002322624,0.00014755808,0.00025398468,0.00031923314,0.000028176795,0.0000746116,0.00009104661,0.0001525335,0.0000022588679],"category_scores_gemma":[0.00021552734,0.00011952748,0.00011155858,0.00025703042,0.00006137979,0.00073732086,0.0000312233,0.00037745203,0.0000040240957],"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":[0.0007261761,0.00008551629,0.6139298,0.000027973374,0.00010455833,0.0001108203,0.0022194055,0.125693,0.14546394,0.000060343562,0.000020725522,0.11155777],"study_design_scores_gemma":[0.001415404,0.00016447823,0.9447776,0.00045058283,0.000017764882,0.00023638137,0.000074141404,0.049568765,0.0026137019,0.0005589387,0.000011524388,0.00011073863],"about_ca_topic_score_codex":0.00061600126,"about_ca_topic_score_gemma":0.0033478935,"teacher_disagreement_score":0.3308478,"about_ca_system_score_codex":0.00054295163,"about_ca_system_score_gemma":0.00007317186,"threshold_uncertainty_score":0.48741892},"labels":[],"label_agreement":null},{"id":"W2991061114","doi":"10.1080/01431161.2019.1694722","title":"Effect of ground surface interpolation methods on the accuracy of forest attribute modelling using unmanned aerial systems-based digital aerial photogrammetry","year":2019,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":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":"Photogrammetry; Point cloud; Terrain; Remote sensing; Forest inventory; Triangulated irregular network; Interpolation (computer graphics); Digital elevation model; Environmental science; Multivariate interpolation; Lidar; Geography; Computer science; Cartography; Forestry; Forest management; Artificial intelligence; Computer vision; Bilinear interpolation","score_opus":0.024831792882994686,"score_gpt":0.3163359822119241,"score_spread":0.2915041893289294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991061114","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62346905,0.000009606557,0.3751464,0.00006786054,0.00089625025,0.00018411856,0.0000072024272,0.0000056398458,0.00021387244],"genre_scores_gemma":[0.9608882,0.000003565957,0.038841445,0.000020766836,0.00020456011,1.2829708e-8,0.000008170072,0.000021232006,0.000012058851],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978461,0.00034389654,0.0007342614,0.00018148172,0.0007237173,0.00017049501],"domain_scores_gemma":[0.9966649,0.0015740481,0.001228405,0.0002447992,0.00023017934,0.000057678993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014962374,0.00017531682,0.00035453084,0.00012155991,0.00006347216,0.0001268523,0.0003264964,0.00008637367,0.000017575627],"category_scores_gemma":[0.00045560833,0.00012695653,0.00022498732,0.0002605026,0.00012489638,0.00025160384,0.00007852873,0.00023833748,0.000008911488],"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":[0.0007830811,0.000029258877,0.0006512144,0.00001919219,0.000110825385,0.0000041070584,0.00015922784,0.78569573,0.16864638,0.00001962318,0.000015039793,0.043866295],"study_design_scores_gemma":[0.00075654,0.00025710269,0.00012786381,0.00039001112,0.00004635573,0.0000818668,0.0000920951,0.93375665,0.06400752,0.000111275556,0.0002656521,0.00010706494],"about_ca_topic_score_codex":0.0013625034,"about_ca_topic_score_gemma":0.000008711608,"teacher_disagreement_score":0.33741912,"about_ca_system_score_codex":0.00023387466,"about_ca_system_score_gemma":0.000047645215,"threshold_uncertainty_score":0.51771367},"labels":[],"label_agreement":null},{"id":"W3001140498","doi":"10.1080/01431161.2019.1708508","title":"Analysis of surfactant-associated bacteria in the sea surface microlayer using deoxyribonucleic acid sequencing and synthetic aperture radar","year":2020,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Methane Hydrates and Related Phenomena","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bedford Institute of Oceanography","funders":"","keywords":"Racing slick; Synthetic aperture radar; Biogeochemical cycle; Pulmonary surfactant; Environmental science; Surface tension; Atmosphere (unit); Satellite; Remote sensing; Geology; Oceanography; Meteorology; Chemistry; Environmental chemistry; Geography; Physics","score_opus":0.01955792652078695,"score_gpt":0.24052682074616585,"score_spread":0.2209688942253789,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3001140498","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9966927,0.00015896907,0.0015514051,0.0013057287,0.000111400244,0.000047007423,0.0000081190865,0.0000031115922,0.000121551195],"genre_scores_gemma":[0.98959947,0.00006782208,0.009836227,0.0004555676,0.000025025674,1.8152074e-9,0.0000032914907,0.000009835421,0.0000027640774],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854267,0.00023398112,0.0004693858,0.00014426805,0.00046679622,0.00014290171],"domain_scores_gemma":[0.99913067,0.00018008502,0.0004847918,0.00007634435,0.00006142645,0.00006667359],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070738705,0.000116082905,0.00029256073,0.000094349496,0.00004295858,0.00005025594,0.0002326711,0.00007056667,0.00008894956],"category_scores_gemma":[0.00021658858,0.0000812104,0.00013045053,0.0004979281,0.000073284406,0.00014983675,0.00007543811,0.0002858458,0.0000020912012],"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":[0.00007764589,0.000013595667,0.01094408,0.000003963526,0.00061620446,0.00016833667,0.0031396425,0.028831646,0.95217705,9.268677e-7,0.00000784268,0.004019078],"study_design_scores_gemma":[0.00066718453,0.000076277945,0.021088516,0.00019979787,0.00056047266,0.000361399,0.0010661945,0.95913523,0.016333653,0.00009766987,0.00020389263,0.00020969384],"about_ca_topic_score_codex":0.00043565762,"about_ca_topic_score_gemma":0.00006460995,"teacher_disagreement_score":0.9358434,"about_ca_system_score_codex":0.0002060465,"about_ca_system_score_gemma":0.000024640436,"threshold_uncertainty_score":0.3311664},"labels":[],"label_agreement":null},{"id":"W3030505590","doi":"10.1080/01431161.2020.1766293","title":"Preface: Interdisciplinary multi-sensor studies of the Pacific and Indian Oceans","year":2020,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Marine and coastal ecosystems","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":"Fisheries and Oceans Canada","funders":"","keywords":"Oceanography; Synthetic aperture radar; Environmental science; Remote sensing; Geology","score_opus":0.0333547179582712,"score_gpt":0.27387097306510627,"score_spread":0.24051625510683508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3030505590","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99211055,0.0005427135,0.0005589018,0.0049971007,0.00087428204,0.00005137851,0.000017071046,0.0000041758067,0.0008438369],"genre_scores_gemma":[0.9963076,0.00013048919,0.0031062977,0.00015152435,0.00023762982,8.8105967e-10,0.0000013089393,0.000002280411,0.00006288701],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991823,0.000057220826,0.0003201328,0.00008070423,0.00028335507,0.00007631719],"domain_scores_gemma":[0.9992701,0.00008028092,0.00031360795,0.00005003229,0.00022309975,0.00006288244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016868586,0.00007239797,0.0001580859,0.000055730165,0.00005379724,0.000032488686,0.00017190166,0.000023357472,0.00001922159],"category_scores_gemma":[0.00013110648,0.00004483344,0.000076890625,0.000074362666,0.000072193056,0.00011723843,0.00008887687,0.00015015628,0.0000030375866],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020947715,0.000009216823,0.058117647,0.00006438108,0.0003890514,0.00027221744,0.011240473,0.0008945058,0.00091339805,0.0000035713572,0.0004935187,0.92739254],"study_design_scores_gemma":[0.003951107,0.0012669116,0.39119887,0.002564811,0.00021058902,0.0075926417,0.099818625,0.46570587,0.005599297,0.0022739612,0.019036949,0.000780359],"about_ca_topic_score_codex":0.00008989368,"about_ca_topic_score_gemma":0.00042421627,"teacher_disagreement_score":0.9266122,"about_ca_system_score_codex":0.000005367143,"about_ca_system_score_gemma":0.00003099855,"threshold_uncertainty_score":0.18282546},"labels":[],"label_agreement":null},{"id":"W3036270649","doi":"10.1080/01431161.2020.1750732","title":"Geometrical constrained independent component analysis for hyperspectral unmixing","year":2020,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Endmember; Hyperspectral imaging; Independent component analysis; Pixel; Computer science; Imaging spectrometer; Blind signal separation; Pattern recognition (psychology); Remote sensing; Artificial intelligence; Principal component analysis; Data set; Set (abstract data type); Spectrometer; Geography","score_opus":0.032210628721360504,"score_gpt":0.2686846749666232,"score_spread":0.2364740462452627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036270649","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19054724,0.00009246173,0.80559886,0.0025565291,0.00074489746,0.00007253434,0.000007325515,0.000059013346,0.00032115693],"genre_scores_gemma":[0.77140373,0.00002729962,0.2277996,0.0001586541,0.0005730962,8.34675e-9,0.000010417701,0.000022044258,0.0000051756883],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984481,0.00003088465,0.00060792745,0.0001511905,0.00057548087,0.00018646287],"domain_scores_gemma":[0.99862355,0.00019217617,0.00025350117,0.000092703514,0.0006750801,0.00016296303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002706621,0.00014801622,0.00033061218,0.0006463657,0.00003620865,0.00012968775,0.00022495836,0.00007168829,0.0000095905825],"category_scores_gemma":[0.00047208625,0.00015057554,0.00035646962,0.0005343577,0.000043204764,0.00015949763,0.000023251594,0.00028132155,0.0000055053642],"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":[0.00023929281,0.000028491855,0.00018954373,0.000033819942,0.004283452,0.0004108621,0.001050812,0.22214091,0.36236253,0.00010166141,0.0005320021,0.40862662],"study_design_scores_gemma":[0.00076661253,0.000047948095,0.0014504456,0.000038209022,0.000253821,0.00030414818,0.00019527013,0.97788304,0.01716386,0.00013088035,0.0016086976,0.00015705865],"about_ca_topic_score_codex":0.000007449152,"about_ca_topic_score_gemma":0.000002684927,"teacher_disagreement_score":0.75574213,"about_ca_system_score_codex":0.00030247704,"about_ca_system_score_gemma":0.00004366394,"threshold_uncertainty_score":0.61402917},"labels":[],"label_agreement":null},{"id":"W3040364706","doi":"10.1080/01431161.2020.1754494","title":"Synthetic Aperture Radar (SAR) image processing for operational space-based agriculture mapping","year":2020,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","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; Agriculture and Agri-Food Canada","funders":"Canadian Space Agency","keywords":"Computer science; Synthetic aperture radar; Remote sensing; Filter (signal processing); Terrain; Speckle noise; Speckle pattern; Artificial intelligence; Computer vision; Geography; Cartography","score_opus":0.011925236699553856,"score_gpt":0.230864138380482,"score_spread":0.21893890168092814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3040364706","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05962376,0.00016976803,0.85266685,0.08337732,0.00093588606,0.0003741669,0.00001957081,0.00006795806,0.0027647337],"genre_scores_gemma":[0.35430723,0.000011145725,0.6396683,0.004654841,0.0011979043,1.9557671e-8,0.00002221812,0.000029604687,0.00010874758],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99782103,0.0000679524,0.0005383992,0.000319815,0.0009992396,0.00025356506],"domain_scores_gemma":[0.99858516,0.00016066202,0.000543822,0.00009581218,0.0004202055,0.00019432176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002834235,0.0002468918,0.00028142185,0.00006770415,0.00016960305,0.00026488426,0.00040434906,0.00013089551,0.000068719535],"category_scores_gemma":[0.0007622643,0.00017839235,0.00025417173,0.00024645723,0.00011225166,0.00043103052,0.000079773046,0.0003933332,0.00003754536],"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":[0.00019034784,0.000038153034,0.000057482648,0.000033373457,0.00009647293,0.00026097635,0.0011584217,0.0094464915,0.78538316,0.000012471507,0.01732928,0.18599334],"study_design_scores_gemma":[0.002096153,0.00018655155,0.0011427626,0.0008221837,0.000086768036,0.0020239952,0.0007396161,0.56878483,0.050971787,0.00039619842,0.3720905,0.00065869186],"about_ca_topic_score_codex":0.000022297823,"about_ca_topic_score_gemma":0.000007712989,"teacher_disagreement_score":0.7344114,"about_ca_system_score_codex":0.00033424085,"about_ca_system_score_gemma":0.00007584085,"threshold_uncertainty_score":0.7274629},"labels":[],"label_agreement":null},{"id":"W3046216337","doi":"10.1080/01431161.2020.1792577","title":"Interpretation and use of geomorphometry in remote sensing: a guide and review of integrated applications","year":2020,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trent University","funders":"","keywords":"Remote sensing; Computer science; Field (mathematics); Variable (mathematics); Digital elevation model; Terrain; Data mining; Data science; Geology; Geography; Cartography","score_opus":0.020004332661571103,"score_gpt":0.2762815920848037,"score_spread":0.2562772594232326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046216337","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30553976,0.0011937879,0.69079125,0.0018979297,0.0000998678,0.00015826512,0.000015269925,0.0000041303665,0.00029973194],"genre_scores_gemma":[0.63159823,0.0056831236,0.3614094,0.0012310728,0.000041863997,7.685801e-9,0.000009116918,0.0000110812525,0.000016088425],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989243,0.00004863088,0.00056935847,0.000113594826,0.00027954407,0.0000645649],"domain_scores_gemma":[0.9990638,0.00013321679,0.00050500326,0.00005944964,0.00017763117,0.000060896065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003048989,0.000074811964,0.00020741062,0.00010356341,0.000012279014,0.000018608554,0.00007575501,0.000027090857,0.0000119948945],"category_scores_gemma":[0.0008417656,0.000070348324,0.000035849083,0.00020617558,0.0000985094,0.0001070491,0.00009097407,0.00011987318,8.22349e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053221993,0.000005526262,0.0006215402,0.00013336675,0.00003491652,0.000031396772,0.00034775693,0.00020487979,0.020415945,0.000011142606,0.00027556822,0.97786474],"study_design_scores_gemma":[0.0013456836,0.00023447792,0.019913664,0.013856192,0.00012977759,0.0013662985,0.00059006346,0.91079766,0.0069491374,0.001853053,0.0426259,0.00033807463],"about_ca_topic_score_codex":0.00076636206,"about_ca_topic_score_gemma":0.00004932274,"teacher_disagreement_score":0.97752666,"about_ca_system_score_codex":0.000062277126,"about_ca_system_score_gemma":0.000024951503,"threshold_uncertainty_score":0.28687215},"labels":[],"label_agreement":null},{"id":"W3047878169","doi":"10.1080/01431161.2020.1763512","title":"Assessing the use of cross-orbit Sentinel-1 images in land cover classification","year":2020,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":29,"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 de Sherbrooke","funders":"","keywords":"Land cover; Support vector machine; Orbit (dynamics); Remote sensing; Computer science; Contextual image classification; Cover (algebra); Land use; Artificial intelligence; Pattern recognition (psychology); Geography; Image (mathematics)","score_opus":0.0840876935383386,"score_gpt":0.333687312960648,"score_spread":0.2495996194223094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047878169","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8395683,0.00008348465,0.1562669,0.0028163055,0.00065343914,0.0000643537,0.00000298894,0.000030916563,0.00051330536],"genre_scores_gemma":[0.9711043,0.00008124594,0.0282021,0.00019350777,0.00036626583,8.142936e-9,0.000005116779,0.000028590586,0.000018823568],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984627,0.00007927496,0.0007150746,0.0001179016,0.0004938827,0.00013116424],"domain_scores_gemma":[0.9983937,0.00025715295,0.0004334275,0.00013976173,0.000722545,0.000053395106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003237268,0.00012678282,0.0002026684,0.00017742795,0.00002919604,0.0004171016,0.00021464973,0.00006631102,0.000006171265],"category_scores_gemma":[0.00080811,0.00010521921,0.000104596846,0.00021698976,0.00009587189,0.00095659756,0.000032513395,0.00035603132,0.000008091913],"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":[0.000070573806,0.000017225355,0.008795851,0.000045515735,0.00013871324,0.0001730081,0.0006817961,0.17233521,0.650588,0.000012620531,0.0011401017,0.16600144],"study_design_scores_gemma":[0.0004581034,0.000008923673,0.0787636,0.00023610768,0.00001897655,0.00022770642,0.000066472494,0.89142305,0.025002072,0.00006229428,0.0036245347,0.000108149914],"about_ca_topic_score_codex":0.000022081424,"about_ca_topic_score_gemma":0.0000027308436,"teacher_disagreement_score":0.71908784,"about_ca_system_score_codex":0.00013742085,"about_ca_system_score_gemma":0.000049998118,"threshold_uncertainty_score":0.42907146},"labels":[],"label_agreement":null},{"id":"W3047889481","doi":"10.1080/01431161.2020.1766146","title":"Unsupervised dimensionality reduction of hyperspectral images using representations of reflectance spectra","year":2020,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","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":"Institut National de la Recherche Scientifique","funders":"Jet Propulsion Laboratory; National Aeronautics and Space Administration","keywords":"Hyperspectral imaging; Dimensionality reduction; Remote sensing; Reflectivity; Reduction (mathematics); Curse of dimensionality; Computer science; Artificial intelligence; Spectral line; Pattern recognition (psychology); Environmental science; Geology; Mathematics; Optics; Physics","score_opus":0.04145658259472484,"score_gpt":0.3066954782531256,"score_spread":0.26523889565840075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047889481","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7983031,0.00022957256,0.19842863,0.001328733,0.00083797524,0.000069174734,0.000007776574,0.000035359426,0.0007596726],"genre_scores_gemma":[0.7435727,0.000086184606,0.25594014,0.000016931332,0.0003538378,3.5498013e-9,0.0000033959655,0.00002200682,0.0000047729723],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99823534,0.00007001757,0.0008263037,0.00014811507,0.00059312326,0.00012707249],"domain_scores_gemma":[0.99805754,0.00007726307,0.0005126157,0.00015765398,0.0011150055,0.000079904996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022566075,0.00013506082,0.0002996168,0.00021738838,0.000030212932,0.000027533197,0.00018478629,0.000058934067,0.000009875928],"category_scores_gemma":[0.00035886987,0.00014332807,0.00017802765,0.00029265534,0.00010869021,0.00032278727,0.000023507622,0.00025016305,0.0000014917612],"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":[0.00009439002,0.000012904758,0.000023477049,0.000029032546,0.00015418114,0.000035144716,0.00059114984,0.032550864,0.95214677,0.000046370875,0.00010730269,0.014208433],"study_design_scores_gemma":[0.0004148758,0.00003895453,0.00094209786,0.0002168354,0.000053721076,0.00057090586,0.00035964593,0.32352033,0.67302656,0.00070269255,0.000046648518,0.00010672967],"about_ca_topic_score_codex":0.000034672412,"about_ca_topic_score_gemma":0.0000013203427,"teacher_disagreement_score":0.29096946,"about_ca_system_score_codex":0.00019007287,"about_ca_system_score_gemma":0.00008146711,"threshold_uncertainty_score":0.5844749},"labels":[],"label_agreement":null},{"id":"W3095757848","doi":"10.1080/01431161.2020.1805136","title":"C-band synthetic aperture radar (SAR) imagery for the classification of diverse cropping systems","year":2020,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":29,"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; Agriculture and Agri-Food Canada","funders":"Canadian Space Agency","keywords":"Synthetic aperture radar; Remote sensing; Constellation; Environmental science; Radar; Earth observation; Random forest; Decision tree; Computer science; Satellite; Artificial intelligence; Geology; Telecommunications","score_opus":0.024912844138275197,"score_gpt":0.24773570270778464,"score_spread":0.22282285856950945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3095757848","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37016913,0.0005397408,0.5885926,0.03374444,0.0036264043,0.0005413863,0.00002331641,0.000033761462,0.002729253],"genre_scores_gemma":[0.9744739,0.000043505188,0.024346089,0.0004338018,0.00061775453,7.4462423e-9,0.0000027232807,0.000014151718,0.00006809256],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854016,0.00006827309,0.00046784309,0.00015524706,0.0006408638,0.00012763446],"domain_scores_gemma":[0.9984404,0.00044552595,0.00068944885,0.000117059884,0.00023430963,0.00007327958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034120044,0.000120480065,0.00019379689,0.00003834833,0.000092262635,0.0000809505,0.00035320636,0.00006545159,0.000016236014],"category_scores_gemma":[0.0007140424,0.00007570791,0.00017843583,0.00012581256,0.00013916935,0.00018318887,0.00006047212,0.00021226832,0.000012271207],"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":[0.00017003206,0.00001712088,0.00007137465,0.000020405038,0.0001626941,0.000054326785,0.0010291818,0.0074087544,0.8366284,0.00002082943,0.005459385,0.14895755],"study_design_scores_gemma":[0.0014149933,0.00021197977,0.0072882324,0.000802164,0.00024064344,0.0020177604,0.0038724816,0.8174937,0.028110089,0.00025169156,0.13789271,0.0004035857],"about_ca_topic_score_codex":0.00011186437,"about_ca_topic_score_gemma":0.000006065282,"teacher_disagreement_score":0.81008494,"about_ca_system_score_codex":0.0001681037,"about_ca_system_score_gemma":0.00002127286,"threshold_uncertainty_score":0.30872792},"labels":[],"label_agreement":null},{"id":"W3120501288","doi":"10.1080/01431161.2020.1856961","title":"Impacts of light detection and ranging (LiDAR) data organization and unit of analysis on land cover classification","year":2021,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Land cover; Lidar; Cover (algebra); Remote sensing; Ranging; Pixel; Land use; Computer science; Perspective (graphical); Environmental science; Data mining; Geography; Ecology; Artificial intelligence","score_opus":0.01690891139356884,"score_gpt":0.2663147833468383,"score_spread":0.24940587195326944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120501288","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9394436,0.00004162512,0.059435077,0.0005048151,0.00008174623,0.000025897176,0.00000620115,0.0000034385732,0.00045762878],"genre_scores_gemma":[0.99106103,0.00014360655,0.0086670015,0.00003417562,0.000049588514,1.1751679e-9,0.0000189647,0.0000067214555,0.00001891613],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99911207,0.000054624983,0.00031096808,0.00013836358,0.00032748078,0.000056500074],"domain_scores_gemma":[0.99902695,0.000085809006,0.00042912393,0.00017938713,0.0002352862,0.000043459764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028416625,0.00006308688,0.00014918517,0.00017150253,0.000042944506,0.00003614544,0.00009559732,0.000039896426,0.000019400164],"category_scores_gemma":[0.00028919065,0.00005816742,0.00003093807,0.0004625367,0.000053226995,0.00016930868,0.00007979216,0.00008549781,0.0000016696779],"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":[0.000055672208,0.000032209835,0.027240535,0.000008800764,0.0003413336,0.000016234662,0.0005055968,0.0015102314,0.7154314,0.000017177335,0.00002008704,0.25482076],"study_design_scores_gemma":[0.0009171268,0.00007265647,0.47029394,0.00020306832,0.0005688335,0.0007676464,0.00035173068,0.18270712,0.34170964,0.00032382365,0.0019069313,0.0001774684],"about_ca_topic_score_codex":0.00014630076,"about_ca_topic_score_gemma":0.00010538027,"teacher_disagreement_score":0.4430534,"about_ca_system_score_codex":0.000047268637,"about_ca_system_score_gemma":0.000021729464,"threshold_uncertainty_score":0.23719986},"labels":[],"label_agreement":null},{"id":"W3120662950","doi":"10.1080/01431161.2020.1862437","title":"Building change detection in very high-resolution remote sensing image based on pseudo-orthorectification","year":2021,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":17,"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 Science Foundation of Jiangsu Province; National Natural Science Foundation of China; Ministry of Natural Resources","keywords":"Orthophoto; Computer science; Aerial image; Shadow (psychology); Computer vision; Remote sensing; Artificial intelligence; Change detection; Facade; Satellite; Line (geometry); Consistency (knowledge bases); Constraint (computer-aided design); Elevation (ballistics); Roof; Photogrammetry; Image (mathematics); Geography; Mathematics","score_opus":0.02225516031754784,"score_gpt":0.26414349224268413,"score_spread":0.24188833192513629,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120662950","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38258412,0.00005975281,0.6132207,0.00077997986,0.0029378626,0.00009108927,0.0000023427542,0.00008675636,0.00023742807],"genre_scores_gemma":[0.6927584,0.000069980844,0.30614054,0.00013226474,0.0008228909,1.2659714e-8,0.00001308274,0.000055240853,0.000007637268],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975193,0.00020851445,0.00084607914,0.0003186981,0.00080072857,0.00030664672],"domain_scores_gemma":[0.9978164,0.00022437124,0.00042180272,0.00031678416,0.0011216099,0.000099043486],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00073718827,0.00026879593,0.0003217289,0.0009833063,0.000081871985,0.00022030863,0.00016134648,0.00018356802,0.0000052513687],"category_scores_gemma":[0.00077094673,0.00031280718,0.00018019252,0.0006142188,0.000049883714,0.00054549554,0.000029632407,0.0006616699,0.000015443029],"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":[0.00007270398,0.000009573398,0.0000034105778,0.000015580878,0.000029641225,0.00038865325,0.0000936778,0.018267842,0.4151635,0.000004672122,0.000017728276,0.56593305],"study_design_scores_gemma":[0.0005824509,0.000029321129,0.0021925645,0.0007218558,0.000024952154,0.0008337127,0.00004929596,0.74917424,0.24522163,0.00039949737,0.0005680298,0.00020247139],"about_ca_topic_score_codex":0.00020188992,"about_ca_topic_score_gemma":0.00015324561,"teacher_disagreement_score":0.73090637,"about_ca_system_score_codex":0.0012988452,"about_ca_system_score_gemma":0.000087988185,"threshold_uncertainty_score":0.9999324},"labels":[],"label_agreement":null},{"id":"W3130723280","doi":"10.1080/01431161.2021.1880661","title":"A new method to estimate global freshwater phytoplankton carbon fixation using satellite remote sensing: initial results","year":2021,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","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":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Environmental science; Phytoplankton; Carbon cycle; Northern Hemisphere; Latitude; Southern Hemisphere; Satellite; Remote sensing; Primary production; Atmospheric sciences; Climatology; Ecosystem; Ecology; Geography; Geology; Nutrient; Biology","score_opus":0.025830276290614167,"score_gpt":0.3191159406907825,"score_spread":0.2932856644001683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3130723280","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4020312,0.0002474482,0.57276785,0.0024986316,0.004583152,0.00011223106,0.00009356895,0.00003370885,0.017632157],"genre_scores_gemma":[0.28140128,0.000018555249,0.7162098,0.00040587515,0.001675425,3.3464453e-10,0.00006823844,0.000007740031,0.000213035],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977308,0.00021376733,0.00075214537,0.0002693434,0.000759691,0.00027424557],"domain_scores_gemma":[0.9982633,0.00012002729,0.00048186048,0.00015181792,0.00073554943,0.00024746673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064240006,0.00018820216,0.00030466582,0.00017279951,0.000068658046,0.0002705219,0.00017958516,0.000089696354,0.000060010818],"category_scores_gemma":[0.00029389394,0.00017144965,0.00014149757,0.00027644774,0.00001318375,0.00022182391,0.00005189302,0.00021916209,0.000016498125],"study_design_candidate":"design_other","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":[0.00047065574,0.0000024468295,0.00030745709,0.00000774352,0.000093830546,0.001903869,0.0001918854,0.010749523,0.0019738416,0.0000024095252,0.00013409325,0.9841623],"study_design_scores_gemma":[0.0011413783,0.00012898317,0.0026856023,0.0006333794,0.00006867577,0.012398212,0.00012378204,0.93073255,0.012844807,0.0032181516,0.035714313,0.0003101784],"about_ca_topic_score_codex":0.014631135,"about_ca_topic_score_gemma":0.023352899,"teacher_disagreement_score":0.9838521,"about_ca_system_score_codex":0.00007323995,"about_ca_system_score_gemma":0.00034679603,"threshold_uncertainty_score":0.9944684},"labels":[],"label_agreement":null},{"id":"W3136020102","doi":"10.1080/01431161.2021.1897187","title":"Aboveground biomass patterns across treeless northern landscapes","year":2021,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Horizon 2020 Framework Programme; Vetenskapsrådet; Academy of Finland; Svenska Forskningsrådet Formas; European Commission; Stockholms Universitet; Aurora Research Institute; Helsingin Yliopisto","keywords":"Tundra; Biomass (ecology); Vegetation (pathology); Environmental science; Physical geography; Deciduous; Remote sensing; Arctic; Satellite imagery; Ecology; Geology; Geography; Biology","score_opus":0.029889663442861765,"score_gpt":0.2816064281200184,"score_spread":0.25171676467715665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136020102","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99321586,0.00051998324,0.0017921696,0.0012577961,0.0020539016,0.000015726606,0.0003907025,0.0000072681364,0.0007465673],"genre_scores_gemma":[0.99699926,0.00028045572,0.0008189329,0.00032938813,0.0012255812,9.502658e-10,0.00025919202,0.000005111723,0.000082048435],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9988696,0.00004654857,0.00031489716,0.00011931088,0.0004599171,0.00018970219],"domain_scores_gemma":[0.99894404,0.0001131058,0.00023222636,0.00008404327,0.0005357921,0.000090802954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020773025,0.0001019364,0.00016478404,0.000070086324,0.000070185975,0.00021801682,0.00019058393,0.00005054678,0.0006611169],"category_scores_gemma":[0.000048914408,0.00008427905,0.00013028856,0.0000916184,0.000020860725,0.00021429152,0.000019828567,0.00014859981,0.00004516714],"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":[0.00008397249,0.00001221636,0.37384534,0.000011197731,0.00013978344,0.003257132,0.0014956885,0.00017890242,0.004313703,8.763246e-7,0.000104177605,0.616557],"study_design_scores_gemma":[0.0022352778,0.00015145578,0.8818643,0.0006720625,0.00006151261,0.01566422,0.006475193,0.04284274,0.011219253,0.0007399684,0.03750474,0.0005692917],"about_ca_topic_score_codex":0.0021082023,"about_ca_topic_score_gemma":0.12192533,"teacher_disagreement_score":0.6159877,"about_ca_system_score_codex":0.000015414742,"about_ca_system_score_gemma":0.000057155412,"threshold_uncertainty_score":0.89409727},"labels":[],"label_agreement":null},{"id":"W3196006465","doi":"10.1080/01431161.2021.1939910","title":"Deep support vector machine for PolSAR image classification","year":2021,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":31,"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":"Artificial intelligence; Support vector machine; Pattern recognition (psychology); Computer science; Artificial neural network; Confusion matrix; Synthetic aperture radar; Parametric statistics; Contextual image classification; Mathematics; Image (mathematics)","score_opus":0.012523542666990324,"score_gpt":0.2706508899775248,"score_spread":0.25812734731053444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196006465","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023233755,0.00021807133,0.99118483,0.0021578125,0.0006383305,0.00006518272,0.000009741281,0.000061866034,0.0033407789],"genre_scores_gemma":[0.2524877,0.00012737908,0.7467836,0.000112639886,0.00038844708,6.469973e-8,0.00002107824,0.000023872706,0.00005522924],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992031,0.000012903709,0.000352083,0.000091572125,0.00023454802,0.00010582712],"domain_scores_gemma":[0.9989401,0.00009763609,0.0001294061,0.00012458657,0.0006578109,0.00005043083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015905818,0.00009617961,0.00014243023,0.00011140709,0.0000317502,0.000058270638,0.00014382835,0.00005663204,0.000044881497],"category_scores_gemma":[0.00012124538,0.000093096314,0.00012988049,0.00007378764,0.000021225384,0.00010800477,0.000015799227,0.00013477275,0.0000061802775],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012364668,0.000011120677,0.000004233802,0.000008561842,0.00009487001,0.00004339575,0.00006734363,0.000010534838,0.04953525,0.0005171428,0.00094313954,0.94875205],"study_design_scores_gemma":[0.00037142338,0.000028421457,0.00027761792,0.00008286711,0.000042292435,0.0015881635,0.0000809549,0.3312135,0.15282327,0.0038664937,0.50947034,0.00015466688],"about_ca_topic_score_codex":0.000004693385,"about_ca_topic_score_gemma":0.0000066658463,"teacher_disagreement_score":0.9485974,"about_ca_system_score_codex":0.00013508482,"about_ca_system_score_gemma":0.00004525491,"threshold_uncertainty_score":0.37963575},"labels":[],"label_agreement":null},{"id":"W3199465235","doi":"10.1080/01431161.2021.1957513","title":"A machine learning approach for spatiotemporal imputation of MODIS chlorophyll-a","year":2021,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Water Quality Monitoring and Analysis","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Imputation (statistics); Moderate-resolution imaging spectroradiometer; Missing data; Support vector machine; Computer science; Artificial neural network; Regression; Random forest; Remote sensing; Artificial intelligence; Data mining; Machine learning; Statistics; Mathematics; Geography","score_opus":0.021522591800169102,"score_gpt":0.27570459564911864,"score_spread":0.25418200384894957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199465235","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5464746,0.000038797745,0.4526569,0.00033101052,0.00021790124,0.000020642006,0.000003109753,0.0000043910395,0.00025269858],"genre_scores_gemma":[0.7987989,0.0000134580705,0.20079277,0.000016407377,0.00019140691,9.667571e-9,0.000016468637,0.000006786063,0.00016375133],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989221,0.000064351385,0.00037669638,0.000106754305,0.00044648978,0.00008357621],"domain_scores_gemma":[0.999245,0.000048166312,0.00040629547,0.000056688583,0.00020373135,0.000040133087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038236988,0.00006768543,0.00015597984,0.0000689886,0.000041597574,0.000035221852,0.000108978005,0.00003154482,0.000016270717],"category_scores_gemma":[0.00018364549,0.00006157416,0.0001663856,0.000101394326,0.000033869946,0.00013433107,0.00005137804,0.0001162808,0.0000016094324],"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":[0.00015705622,0.00008455796,0.003755324,0.000024721174,0.0003136752,0.00007475478,0.0015906055,0.23343086,0.18719701,0.00001706413,0.00005777004,0.5732966],"study_design_scores_gemma":[0.0007392375,0.000084981344,0.0014076607,0.00007706452,0.00006885244,0.00024738957,0.00030731835,0.8206331,0.17360698,0.0013909637,0.0013064066,0.00013003977],"about_ca_topic_score_codex":0.00034204428,"about_ca_topic_score_gemma":0.0000033511715,"teacher_disagreement_score":0.58720225,"about_ca_system_score_codex":0.00010148515,"about_ca_system_score_gemma":0.000018850402,"threshold_uncertainty_score":0.25109214},"labels":[],"label_agreement":null},{"id":"W3208937889","doi":"10.1080/01431161.2021.1981559","title":"Counting Carbon: Quantifying Biomass in the McMurdo Dry Valleys through Orbital &amp; Field Observations","year":2021,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Polar Research and Ecology","field":"Environmental Science","cited_by":14,"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":"Photosynthetically active radiation; Environmental science; Biomass (ecology); Remote sensing; Satellite imagery; Biosphere; Geology; Oceanography; Ecology; Photosynthesis; Biology; Botany","score_opus":0.08062888918534811,"score_gpt":0.3368044201603103,"score_spread":0.2561755309749622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208937889","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97775745,0.00011795371,0.0074054156,0.009165336,0.0005665735,0.000034621055,8.3252365e-7,0.000004360076,0.0049474547],"genre_scores_gemma":[0.9739468,0.000056277135,0.02477014,0.000914249,0.00022288706,2.228362e-8,0.0000032522414,0.0000067395904,0.000079633246],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99858856,0.00012535174,0.00034609518,0.000119638236,0.0006086751,0.00021168051],"domain_scores_gemma":[0.99920684,0.00036680634,0.00017716318,0.00011425496,0.00009900201,0.000035954927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071987236,0.000073734795,0.000115516326,0.00005542954,0.00008464057,0.00010116176,0.00031371895,0.000051293093,0.00012371584],"category_scores_gemma":[0.00094727206,0.000058894486,0.0000831042,0.00021628929,0.000057434452,0.00027321224,0.0001485976,0.00033918544,0.000022269016],"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":[0.00022615348,0.00026399145,0.27957657,0.000025489244,0.0003798433,0.010508008,0.009548411,0.002529038,0.5362601,0.00034494948,0.0042500156,0.15608741],"study_design_scores_gemma":[0.0042948904,0.0003745329,0.58740187,0.0009896947,0.00010138102,0.017040806,0.00803332,0.10354099,0.05634182,0.028932989,0.19179188,0.0011558412],"about_ca_topic_score_codex":0.0029806034,"about_ca_topic_score_gemma":0.0069892327,"teacher_disagreement_score":0.4799183,"about_ca_system_score_codex":0.00017922051,"about_ca_system_score_gemma":0.00005913815,"threshold_uncertainty_score":0.45058006},"labels":[],"label_agreement":null},{"id":"W4233199126","doi":"10.1080/01431160120291","title":"Multitemporal monitoring of soil moisture with RADARSAT SAR during the 1997 Southern Great Plains hydrology experiment","year":2001,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":53,"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; University of Bath; National Aeronautics and Space Administration; U.S. Department of Energy","keywords":"Hydrology (agriculture); Environmental science; Water content; Geology; Remote sensing","score_opus":0.009195638982366883,"score_gpt":0.23652836510455966,"score_spread":0.22733272612219277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233199126","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9943989,0.0001660134,0.0021267931,0.000782738,0.000775381,0.00006749075,0.000001194958,0.000012468291,0.0016690264],"genre_scores_gemma":[0.98141384,0.000059849284,0.017501995,0.000081060905,0.00069350854,3.1512533e-9,0.000001094125,0.0000240296,0.00022463326],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982698,0.00007981987,0.00044222528,0.00017989997,0.00080217933,0.00022609605],"domain_scores_gemma":[0.9990444,0.00006916884,0.0005285897,0.000178564,0.00009961726,0.00007966328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024650616,0.00018283965,0.00023838994,0.00009369729,0.00012950778,0.000040395586,0.0003033905,0.000079209865,0.000017337165],"category_scores_gemma":[0.00003363613,0.000114198316,0.00013925911,0.00012595016,0.00022219856,0.00014755383,0.00011377836,0.0003253866,0.0000099582885],"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":[0.0018549145,0.00011478214,0.2254644,0.000014137075,0.00077575695,0.0039698835,0.0154826855,0.047683556,0.21284346,0.0000031273642,0.00015105956,0.49164224],"study_design_scores_gemma":[0.010175061,0.00092083385,0.41519663,0.0021024318,0.00036463267,0.047631912,0.018168172,0.04837234,0.42181906,0.00062342617,0.033106584,0.0015189137],"about_ca_topic_score_codex":0.002170206,"about_ca_topic_score_gemma":0.0007024502,"teacher_disagreement_score":0.4901233,"about_ca_system_score_codex":0.00027257248,"about_ca_system_score_gemma":0.000022285809,"threshold_uncertainty_score":0.46568722},"labels":[],"label_agreement":null},{"id":"W4234977460","doi":"10.1080/01431160119688","title":"Wind and cloud cover effects on spectral measurements of flooded rice crops in red and near-infrared SPOT-HRV bands","year":2001,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Radiance; Cloud cover; Irradiance; Environmental science; Infrared; Atmospheric sciences; Remote sensing; Wavelength; Physics; Geography; Optics; Cloud computing","score_opus":0.010795117177132914,"score_gpt":0.23396203098083185,"score_spread":0.22316691380369894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234977460","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9933445,0.000055935427,0.00029007913,0.00064430205,0.00075850816,0.000093904535,8.1792587e-7,0.000005727779,0.0048062354],"genre_scores_gemma":[0.98309845,0.00007141469,0.016169218,0.00021206416,0.000297665,2.2704953e-9,0.0000011438955,0.000012487258,0.00013753699],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.998268,0.000108820896,0.00041629552,0.00019697356,0.000827757,0.00018213215],"domain_scores_gemma":[0.9992272,0.0001232039,0.00036405498,0.000099313256,0.0000876387,0.0000985404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045040366,0.00015907145,0.0002518607,0.00010216813,0.00004722894,0.00007668163,0.00014141732,0.00008518424,0.000019275803],"category_scores_gemma":[0.0002595262,0.00012895672,0.00006329963,0.00016884317,0.00011441115,0.00021351654,0.00007348403,0.00028569216,0.0000061740216],"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":[0.0016272613,0.00012974016,0.055484172,0.000028601737,0.0002532346,0.0018908749,0.002177973,0.0077040144,0.7170164,0.0000024162548,0.002022041,0.21166329],"study_design_scores_gemma":[0.003886516,0.00035306512,0.89001167,0.00092483783,0.000053955217,0.00353597,0.00006890671,0.015470847,0.08255874,0.0007581333,0.0020686486,0.00030870357],"about_ca_topic_score_codex":0.00019534114,"about_ca_topic_score_gemma":0.000055008863,"teacher_disagreement_score":0.8345275,"about_ca_system_score_codex":0.00024932524,"about_ca_system_score_gemma":0.000016975457,"threshold_uncertainty_score":0.5258702},"labels":[],"label_agreement":null},{"id":"W4251341345","doi":"10.1080/01431160116823","title":"Exploring spatial and temporal variation in middle infrared reflectance (at 3.75 <i>@</i>m) measured from the tropical forests of west Africa","year":2001,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Oceanic and Atmospheric Administration; Natural Environment Research Council; University of Southampton; University of Winnipeg","keywords":"Advanced very-high-resolution radiometer; Remote sensing; Environmental science; Radiometer; Radiometry; Climatology; Geography; Geology; Satellite; Physics","score_opus":0.053186566065050754,"score_gpt":0.24003458513004197,"score_spread":0.1868480190649912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251341345","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9862782,0.000101835,0.010833858,0.0012374715,0.000579492,0.00007290049,0.000001964481,0.0000069516213,0.00088732585],"genre_scores_gemma":[0.97553056,0.00012270674,0.023906335,0.000068473506,0.00031770262,1.2389758e-8,0.0000021750525,0.000010112009,0.00004190639],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9983286,0.000116886,0.00046661662,0.00016984441,0.0007654603,0.00015256977],"domain_scores_gemma":[0.99909496,0.00016376466,0.00045550623,0.00012279271,0.00010291898,0.00006004968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002725448,0.0001211537,0.00019237181,0.00005072364,0.00006312659,0.000045492347,0.00022943923,0.000062089275,0.000038464903],"category_scores_gemma":[0.0003044071,0.00008718513,0.0000704784,0.00015291851,0.00010586385,0.00031569402,0.0001202102,0.0002498662,0.000008065076],"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":[0.0014045713,0.000105657404,0.33108634,0.0000064812666,0.00018289163,0.00077300466,0.019611733,0.006519279,0.3453156,0.00000785996,0.00081673235,0.29416984],"study_design_scores_gemma":[0.0008187754,0.000054106073,0.9709588,0.00028778173,0.000017731074,0.00047936608,0.00018115855,0.021646898,0.0020794559,0.0014484191,0.001907614,0.00011987485],"about_ca_topic_score_codex":0.0019535418,"about_ca_topic_score_gemma":0.007527822,"teacher_disagreement_score":0.6398725,"about_ca_system_score_codex":0.0003109059,"about_ca_system_score_gemma":0.000015592943,"threshold_uncertainty_score":0.42007014},"labels":[],"label_agreement":null},{"id":"W4295854875","doi":"10.1080/01431161.2022.2115863","title":"Unsupervised change detection in SAR images based on generalized likelihood ratio test and a two-stage morphological filter","year":2022,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":8,"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":"Speckle noise; Synthetic aperture radar; Speckle pattern; Computer science; Likelihood-ratio test; Change detection; Artificial intelligence; Matched filter; Pixel; Pattern recognition (psychology); Noise (video); Filter (signal processing); Mathematics; Computer vision; Statistics; Image (mathematics)","score_opus":0.027585013622797857,"score_gpt":0.2603106715957832,"score_spread":0.23272565797298533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295854875","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.887793,0.000110799694,0.10921425,0.0013094427,0.0010602433,0.00016069856,0.000019413641,0.000063511456,0.00026864198],"genre_scores_gemma":[0.9670294,0.00004196623,0.032095622,0.0004425948,0.0003264754,1.3343387e-7,0.000011045742,0.000033062657,0.000019686731],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858075,0.00013739552,0.00043869478,0.0001691725,0.000499259,0.00017471595],"domain_scores_gemma":[0.9992639,0.00018520327,0.0001730632,0.0001278456,0.00018611367,0.00006390232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046422967,0.00015816266,0.0002016661,0.00052647217,0.00006939217,0.000096699936,0.00014061347,0.000045940156,0.00003282382],"category_scores_gemma":[0.00022891507,0.00015985487,0.00008062937,0.00020153366,0.00003377504,0.00019699661,0.00004221465,0.00046274604,0.0000029941648],"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":[0.00016646142,0.000042006533,0.0001915752,0.000009967261,0.00003129284,0.0009477099,0.00024812028,0.044587005,0.5539013,0.0000018868194,0.00006613643,0.3998065],"study_design_scores_gemma":[0.0017162852,0.000129553,0.004604409,0.00007760111,0.00001194473,0.0007733375,0.00009409559,0.9506453,0.04056228,0.00007603715,0.0011506871,0.0001584232],"about_ca_topic_score_codex":0.00006157395,"about_ca_topic_score_gemma":0.000022824637,"teacher_disagreement_score":0.9060583,"about_ca_system_score_codex":0.00041989883,"about_ca_system_score_gemma":0.00002596326,"threshold_uncertainty_score":0.65186924},"labels":[],"label_agreement":null},{"id":"W4312227307","doi":"10.1080/01431161.2022.2143733","title":"Radar altimetry for classifying surface conditions of subarctic lakes during freezing and thawing periods","year":2022,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":0,"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","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Altimeter; Subarctic climate; Remote sensing; Satellite; Environmental science; Spectroradiometer; Support vector machine; Climatology; Physical geography; Geography; Geology; Computer science; Oceanography; Artificial intelligence; Reflectivity","score_opus":0.02274303063227612,"score_gpt":0.2566978083928014,"score_spread":0.23395477776052526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312227307","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99299157,0.0009233586,0.004170036,0.00082543876,0.0008613187,0.000055670967,0.000061299965,0.0000060303123,0.0001052511],"genre_scores_gemma":[0.9635518,0.000066278466,0.036089014,0.000077622826,0.00016484426,8.393384e-9,0.0000116522915,0.0000040392133,0.00003470675],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990875,0.000038424652,0.00032598348,0.000091032634,0.00033808599,0.00011897068],"domain_scores_gemma":[0.99906147,0.00032618776,0.00030988263,0.000047177513,0.00021353993,0.00004172582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003175008,0.00007023161,0.00015662555,0.00007289063,0.00038578015,0.000042472304,0.00011855286,0.000015528956,0.00012363754],"category_scores_gemma":[0.00014102571,0.000068053654,0.00009068527,0.00011304252,0.00005195812,0.00015809065,0.0000326,0.00014745761,2.775494e-7],"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":[0.00074999075,0.000060494327,0.47244847,0.00018375392,0.0015360651,0.00035789073,0.007536056,0.13477138,0.08099082,0.00019910808,0.0005087633,0.3006572],"study_design_scores_gemma":[0.0022715202,0.00032441673,0.68267447,0.00030254436,0.00015172026,0.0021025934,0.016015437,0.28375047,0.00297155,0.0019359145,0.007124593,0.00037476653],"about_ca_topic_score_codex":0.00039658768,"about_ca_topic_score_gemma":0.0001535356,"teacher_disagreement_score":0.30028245,"about_ca_system_score_codex":0.000024389099,"about_ca_system_score_gemma":0.000047287074,"threshold_uncertainty_score":0.29671478},"labels":[],"label_agreement":null},{"id":"W4312464114","doi":"10.1080/01431161.2022.2142077","title":"Integrating Sentinel-1 SAR and Sentinel-2 optical imagery with a crop structure dynamics model to track crop condition","year":2022,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","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":"University of Calgary; Agriculture and Agri-Food Canada","funders":"","keywords":"Normalized Difference Vegetation Index; Remote sensing; Environmental science; Canola; Growing season; Synthetic aperture radar; Leaf area index; Polarimetry; Geography; Agronomy","score_opus":0.006727583205311761,"score_gpt":0.23885907745297166,"score_spread":0.23213149424765991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312464114","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9263893,0.000008687267,0.06844929,0.003378388,0.00036962228,0.000098544086,0.000015662496,0.000021307604,0.0012692391],"genre_scores_gemma":[0.837324,0.0000037722937,0.1612791,0.0009898418,0.00018262482,1.0157403e-8,0.000018470611,0.000025205758,0.00017698204],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977613,0.00008583172,0.00047525842,0.00029743646,0.0011325735,0.00024757395],"domain_scores_gemma":[0.99902374,0.00006993492,0.00042525728,0.00012926756,0.0001900302,0.00016178603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030410834,0.00021754757,0.00024477876,0.00014577268,0.00022435248,0.00019652983,0.0002603121,0.000057747202,0.00006554124],"category_scores_gemma":[0.00014304851,0.0001696786,0.000092851515,0.00023760913,0.00013740952,0.00029962248,0.0003091675,0.00068864046,0.00000483828],"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":[0.0003683245,0.000058652848,0.0010264892,0.000010025436,0.00017541765,0.0011640656,0.001723616,0.5950302,0.3059287,0.0000780479,0.0037031176,0.09073334],"study_design_scores_gemma":[0.00062390295,0.00007960934,0.003008219,0.0001146039,0.000049810802,0.011376035,0.0006162107,0.9782138,0.0041675773,0.0008337466,0.0006421629,0.00027431108],"about_ca_topic_score_codex":0.00006304172,"about_ca_topic_score_gemma":0.00007368348,"teacher_disagreement_score":0.3831836,"about_ca_system_score_codex":0.0006622839,"about_ca_system_score_gemma":0.00004001466,"threshold_uncertainty_score":0.6919293},"labels":[],"label_agreement":null},{"id":"W4320007571","doi":"10.1080/01431161.2022.2164528","title":"Derivation and assessment of forest-relevant polarimetric indices using RCM compact-pol data","year":2023,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"","keywords":"Remote sensing; Polarimetry; Radar; Environmental science; Land cover; Range (aeronautics); Synthetic aperture radar; Meteorology; Computer science; Geography; Land use","score_opus":0.044690846220532064,"score_gpt":0.34033153167148666,"score_spread":0.2956406854509546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320007571","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3750045,0.00017272963,0.62393,0.00032027566,0.00022040852,0.00005089074,0.000013581902,0.0000417423,0.00024592938],"genre_scores_gemma":[0.57391906,0.00019106049,0.4257556,0.000014703816,0.00009487356,3.457239e-9,0.000012008118,0.000011056285,0.0000016244611],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905384,0.000018184497,0.0003829092,0.00008763361,0.00036440464,0.00009302416],"domain_scores_gemma":[0.99917054,0.00016989089,0.00024078526,0.00016656246,0.00021275414,0.000039486957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000367165,0.00008697378,0.00016375967,0.00054995395,0.000031783024,0.000043836837,0.00024660156,0.00004818067,0.0000030097447],"category_scores_gemma":[0.000106753876,0.000079532656,0.00003545792,0.00028210986,0.000033529122,0.00025022728,0.00007223988,0.00015841168,5.2721157e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000058819232,0.00000909437,0.0012659847,0.000020228046,0.00017393011,0.000020913778,0.00007100478,0.00041089242,0.01459801,0.00019986478,0.00019968089,0.98302454],"study_design_scores_gemma":[0.00024011509,0.000023414454,0.028352996,0.0002626654,0.00004565168,0.00042264038,0.00010199271,0.9346041,0.0074784835,0.0014022918,0.026959246,0.00010642202],"about_ca_topic_score_codex":0.00016490469,"about_ca_topic_score_gemma":0.000009570175,"teacher_disagreement_score":0.9829181,"about_ca_system_score_codex":0.00008642827,"about_ca_system_score_gemma":0.000045468612,"threshold_uncertainty_score":0.32432476},"labels":[],"label_agreement":null},{"id":"W4353093529","doi":"10.1080/01431161.2023.2187723","title":"A Bayesian neural network approach for tropospheric temperature retrievals from a lidar instrument","year":2023,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; National Research Council Canada","funders":"","keywords":"Lidar; Artificial neural network; Altitude (triangle); Environmental science; Remote sensing; Troposphere; Range (aeronautics); Atmospheric temperature; Meteorology; Bayesian probability; Temperature measurement; Computer science; Mathematics; Artificial intelligence; Geology; Physics; Materials science","score_opus":0.009442513360958683,"score_gpt":0.22610244531057028,"score_spread":0.21665993194961158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4353093529","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.728494,0.000041029973,0.2694009,0.0006571675,0.0009039998,0.00012358661,0.0000048126685,0.000024731053,0.00034978116],"genre_scores_gemma":[0.61672163,0.00006133422,0.38200593,0.00036775524,0.00057270605,6.6761956e-8,0.000016613698,0.000023452896,0.00023049791],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99846315,0.00004462049,0.00040448588,0.00021136449,0.0006153288,0.0002610594],"domain_scores_gemma":[0.99935764,0.00006530648,0.00031700524,0.00012046438,0.000026064603,0.00011353228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026924332,0.00015939701,0.00020997517,0.000013450433,0.000097713346,0.00007589778,0.00030822292,0.00008909516,0.00007590095],"category_scores_gemma":[0.000051777177,0.00013882933,0.00018271197,0.00023257214,0.00008990599,0.00019575086,0.00015086879,0.00022556454,0.000013963868],"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":[0.0002739261,0.00002653634,0.0034575455,0.0000028623251,0.00014722456,0.00012135342,0.00033262503,0.6779304,0.0059816916,0.0000039993706,0.0014925818,0.3102292],"study_design_scores_gemma":[0.00083103805,0.00013096617,0.010218275,0.000050214,0.000039985334,0.00020916945,0.00038643938,0.9814387,0.00024637088,0.0019203259,0.0043283696,0.0002001715],"about_ca_topic_score_codex":0.00013580735,"about_ca_topic_score_gemma":0.000007427816,"teacher_disagreement_score":0.31002903,"about_ca_system_score_codex":0.00036966885,"about_ca_system_score_gemma":0.000013776327,"threshold_uncertainty_score":0.56612957},"labels":[],"label_agreement":null},{"id":"W4364360285","doi":"10.1080/01431161.2023.2195571","title":"A transfer learning approach for automatic mapping of retrogressive thaw slumps (RTSs) in the western Canadian Arctic","year":2023,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":8,"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 Ottawa","funders":"","keywords":"Thermokarst; Permafrost; Landform; Arctic; Computer science; Remote sensing; Ecosystem; Satellite imagery; Transfer of learning; Physical geography; Environmental science; Artificial intelligence; Geology; Geomorphology; Oceanography; Ecology; Geography","score_opus":0.05086450828178037,"score_gpt":0.270688532880474,"score_spread":0.21982402459869363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4364360285","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99461246,0.00012657474,0.0018564359,0.0022007357,0.00039301088,0.00011266302,0.00008969974,0.000006094492,0.0006023043],"genre_scores_gemma":[0.99758554,0.00007299722,0.0015579987,0.00035172742,0.0002443771,2.036848e-8,0.00015799576,0.0000044172984,0.000024915995],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895877,0.00008449175,0.00032707918,0.00008312122,0.00034949946,0.00019705985],"domain_scores_gemma":[0.9992875,0.00027915457,0.00012892157,0.00005260996,0.00019293111,0.000058893613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007649832,0.00007719724,0.00015533804,0.00042505268,0.00006312844,0.00006606768,0.00022792364,0.0000418375,0.000053561915],"category_scores_gemma":[0.000120300465,0.000056185327,0.000099570854,0.00023117723,0.000035736455,0.00015077027,0.000004303939,0.00020965573,0.0000051147867],"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":[0.00015291576,0.000017110788,0.33485284,0.00021330045,0.00026103077,0.00095745554,0.03113114,0.033952646,0.0012891103,0.000015797803,0.00046953675,0.5966871],"study_design_scores_gemma":[0.0007637119,0.00013146452,0.25875914,0.0006982274,0.000030822688,0.0011772857,0.007705784,0.72775835,0.00009384684,0.0003738785,0.0023527383,0.00015476729],"about_ca_topic_score_codex":0.03787937,"about_ca_topic_score_gemma":0.097098835,"teacher_disagreement_score":0.6938057,"about_ca_system_score_codex":0.000020649431,"about_ca_system_score_gemma":0.0000879969,"threshold_uncertainty_score":0.9685275},"labels":[],"label_agreement":null},{"id":"W4382791918","doi":"10.1080/01431161.2023.2221803","title":"Investigation of the sensitivity response of Touzi target scattering decomposition to modeled early ice growth","year":2023,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","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":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada","funders":"Canadian Space Agency; Environment and Climate Change Canada","keywords":"Sea ice; Scattering; Geology; Bay; Synthetic aperture radar; Decomposition; Salinity; Remote sensing; Environmental science; Optics; Oceanography; Physics; Chemistry","score_opus":0.014958899292782237,"score_gpt":0.24196900817480407,"score_spread":0.22701010888202183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382791918","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9635896,0.000003341363,0.032811724,0.002916139,0.0005537647,0.000046484885,0.000018142377,0.000006314876,0.00005450702],"genre_scores_gemma":[0.9722785,0.0000069073794,0.02737645,0.00020619856,0.000107751446,1.9404482e-9,0.000006146808,0.0000032410671,0.000014851007],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9987892,0.00019771124,0.00034999778,0.00007757304,0.00048388893,0.00010163692],"domain_scores_gemma":[0.9986838,0.00034423076,0.00036259694,0.00007196171,0.00048112706,0.00005626025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009624368,0.000068023786,0.00013396435,0.00019949365,0.0000541161,0.000020574475,0.00014246747,0.000031505366,0.0000081755],"category_scores_gemma":[0.00029495597,0.0000524682,0.000090316105,0.00024217115,0.000065856744,0.00018825715,0.000028533183,0.0001193992,0.0000057034435],"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":[0.0036027858,0.000014314428,0.200854,0.000063860614,0.00033596382,0.00028857435,0.009788386,0.25324154,0.43238947,0.00006253169,0.00018495534,0.099173635],"study_design_scores_gemma":[0.000211497,0.00008212257,0.5564081,0.00030703645,0.000017284328,0.0002781353,0.00022708035,0.42041224,0.019832596,0.0021421479,0.000013022018,0.000068718546],"about_ca_topic_score_codex":0.00091770303,"about_ca_topic_score_gemma":0.00008800879,"teacher_disagreement_score":0.41255686,"about_ca_system_score_codex":0.000019513218,"about_ca_system_score_gemma":0.000078137484,"threshold_uncertainty_score":0.21395911},"labels":[],"label_agreement":null},{"id":"W4383823487","doi":"10.1080/01431161.2023.2225711","title":"A large-scale disturbance mapping ensemble through data-driven regionalization","year":2023,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Biodiversity Monitoring Institute; University of Calgary; University of Alberta","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Disturbance (geology); Scale (ratio); Vegetation (pathology); Cluster analysis; Variance (accounting); Computer science; Multispectral image; Thematic map; Remote sensing; Data mining; Environmental science; Cartography; Geography; Machine learning; Artificial intelligence","score_opus":0.030893076095630048,"score_gpt":0.2799303360220743,"score_spread":0.24903725992644424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383823487","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4711736,0.00007614789,0.50891846,0.0078828735,0.0031487455,0.00014712424,0.000020648282,0.00012098826,0.008511403],"genre_scores_gemma":[0.7165747,0.00036836695,0.27861524,0.0011102695,0.0014560688,1.1752026e-8,0.00017372878,0.000045021607,0.0016565695],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99775916,0.00008236001,0.0004907966,0.0003091022,0.0010919564,0.00026660305],"domain_scores_gemma":[0.9988186,0.00009162574,0.0005118695,0.0003346347,0.00016922265,0.00007409732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048975815,0.00015499073,0.00020097455,0.000093987095,0.000122038444,0.000106065454,0.0006991628,0.00008284793,0.00004312014],"category_scores_gemma":[0.00025995137,0.00012890187,0.00010281544,0.0005025544,0.000082998886,0.0007019824,0.00047898994,0.00025758773,0.00022617786],"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":[0.0001885549,0.00012736917,0.0037891238,0.000029727991,0.00047933147,0.002673625,0.01565677,0.084828936,0.11093501,0.0001382243,0.37423822,0.4069151],"study_design_scores_gemma":[0.00084050157,0.00003409559,0.015701555,0.0004970122,0.000036741596,0.0023073351,0.0012234618,0.55605847,0.0016565636,0.004702637,0.41658813,0.00035350485],"about_ca_topic_score_codex":0.000085039814,"about_ca_topic_score_gemma":0.00012259875,"teacher_disagreement_score":0.47122952,"about_ca_system_score_codex":0.0002800149,"about_ca_system_score_gemma":0.000025217194,"threshold_uncertainty_score":0.52564657},"labels":[],"label_agreement":null},{"id":"W4385584274","doi":"10.1080/01431161.2023.2240028","title":"Estimating ground-level CH<sub>4</sub> concentrations inferred from Sentinel-5P","year":2023,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Basic Research Program of Jiangsu Province; Ministry of Natural Resources","keywords":"Environmental science; Satellite; Greenhouse gas; Correlation coefficient; Atmospheric sciences; Ground level; Methane; Christian ministry; Meteorology; Geography; Geology; Chemistry; Statistics; Ground floor; Mathematics","score_opus":0.018123035329669326,"score_gpt":0.24832450218832913,"score_spread":0.2302014668586598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385584274","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73239106,0.000008064026,0.26556265,0.0005585446,0.0009798852,0.00003795525,0.0000039650517,0.000027695582,0.00043018634],"genre_scores_gemma":[0.87654865,0.00004446173,0.12258084,0.00030784728,0.00040402892,2.953392e-8,0.000019795554,0.000021410888,0.00007294287],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998339,0.000041800693,0.00048464525,0.00018283247,0.000726859,0.00022488188],"domain_scores_gemma":[0.99922836,0.00010235224,0.00040232638,0.0001153317,0.000035767785,0.00011587325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022207083,0.00014915536,0.00016848081,0.000027256403,0.00011354222,0.00008989618,0.00025766375,0.00006302285,0.000088089684],"category_scores_gemma":[0.00012264917,0.00014667265,0.00012327427,0.0001824214,0.00013856067,0.0003800996,0.00019638373,0.00022774002,0.00017152267],"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":[0.00004158786,0.000031608175,0.009989237,0.0000019526178,0.00014696912,0.00043122115,0.00073032576,0.37371886,0.147307,0.000010716959,0.00070113305,0.4668894],"study_design_scores_gemma":[0.0004938042,0.000017423867,0.06919565,0.000073767784,0.000028281718,0.00022789241,0.00022235568,0.92454344,0.002704945,0.0017811297,0.0005453987,0.00016594154],"about_ca_topic_score_codex":0.00021223536,"about_ca_topic_score_gemma":0.00003725303,"teacher_disagreement_score":0.5508246,"about_ca_system_score_codex":0.00038384862,"about_ca_system_score_gemma":0.000018579105,"threshold_uncertainty_score":0.5981137},"labels":[],"label_agreement":null},{"id":"W4387132604","doi":"10.1080/01431161.2023.2257862","title":"Visual analysis of coal fire detection research based on bibliometrics","year":2023,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Coal Properties and Utilization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Coal; China; Remote sensing; Bibliometrics; Data science; Geography; Environmental science; Environmental resource management; Computer science; Library science; Archaeology","score_opus":0.06608073034098433,"score_gpt":0.3609703599536663,"score_spread":0.29488962961268195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387132604","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8645962,0.000055918415,0.13375166,0.00018871014,0.0008840237,0.000030626597,0.0000036866816,0.000036936104,0.00045224972],"genre_scores_gemma":[0.9990011,0.00015878961,0.00060400215,0.000019055062,0.00017626019,8.802185e-9,0.0000067490637,0.000013369336,0.000020704498],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984859,0.00005056615,0.00033338208,0.00006738528,0.00094423455,0.000118498254],"domain_scores_gemma":[0.9986601,0.00021356998,0.00009634679,0.00006729498,0.0009209222,0.00004175773],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.00076195324,0.00006186964,0.00014920923,0.011455788,0.00003649699,0.000053134565,0.00011623806,0.000050045164,0.00001409404],"category_scores_gemma":[0.00038390327,0.000056491812,0.00013702021,0.009361891,0.000028135448,0.00009293655,0.000023311139,0.0002099039,0.0000060093444],"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":[0.0000689431,0.00000766597,0.0000827875,0.000008345201,0.00026103517,0.000033924924,0.0000509505,0.58439296,0.009577205,0.000001509622,0.00017568043,0.405339],"study_design_scores_gemma":[0.00018189446,0.00009284625,0.00453623,0.00008072498,0.00005146991,0.000008861797,0.00007311961,0.97530603,0.018311054,0.00003688733,0.0012732064,0.000047668742],"about_ca_topic_score_codex":0.00006501457,"about_ca_topic_score_gemma":0.000024673944,"teacher_disagreement_score":0.40529132,"about_ca_system_score_codex":0.00014389073,"about_ca_system_score_gemma":0.000024502255,"threshold_uncertainty_score":0.9997485},"labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"design_other","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"split"},{"id":"W4389224949","doi":"10.1080/01431161.2023.2283904","title":"CT-Fire: a CNN-Transformer for wildfire classification on ground and aerial images","year":2023,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Firefighting; Computer science; Environmental science; Fire detection; Transformer; Remote sensing; Aerial imagery; Artificial intelligence; Aerial image; Benchmark (surveying); Ecosystem; Cartography; Geography; Image (mathematics); Ecology; Engineering","score_opus":0.021016009060219828,"score_gpt":0.26434953097108227,"score_spread":0.24333352191086244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389224949","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94475305,0.00009765862,0.045331515,0.0023535443,0.0062089884,0.00014262153,0.000011741994,0.00012547981,0.0009753687],"genre_scores_gemma":[0.99678737,0.00021711957,0.0018508087,0.000057366462,0.0009154406,7.876731e-8,0.000006481373,0.00002325271,0.0001420985],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922466,0.000015842168,0.00031470592,0.0000788829,0.00025715763,0.00010872225],"domain_scores_gemma":[0.99959916,0.000093449795,0.000090443966,0.000052062194,0.00011736031,0.000047546655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024223383,0.000091235415,0.0001338097,0.00018259406,0.000048749047,0.00007837623,0.00007311334,0.00003548225,0.0000022721163],"category_scores_gemma":[0.000064279535,0.00008568766,0.00008763733,0.00009106945,0.000020179963,0.00015101467,0.0000040566542,0.000120876146,0.000011669753],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016464709,0.0000049894547,0.000010841693,0.000026119016,0.0001603689,0.000063238935,0.00031980433,0.0011737788,0.061296657,0.000047624762,0.00311953,0.9336124],"study_design_scores_gemma":[0.0023630934,0.00018082235,0.0064768135,0.00053534156,0.00004172872,0.0019667984,0.0005893121,0.8510715,0.017880717,0.0010461505,0.11753246,0.00031526017],"about_ca_topic_score_codex":0.000017846134,"about_ca_topic_score_gemma":0.000006454749,"teacher_disagreement_score":0.93329716,"about_ca_system_score_codex":0.00009511542,"about_ca_system_score_gemma":0.000014797373,"threshold_uncertainty_score":0.34942412},"labels":[],"label_agreement":null},{"id":"W4390918430","doi":"10.1080/01431161.2023.2299278","title":"Evaluating global vegetation products for application in heterogeneous forest-savanna landscapes","year":2024,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"H2020 European Research Council; Natural Sciences and Engineering Research Council of Canada; Horizon 2020 Framework Programme; European Commission","keywords":"Vegetation (pathology); Environmental science; Context (archaeology); Canopy; Scale (ratio); Remote sensing; Product (mathematics); Physical geography; Global change; Tree canopy; Satellite imagery; Geography; Ecology; Climate change; Cartography; Mathematics","score_opus":0.02176821038036494,"score_gpt":0.3310283031434259,"score_spread":0.309260092763061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390918430","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7378903,0.0002492048,0.25828537,0.0019203761,0.00068888045,0.000223439,0.0000027956778,0.000026605347,0.00071301585],"genre_scores_gemma":[0.88752425,0.000018005901,0.11195045,0.00007402913,0.00038520573,1.1986805e-7,0.000008888174,0.00001242764,0.000026638378],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987397,0.00003846704,0.00041199438,0.00021832004,0.00044984178,0.00014162992],"domain_scores_gemma":[0.99943316,0.00009238579,0.00018416306,0.00010546371,0.00014546642,0.000039361334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058462063,0.00010020348,0.00011729132,0.00009634273,0.000043576034,0.000107783286,0.00016108993,0.00004770593,0.0000028642123],"category_scores_gemma":[0.00020318053,0.000092284594,0.00008424545,0.00023224884,0.000030941283,0.00018865755,0.000035267287,0.00011315995,0.000030031555],"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":[0.000045201334,0.000013984379,0.0002675505,0.000014826541,0.000030872314,0.00002430473,0.00026251646,0.061864655,0.020516876,0.000056182267,0.00012235179,0.91678065],"study_design_scores_gemma":[0.0003330524,0.00006849174,0.0027227637,0.0002055735,0.000026592548,0.0007618108,0.00003237268,0.9735767,0.0035402414,0.010558672,0.008057229,0.00011645934],"about_ca_topic_score_codex":0.00015064223,"about_ca_topic_score_gemma":0.0004041009,"teacher_disagreement_score":0.91666424,"about_ca_system_score_codex":0.0003932218,"about_ca_system_score_gemma":0.000048268175,"threshold_uncertainty_score":0.37632564},"labels":[],"label_agreement":null},{"id":"W4392297798","doi":"10.1080/01431161.2024.2320179","title":"Automated ocean front feature mapping using Sentinel-1 with examples from the Gulf Stream","year":2024,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Oceanographic and Atmospheric Processes","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 Waterloo; University of Ottawa","funders":"","keywords":"Feature (linguistics); Front (military); Gulf Stream; Remote sensing; Feature tracking; Geology; Computer science; Oceanography; Artificial intelligence; Feature extraction","score_opus":0.015370994753437823,"score_gpt":0.23138788254617015,"score_spread":0.21601688779273234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392297798","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96164256,0.0035933703,0.031110728,0.001895599,0.0012154349,0.000037226215,0.000028230736,0.00008623143,0.00039062346],"genre_scores_gemma":[0.93261737,0.00014199942,0.06587871,0.00028379797,0.0009925939,4.926589e-10,0.000029403322,0.000007041964,0.000049108934],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988725,0.000046473095,0.00024774484,0.00014581396,0.00053187297,0.00015556646],"domain_scores_gemma":[0.9991851,0.00024270712,0.00019803901,0.00007640677,0.00023579545,0.00006195753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021805328,0.0001327508,0.00014710698,0.000053477906,0.00010694777,0.00039136142,0.00026042009,0.00004572434,0.00008316291],"category_scores_gemma":[0.00004893575,0.00007446489,0.00008979147,0.00019553985,0.000073940544,0.00031640186,0.000014263561,0.00026966678,0.000008557128],"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":[0.00026342497,0.000017487011,0.11643326,0.000044577515,0.0019274253,0.0036211803,0.0031907733,0.033219602,0.0011136711,0.000010314841,0.009419548,0.8307387],"study_design_scores_gemma":[0.0002718764,0.00003340117,0.042180263,0.0013878058,0.00008092269,0.0018048295,0.0014314539,0.9360089,0.00017673887,0.00071538554,0.01573733,0.00017110116],"about_ca_topic_score_codex":0.0011140861,"about_ca_topic_score_gemma":0.00024763081,"teacher_disagreement_score":0.9027893,"about_ca_system_score_codex":0.000013006882,"about_ca_system_score_gemma":0.00013829187,"threshold_uncertainty_score":0.3773907},"labels":[],"label_agreement":null},{"id":"W4392864560","doi":"10.1080/01431161.2024.2326534","title":"Characterizing post-fire northern boreal forest height dynamics","year":2024,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Taiga; Boreal; Environmental science; Remote sensing; Fire regime; Physical geography; Geography; Meteorology; Ecology; Forestry; Ecosystem","score_opus":0.00496521121648808,"score_gpt":0.2239683013471609,"score_spread":0.21900309013067282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392864560","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97989464,0.00010270372,0.009039432,0.0031771003,0.0036680386,0.00006631601,0.000010033702,0.00003773414,0.004004013],"genre_scores_gemma":[0.9944097,0.000021471253,0.0043982435,0.0002389611,0.00073689397,1.4494319e-8,0.000013917961,0.000031787324,0.000149012],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984838,0.000051140014,0.0004162565,0.00017012714,0.00069251104,0.00018621031],"domain_scores_gemma":[0.99935836,0.0001065495,0.0002251983,0.000109365435,0.00010564565,0.00009491269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040039638,0.00013962442,0.00016375483,0.00010840435,0.000047214122,0.0002042133,0.00030067825,0.000061982144,0.000047584625],"category_scores_gemma":[0.000103582,0.00011833935,0.0001592674,0.00011851364,0.00005166695,0.0004854512,0.00010819867,0.00029213872,0.00019598239],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044380686,0.000011954709,0.00447904,0.000012813642,0.000110669556,0.0017437061,0.0004475542,0.00019759816,0.010552879,0.000019584266,0.00017912193,0.9822007],"study_design_scores_gemma":[0.00026565767,0.0001204619,0.051219486,0.00076875236,0.000031729858,0.0060922727,0.00011224494,0.924893,0.000995121,0.0003842445,0.014889041,0.00022794737],"about_ca_topic_score_codex":0.0013553275,"about_ca_topic_score_gemma":0.0032327163,"teacher_disagreement_score":0.98197275,"about_ca_system_score_codex":0.0008404813,"about_ca_system_score_gemma":0.000037138976,"threshold_uncertainty_score":0.48257384},"labels":[],"label_agreement":null},{"id":"W4396897844","doi":"10.1080/01431161.2024.2347526","title":"Effect of shade on simultaneous estimation of non-photosynthetic and photosynthetic vegetation cover using the NDVI-NSSI normalized difference triangular space","year":2024,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":4,"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":"China Postdoctoral Science Foundation; National Natural Science Foundation of China; Ministry of Natural Resources","keywords":"Normalized Difference Vegetation Index; Photosynthesis; Cover (algebra); Vegetation (pathology); Vegetation cover; Remote sensing; Environmental science; Space (punctuation); Computer science; Mathematics; Atmospheric sciences; Geology; Botany; Ecology; Biology; Leaf area index; Land use; Medicine","score_opus":0.005250872958232961,"score_gpt":0.2594592653617358,"score_spread":0.25420839240350285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396897844","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.955321,0.00012099546,0.04304483,0.00027735074,0.0006544096,0.00019847041,0.0000026763178,0.000009273753,0.00037097136],"genre_scores_gemma":[0.98750865,0.000048107173,0.012267125,0.000034143486,0.000079863436,1.3614674e-8,0.0000013802302,0.00001741497,0.000043275722],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99809647,0.00021693854,0.00050689897,0.00018482073,0.0008591192,0.0001357852],"domain_scores_gemma":[0.9980603,0.0011411884,0.00051255204,0.00013979443,0.00009376517,0.000052434025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006769367,0.00018293907,0.00029620586,0.00013506913,0.000055263827,0.000080560196,0.0001995348,0.00008372061,0.000020720423],"category_scores_gemma":[0.00064696267,0.000111637295,0.0001534451,0.00019430985,0.00019825851,0.0001471384,0.000064539156,0.00026987575,0.00000692558],"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":[0.00025753267,0.000013325465,0.000017304574,0.000052870062,0.000081093945,0.00012027253,0.0008485363,0.18529059,0.55244505,0.0000036047256,0.00000909732,0.26086074],"study_design_scores_gemma":[0.00035846716,0.00019933868,0.0003373561,0.0012470594,0.00009691796,0.00087849126,0.000027235965,0.71121645,0.28534117,0.00015762047,0.000059314385,0.000080595295],"about_ca_topic_score_codex":0.00016042915,"about_ca_topic_score_gemma":0.000007976658,"teacher_disagreement_score":0.5259259,"about_ca_system_score_codex":0.00021237563,"about_ca_system_score_gemma":0.00002587508,"threshold_uncertainty_score":0.45524368},"labels":[],"label_agreement":null},{"id":"W4397044387","doi":"10.1080/01431161.2024.2349265","title":"Implementing robust outlier detection to enhance estimation accuracy of GNSS-IR based seasonal snow depth retrievals","year":2024,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":6,"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":"GNSS applications; Outlier; Snow; Remote sensing; Anomaly detection; Estimation; Environmental science; Computer science; Global Positioning System; Geography; Meteorology; Data mining; Artificial intelligence","score_opus":0.026399774307900022,"score_gpt":0.30470735746408195,"score_spread":0.2783075831561819,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4397044387","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.504797,0.00030215952,0.4919374,0.0012195678,0.001422427,0.00006240222,0.000020443984,0.000013893547,0.00022471767],"genre_scores_gemma":[0.89707655,0.000035674897,0.10222984,0.00018668597,0.00041363278,9.580591e-9,0.000015852802,0.0000049277746,0.000036834717],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985337,0.000040206203,0.0005029431,0.00013954907,0.0006077095,0.0001758481],"domain_scores_gemma":[0.9984712,0.0005219844,0.00030926097,0.000068233654,0.0005600393,0.00006927789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006687948,0.00009992309,0.00015320373,0.00014134898,0.00010324655,0.00013892133,0.00014435087,0.00003299939,0.00019215998],"category_scores_gemma":[0.0009527183,0.00008697969,0.00012178597,0.0003225513,0.000022466656,0.00031399,0.000019605888,0.00015326904,0.000014925252],"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":[0.000067034925,0.0000039559627,0.0019116587,0.0000138157075,0.00008699372,0.000032034965,0.00020537313,0.05320775,0.0013494233,0.000003014407,0.00018741288,0.94293153],"study_design_scores_gemma":[0.00015148934,0.00010036298,0.071866855,0.00050179503,0.000040959098,0.00010022209,0.00024964803,0.9073992,0.010715111,0.00023232824,0.008528339,0.000113736525],"about_ca_topic_score_codex":0.00042682025,"about_ca_topic_score_gemma":0.0013755334,"teacher_disagreement_score":0.9428178,"about_ca_system_score_codex":0.0000424909,"about_ca_system_score_gemma":0.00010973444,"threshold_uncertainty_score":0.3546929},"labels":[],"label_agreement":null},{"id":"W4399943303","doi":"10.1080/01431161.2024.2365813","title":"Automated traffic sign change detection using low-cost LiDAR scans and unsupervised machine learning","year":2024,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia; University of British Columbia","funders":"","keywords":"Computer science; Lidar; Sign (mathematics); Change detection; Artificial intelligence; Traffic sign; Unsupervised learning; Remote sensing; Machine learning; Geology","score_opus":0.013515597593380727,"score_gpt":0.24919485264875274,"score_spread":0.23567925505537202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399943303","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9150912,0.00093912694,0.07952418,0.00005696743,0.003989445,0.00006172706,0.000002688503,0.00028263434,0.00005200992],"genre_scores_gemma":[0.9916079,0.00023129796,0.0066217785,0.000022774519,0.0014654499,2.7748088e-8,0.0000022930342,0.00004225442,0.0000062397066],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913746,0.000026694432,0.00029532472,0.000103694285,0.00026431103,0.00017248787],"domain_scores_gemma":[0.99960274,0.000043275937,0.0000656402,0.00004241686,0.00018320669,0.00006271658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019956003,0.00014390414,0.0001539346,0.00038546877,0.00006497334,0.00018663959,0.000078591234,0.000070284375,0.0000046142472],"category_scores_gemma":[0.000041659907,0.00013399513,0.00007672335,0.00015509984,0.000022717133,0.00033764273,0.000019768973,0.0004255438,0.000002175673],"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":[0.000025439285,0.000001663338,0.00003306398,0.00005071449,0.00014276712,0.00044313207,0.0011464832,0.081587285,0.06795187,0.000002098075,0.000005116952,0.84861034],"study_design_scores_gemma":[0.0002462851,0.000019714498,0.00036069384,0.00094978925,0.00003268144,0.001983096,0.00013813104,0.982997,0.011728251,0.000024820445,0.0013916912,0.00012785797],"about_ca_topic_score_codex":0.00004693054,"about_ca_topic_score_gemma":0.000017366912,"teacher_disagreement_score":0.9014097,"about_ca_system_score_codex":0.00024733675,"about_ca_system_score_gemma":0.000022288128,"threshold_uncertainty_score":0.5464162},"labels":[],"label_agreement":null},{"id":"W4400202461","doi":"10.1080/01431161.2024.2367172","title":"Automatic detection and tracking polar lows from synthetic aperture radar and radiometer observations","year":2024,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Ionosphere and magnetosphere dynamics","field":"Physics and Astronomy","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":"Bedford Institute of Oceanography; Fisheries and Oceans Canada","funders":"National Natural Science Foundation of China","keywords":"Remote sensing; Synthetic aperture radar; Geology; Tracking (education); Radiometer; Polar; Radar; Geodesy; Computer science; Telecommunications; Physics","score_opus":0.009075548433191982,"score_gpt":0.23128018208518122,"score_spread":0.22220463365198925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400202461","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.779274,0.0007409766,0.21797998,0.0007458363,0.0010114373,0.000036644902,0.000013682755,0.000015532847,0.00018189283],"genre_scores_gemma":[0.95967376,0.000034760596,0.039504196,0.00008885028,0.00062774733,1.6479726e-8,0.000007196417,0.000015546766,0.000047953738],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933195,0.000035763154,0.0002542557,0.00011245899,0.00018130882,0.00008424716],"domain_scores_gemma":[0.9994043,0.0002566586,0.00011340997,0.00005174864,0.00012570719,0.000048151513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013788677,0.00009895463,0.00012932811,0.00007522496,0.00005457508,0.00025363214,0.00006172628,0.000035479094,0.00005153953],"category_scores_gemma":[0.00003336432,0.000086822,0.00007337299,0.0000712737,0.000028326082,0.00031462262,0.000023868371,0.00020311857,0.0000023488103],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001166836,0.0000059245185,0.000110523935,0.000007256808,0.00021594542,0.000038152866,0.0003658376,0.000020408104,0.01804078,0.00013232879,0.000028004042,0.9810232],"study_design_scores_gemma":[0.00046564534,0.00005591987,0.0038887204,0.0006612931,0.00015119115,0.00036087196,0.000505325,0.9661355,0.0012886899,0.012583747,0.013696539,0.00020654772],"about_ca_topic_score_codex":0.00026395242,"about_ca_topic_score_gemma":0.00002280504,"teacher_disagreement_score":0.9808166,"about_ca_system_score_codex":0.000039958486,"about_ca_system_score_gemma":0.000034487442,"threshold_uncertainty_score":0.35404986},"labels":[],"label_agreement":null},{"id":"W4400496942","doi":"10.1080/01431161.2024.2371083","title":"A machine learning-based framework for spatio-temporal extension and filling of SMOS surface soil moisture observations over Canada","year":2024,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"Canadian Space Agency; Trottier Institute for Sustainability in Engineering and Design","keywords":"Environmental science; Moisture; Extension (predicate logic); Water content; Remote sensing; Surface (topology); Hydrology (agriculture); Soil science; Computer science; Meteorology; Geology; Geography; Mathematics","score_opus":0.015200968177382725,"score_gpt":0.2532297448955456,"score_spread":0.2380287767181629,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400496942","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8290351,0.0006063589,0.16589506,0.002876456,0.0013463395,0.000084495456,0.000008528125,0.000014633095,0.00013303377],"genre_scores_gemma":[0.87947446,0.000040744664,0.11983525,0.0003284331,0.00021212758,4.91882e-9,0.000015576143,0.000020197453,0.00007318545],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986397,0.000044982935,0.00040984765,0.00018340656,0.0005828148,0.00013924969],"domain_scores_gemma":[0.99895257,0.00042419822,0.00029828984,0.00008486234,0.00016619585,0.0000738957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031489288,0.000135062,0.0001971811,0.00006715664,0.00008824054,0.0000665762,0.00010813613,0.000083104846,0.000013983557],"category_scores_gemma":[0.0003749593,0.00011334815,0.000107643034,0.00014342654,0.00006948615,0.00013470664,0.000046653677,0.0003491748,5.571153e-7],"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":[0.00045842715,0.000043729364,0.029034967,0.00011970373,0.00037995446,0.000688434,0.0012236077,0.51680034,0.029732402,0.00011531561,0.002599407,0.4188037],"study_design_scores_gemma":[0.00038244115,0.0000717558,0.027250376,0.00093534135,0.00006121939,0.00023534932,0.00010253147,0.94642556,0.0039294143,0.002310413,0.018124275,0.00017135577],"about_ca_topic_score_codex":0.12798035,"about_ca_topic_score_gemma":0.18383375,"teacher_disagreement_score":0.42962515,"about_ca_system_score_codex":0.00025279305,"about_ca_system_score_gemma":0.00015401273,"threshold_uncertainty_score":0.8778265},"labels":[],"label_agreement":null},{"id":"W4400889525","doi":"10.1080/01431161.2024.2377228","title":"Generating surface soil moisture at the 30 m resolution in grape-growing areas based on stacked ensemble learning","year":2024,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","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":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Environmental science; Water content; Moisture; Remote sensing; Surface (topology); Soil science; Geology; Meteorology; Geography; Mathematics","score_opus":0.009725774006570844,"score_gpt":0.2456714917741203,"score_spread":0.23594571776754944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400889525","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97479993,0.00034184573,0.01219925,0.0041223713,0.0015838604,0.000069961876,5.121915e-7,0.000032576812,0.0068496927],"genre_scores_gemma":[0.98847955,0.000043561282,0.009748505,0.0007673268,0.00050191133,6.245763e-9,0.0000037976436,0.000029517243,0.00042583328],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99782515,0.00023454256,0.0004627484,0.00026049212,0.0009601997,0.00025686543],"domain_scores_gemma":[0.9991053,0.00040036681,0.00022489745,0.00013140001,0.000071333554,0.00006670302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001022167,0.00018182573,0.00017733162,0.00014228531,0.00020639802,0.00019382231,0.00019967783,0.000094967865,0.00003799624],"category_scores_gemma":[0.00029145487,0.00012969984,0.00017284366,0.00026566547,0.000093546114,0.00023881697,0.000106516454,0.0007505009,0.000055125012],"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":[0.000070315196,0.000009126918,0.00071170524,0.000004431694,0.000033086733,0.0006808346,0.0005964408,0.67409533,0.08005729,0.0000035549706,0.000550535,0.24318731],"study_design_scores_gemma":[0.00039264016,0.00006224771,0.00455277,0.0007525183,0.00002457871,0.00061235466,0.0003339691,0.97420746,0.010834041,0.00025348517,0.007797682,0.00017622599],"about_ca_topic_score_codex":0.0008197937,"about_ca_topic_score_gemma":0.0015002337,"teacher_disagreement_score":0.30011213,"about_ca_system_score_codex":0.0009255391,"about_ca_system_score_gemma":0.000050327988,"threshold_uncertainty_score":0.5289006},"labels":[],"label_agreement":null},{"id":"W4403131955","doi":"10.1080/01431161.2024.2394237","title":"Angular variation of SAR polarimetric parameters over multiyear ice","year":2024,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Remote sensing; Variation (astronomy); Polarimetry; Environmental science; Sea ice; Synthetic aperture radar; Geology; Climatology; Geodesy; Physics; Astronomy","score_opus":0.008598369862025801,"score_gpt":0.253159567058293,"score_spread":0.2445611971962672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403131955","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.090088174,0.00094687735,0.90690786,0.00021837847,0.001013102,0.00005027099,0.000007027952,0.000073795156,0.00069453166],"genre_scores_gemma":[0.48936197,0.000101154175,0.5103015,0.000022691858,0.00018599001,5.4870086e-9,0.0000016624375,0.000015818865,0.000009242248],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999103,0.000017891152,0.00036277834,0.00008165308,0.00035067703,0.000083994055],"domain_scores_gemma":[0.9993836,0.00016285646,0.00010461034,0.00010037405,0.00021291866,0.000035595047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024136915,0.000092685405,0.00014093694,0.0004748577,0.00001381873,0.000052086296,0.00014304208,0.00006890536,0.0000157799],"category_scores_gemma":[0.00009773656,0.00008476852,0.00012490325,0.00024111905,0.000021361944,0.00014023944,0.000016936146,0.0001866469,0.0000045311795],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008599595,0.000008346621,0.000012222779,0.000018357041,0.00023603716,0.000042792406,0.00020745922,0.00014681177,0.019990725,0.0003386944,0.0002675079,0.97872245],"study_design_scores_gemma":[0.00034037168,0.000058793106,0.0029673995,0.0007305648,0.00012979713,0.00091324677,0.00006730654,0.5622188,0.09828637,0.0037247434,0.33030447,0.00025814516],"about_ca_topic_score_codex":0.00015632765,"about_ca_topic_score_gemma":0.0000019055293,"teacher_disagreement_score":0.9784643,"about_ca_system_score_codex":0.00013758609,"about_ca_system_score_gemma":0.000027377266,"threshold_uncertainty_score":0.345676},"labels":[],"label_agreement":null},{"id":"W4403686227","doi":"10.1080/01431161.2024.2399326","title":"Drone-based infrared thermography to measure the intranasal temperature of baleen whales","year":2024,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":5,"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":"Fisheries and Oceans Canada","keywords":"Baleen; Thermography; Measure (data warehouse); Remote sensing; Drone; Environmental science; Infrared; Geology; Computer science; Biology; Physics; Optics; Ecology; Whale","score_opus":0.012723214044084597,"score_gpt":0.24870797249605744,"score_spread":0.23598475845197284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403686227","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9706443,0.0013765831,0.00602568,0.0111784935,0.0011764928,0.00016934049,0.000010831892,0.000022504139,0.009395759],"genre_scores_gemma":[0.9914564,0.0000960455,0.0072568357,0.00086696533,0.00024561642,2.8844632e-8,8.221336e-7,0.000013126749,0.00006417476],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985705,0.00006819279,0.0003377103,0.00012432937,0.0007803811,0.00011889393],"domain_scores_gemma":[0.9994594,0.00011832009,0.00013400753,0.00010739156,0.00012380058,0.00005707668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000464844,0.00011261867,0.00015621651,0.00010200401,0.000048629056,0.00008317227,0.00037186808,0.000038963735,0.00016585978],"category_scores_gemma":[0.00010467256,0.00007212847,0.00018789884,0.0002950033,0.00010028923,0.00013202985,0.00012892345,0.00024377748,0.000014674172],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020838129,0.00002635031,0.0022328647,0.00002632104,0.00041439192,0.00028561254,0.0010566288,0.0018397176,0.046505284,0.00006732857,0.01001166,0.9373255],"study_design_scores_gemma":[0.0021855591,0.0010419195,0.2385767,0.005433393,0.00045791842,0.0017112042,0.0014982942,0.03984801,0.05543116,0.0060686357,0.6465974,0.0011498438],"about_ca_topic_score_codex":0.00031942956,"about_ca_topic_score_gemma":0.00037966642,"teacher_disagreement_score":0.93617564,"about_ca_system_score_codex":0.000117214026,"about_ca_system_score_gemma":0.000031638734,"threshold_uncertainty_score":0.29413134},"labels":[],"label_agreement":null},{"id":"W4403686239","doi":"10.1080/01431161.2024.2416591","title":"Observed response of microwave land surface emissivity to antecedent rainfall in Hainan Island","year":2024,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Soil Moisture and Remote Sensing","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":"Université du Québec en Abitibi-Témiscamingue","funders":"Natural Science Foundation of Anhui Province; National Natural Science Foundation of China","keywords":"Environmental science; Vegetation (pathology); Microwave; Emissivity; Hydrology (agriculture); Magnitude (astronomy); Atmospheric sciences; Geology","score_opus":0.015419587689367334,"score_gpt":0.266911806569967,"score_spread":0.25149221888059964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403686239","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98567057,0.00019531876,0.008876387,0.00266755,0.0010164382,0.00006430359,0.0000019607876,0.000008908821,0.0014985414],"genre_scores_gemma":[0.980216,0.0000329,0.019158931,0.00018407403,0.00012975186,1.3838287e-9,8.010116e-7,0.000014749819,0.00026278096],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9985136,0.00013824852,0.0004710792,0.00017711875,0.0005319482,0.00016801264],"domain_scores_gemma":[0.99930614,0.0002439545,0.00016751993,0.00010957831,0.00008160483,0.00009122112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010681489,0.000121914265,0.00021468657,0.00017029406,0.000022807024,0.00006889895,0.00019436065,0.00006504934,0.00001391636],"category_scores_gemma":[0.00033039608,0.00010125426,0.000121363846,0.00023648271,0.00006239033,0.00014054072,0.000117594085,0.00024093631,0.000016187543],"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":[0.0010281267,0.000027656552,0.0075869625,0.0000133658095,0.00007883721,0.0022247056,0.0029093542,0.0067078723,0.72686327,0.0000020099217,0.0007918231,0.25176603],"study_design_scores_gemma":[0.0024068404,0.00049775984,0.54278713,0.004871744,0.000079410565,0.0065810177,0.00087644305,0.111899026,0.29856753,0.0015606021,0.029125277,0.00074719626],"about_ca_topic_score_codex":0.0010700931,"about_ca_topic_score_gemma":0.0032063872,"teacher_disagreement_score":0.5352002,"about_ca_system_score_codex":0.000338197,"about_ca_system_score_gemma":0.00005245523,"threshold_uncertainty_score":0.4129029},"labels":[],"label_agreement":null},{"id":"W4404717224","doi":"10.1080/01431161.2024.2412807","title":"Preface: the role of space ocean science and technology towards sustainable development goal","year":2024,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Space exploration and regulation","field":"Physics and Astronomy","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":"Fisheries and Oceans Canada","funders":"","keywords":"Space (punctuation); Sustainable development; Environmental resource management; Computer science; Environmental science; Ecology; Biology","score_opus":0.005092164426539017,"score_gpt":0.2517397935808934,"score_spread":0.24664762915435437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404717224","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95457405,0.00043966662,0.033807363,0.007919067,0.0004984441,0.000067682005,6.666787e-7,0.000011579599,0.0026814744],"genre_scores_gemma":[0.9911883,0.0000055722967,0.0083080735,0.0000087250255,0.0001744686,9.002748e-9,5.4377665e-7,0.000004554095,0.0003097379],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99927634,0.000010152437,0.00017571695,0.00007208441,0.00037870614,0.00008700625],"domain_scores_gemma":[0.9987937,0.000018757904,0.0001248517,0.000046573252,0.000990919,0.000025203492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041277584,0.00005114847,0.000067071036,0.0002893162,0.00007840081,0.00011652106,0.00012982663,0.000016518345,0.0000066448783],"category_scores_gemma":[0.000037291018,0.00003434454,0.000022912604,0.00027374123,0.00014814068,0.0002258457,0.00006982894,0.00010994134,0.0000010543187],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014227011,0.000008700304,0.00041596516,0.000005626209,0.000095110816,0.000013351355,0.0022384454,0.0002060321,0.021924723,0.029527228,0.00013359469,0.945417],"study_design_scores_gemma":[0.00056125,0.00007767655,0.0023997126,0.0005128241,0.00004230248,0.00029929756,0.037552513,0.11835472,0.5740876,0.06732328,0.19857086,0.00021796067],"about_ca_topic_score_codex":0.00002505675,"about_ca_topic_score_gemma":0.0000010133618,"teacher_disagreement_score":0.945199,"about_ca_system_score_codex":0.00008129216,"about_ca_system_score_gemma":0.00048299594,"threshold_uncertainty_score":0.14005297},"labels":[],"label_agreement":null},{"id":"W4409668309","doi":"10.1080/01431161.2025.2492412","title":"Forest aboveground biomass estimation using deep learning data fusion of ALS, multispectral, and topographic data","year":2025,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":8,"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; University of British Columbia","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Multispectral image; Remote sensing; Biomass (ecology); Environmental science; Sensor fusion; Estimation; Multispectral pattern recognition; Hyperspectral imaging; Fusion; Computer science; Geology; Artificial intelligence","score_opus":0.030627618199692785,"score_gpt":0.3202754772986386,"score_spread":0.2896478590989458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409668309","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6299987,0.00022634381,0.3685431,0.00057983346,0.0002700102,0.000056666948,0.000007195415,0.000008957069,0.00030921825],"genre_scores_gemma":[0.7445746,0.00017713633,0.25507143,0.000043276657,0.0000615654,1.7489924e-9,0.000045915458,0.000007552909,0.000018521761],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986807,0.000066015484,0.00046312832,0.00025426675,0.0004171069,0.00011874279],"domain_scores_gemma":[0.9988331,0.00013040812,0.00046272652,0.00042963377,0.00009484385,0.00004928281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006064142,0.00010533436,0.00016684676,0.00020938476,0.00012210949,0.00008876468,0.0005772224,0.00005671063,0.000009129223],"category_scores_gemma":[0.0002963471,0.000099276745,0.000036748414,0.0002597731,0.00015949184,0.00061208376,0.0006067535,0.00019693717,0.0000018572251],"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":[0.000049318798,0.000032020296,0.003347096,0.000012992346,0.00011321214,0.000027148888,0.00016832876,0.010111441,0.0950629,0.000056076955,0.00014405222,0.8908754],"study_design_scores_gemma":[0.0002909565,0.0000183166,0.012207213,0.0002238149,0.000061569575,0.00032484144,0.00013293014,0.98116994,0.0012870703,0.0012990673,0.0028990447,0.00008524338],"about_ca_topic_score_codex":0.0014144639,"about_ca_topic_score_gemma":0.0003625969,"teacher_disagreement_score":0.9710585,"about_ca_system_score_codex":0.000088421184,"about_ca_system_score_gemma":0.000028968641,"threshold_uncertainty_score":0.4048388},"labels":[],"label_agreement":null},{"id":"W4410039264","doi":"10.1080/01431161.2025.2495996","title":"Upscaling UAV and Lidar-derived forest gap area and edge length extractions using radar and optical sentinel images","year":2025,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Remote sensing; Lidar; Radar; Enhanced Data Rates for GSM Evolution; Environmental science; Geology; Computer science; Computer vision; Telecommunications","score_opus":0.01981334671451147,"score_gpt":0.2839423168472606,"score_spread":0.26412897013274916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410039264","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87674,0.00017902485,0.11767242,0.002478901,0.00022900323,0.000055560442,0.0000015473557,0.000011789132,0.0026317642],"genre_scores_gemma":[0.88229144,0.00020617437,0.11711277,0.00015240001,0.00012015978,6.899147e-9,0.0000011620958,0.000009988498,0.00010592812],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901754,0.000037188816,0.00033598416,0.00020184579,0.00026143988,0.00014600926],"domain_scores_gemma":[0.9993573,0.00016782155,0.00019022299,0.000091878435,0.0000881521,0.00010464489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026770882,0.00012699806,0.00016953991,0.00014111528,0.0001994975,0.00018793138,0.00008130717,0.000060755643,0.0000069818293],"category_scores_gemma":[0.00015337035,0.00011883638,0.00004941252,0.00010233141,0.0002632898,0.00023624704,0.00013948478,0.00024412846,0.0000014281711],"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":[0.00007871493,0.000040511994,0.0048185983,0.0000135341825,0.00016022008,0.00012551308,0.00044064096,0.0009259409,0.4010744,0.00013979348,0.000285302,0.59189683],"study_design_scores_gemma":[0.0030913528,0.000083519095,0.2035539,0.0013586521,0.0004641213,0.013985983,0.0020345908,0.7099359,0.037161067,0.0130210435,0.014496031,0.0008138185],"about_ca_topic_score_codex":0.00016047564,"about_ca_topic_score_gemma":0.000033232867,"teacher_disagreement_score":0.70900995,"about_ca_system_score_codex":0.00009517191,"about_ca_system_score_gemma":0.000025799856,"threshold_uncertainty_score":0.4846007},"labels":[],"label_agreement":null},{"id":"W4411008304","doi":"10.1080/01431161.2025.2512162","title":"Evaluation of SWOT’s performance for river water level retrieval in the Yangtze River Basin","year":2025,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Novelis (Canada)","funders":"Key Technologies Research and Development Program; National Natural Science Foundation of China","keywords":"SWOT analysis; Yangtze river; Water resource management; Environmental science; Structural basin; Hydrology (agriculture); Drainage basin; Environmental resource management; China; Geography; Geology; Business; Cartography; Geomorphology","score_opus":0.03692571000256626,"score_gpt":0.3113961009088313,"score_spread":0.274470390906265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411008304","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97011256,0.000017499506,0.023975391,0.0020013214,0.00067520514,0.00018148641,0.0000014305533,0.0000017254573,0.0030333635],"genre_scores_gemma":[0.982264,0.00002986729,0.01714649,0.000269601,0.000069160335,4.8783217e-8,0.0000022722058,0.000003614403,0.00021498119],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984059,0.000098299955,0.00031103875,0.00008684006,0.0009948547,0.00010309637],"domain_scores_gemma":[0.9994747,0.00006091915,0.00015984826,0.000082056,0.00021089353,0.000011582938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027113433,0.00006566969,0.00009728442,0.00012694334,0.000037410773,0.000023281773,0.00025510977,0.00002685744,0.000044208853],"category_scores_gemma":[0.00009720694,0.000041249084,0.000074166994,0.00009115539,0.0000754707,0.00021824628,0.000078207115,0.00009616977,0.000005566016],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035263496,0.00008224041,0.003261471,0.000014509241,0.00018381649,0.000014603161,0.0031776123,0.020303693,0.01206351,0.000097464675,0.0026200216,0.9578284],"study_design_scores_gemma":[0.0040504336,0.00017590405,0.28203002,0.00038519557,0.0003218202,0.000048728332,0.00062217255,0.63891876,0.041946817,0.013182346,0.018128252,0.00018956745],"about_ca_topic_score_codex":0.00017990997,"about_ca_topic_score_gemma":0.00006783311,"teacher_disagreement_score":0.95763886,"about_ca_system_score_codex":0.00029018783,"about_ca_system_score_gemma":0.000029000988,"threshold_uncertainty_score":0.1682089},"labels":[],"label_agreement":null},{"id":"W4411451452","doi":"10.1080/01431161.2025.2521072","title":"Tree species proportion prediction using airborne laser scanning and Sentinel-2 data within a deep learning based dual-stream data fusion approach","year":2025,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal; Natural Resources Canada; University of British Columbia","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Remote sensing; Laser scanning; Sensor fusion; Environmental science; Tree (set theory); Dual (grammatical number); Fusion; Computer science; Geology; Artificial intelligence; Laser; Mathematics","score_opus":0.03225080861585989,"score_gpt":0.28084635443367684,"score_spread":0.24859554581781695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411451452","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5640504,0.00006250844,0.4321842,0.00077817356,0.0003966762,0.000110974455,0.000013193591,0.000033784596,0.0023701163],"genre_scores_gemma":[0.8188385,0.00004117249,0.18027285,0.0001149557,0.0002851035,1.0685557e-8,0.00023739552,0.000016285856,0.0001937464],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980523,0.00012290751,0.0005770814,0.0004202076,0.0006653534,0.00016214848],"domain_scores_gemma":[0.99865955,0.000071710194,0.0005791425,0.00047816386,0.00013641301,0.00007501476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011032511,0.00015544074,0.00018820529,0.00021277706,0.000238209,0.00017484906,0.00041893413,0.000075303746,0.000015269823],"category_scores_gemma":[0.0003832682,0.00014293451,0.000041071682,0.00028221146,0.0001536762,0.00060540624,0.00061816897,0.0003642096,0.0000027435347],"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":[0.00017921288,0.00010406897,0.0065358547,0.000024729292,0.00017207721,0.00009033979,0.00053757237,0.08900238,0.064086094,0.000008526109,0.0010689887,0.83819014],"study_design_scores_gemma":[0.00050497503,0.00001657513,0.009921523,0.00026480094,0.00009303049,0.00045224393,0.00048016876,0.9822556,0.001813656,0.00010608733,0.003978653,0.000112707945],"about_ca_topic_score_codex":0.00020021616,"about_ca_topic_score_gemma":0.00006637276,"teacher_disagreement_score":0.8932532,"about_ca_system_score_codex":0.00017122117,"about_ca_system_score_gemma":0.00006653442,"threshold_uncertainty_score":0.58287007},"labels":[],"label_agreement":null},{"id":"W4412526379","doi":"10.1080/01431161.2025.2524082","title":"A review on aboveground biomass estimation methods utilizing forest structural characteristics","year":2025,"lang":"en","type":"review","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Biomass (ecology); Estimation; Environmental science; Remote sensing; Forestry; Computer science; Geography; Ecology; Biology; Engineering","score_opus":0.043512604818295234,"score_gpt":0.40443747973161004,"score_spread":0.3609248749133148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412526379","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000033286487,0.90044373,0.093372345,0.00039291522,0.0020619486,0.00039017486,0.000018866942,0.000028180126,0.0032585282],"genre_scores_gemma":[0.00003892805,0.8015971,0.19748075,0.0003582986,0.00030514947,5.5898695e-8,0.000053917516,0.000030747928,0.00013501503],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970332,0.00036173113,0.0013814375,0.0003375997,0.0006610517,0.00022498428],"domain_scores_gemma":[0.99682087,0.0005894642,0.0019391765,0.0003605545,0.00017838385,0.00011153007],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009466034,0.0003952566,0.0011807623,0.0003195249,0.00012141425,0.00015099187,0.00058721536,0.00018771643,0.000045117653],"category_scores_gemma":[0.0009172312,0.00031294298,0.00063586223,0.000420639,0.00010634727,0.00015860533,0.00016959892,0.0006303037,0.00006687954],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000064415703,0.0000070893752,5.394076e-7,0.0019525555,0.00013328138,0.00004320213,0.000019454314,0.000019197334,0.000009435152,0.000018904067,0.0007767948,0.9970131],"study_design_scores_gemma":[0.00011280402,0.000030800435,0.00002470447,0.09389968,0.0005819973,0.0015241248,0.0000058175806,0.008393557,0.00000897328,0.00060571753,0.8945365,0.000275334],"about_ca_topic_score_codex":0.000082737395,"about_ca_topic_score_gemma":0.000009968822,"teacher_disagreement_score":0.9967378,"about_ca_system_score_codex":0.0007827925,"about_ca_system_score_gemma":0.00015198295,"threshold_uncertainty_score":0.9999323},"labels":[],"label_agreement":null},{"id":"W4412865079","doi":"10.1080/01431161.2025.2528256","title":"Near-real time monitoring of burned area at global scale based on deep learning","year":2025,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Theratechnologies (Canada)","funders":"","keywords":"Scale (ratio); Remote sensing; Deep learning; Environmental science; Computer science; Meteorology; Geology; Artificial intelligence; Cartography; Geography","score_opus":0.005150472560609071,"score_gpt":0.24246072891760564,"score_spread":0.23731025635699657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412865079","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9793901,0.000016739139,0.006763444,0.0003091416,0.0010837899,0.000056682762,0.000001540132,0.000015631018,0.012362948],"genre_scores_gemma":[0.98195875,0.000006139365,0.017596627,0.00004477328,0.00012321447,1.2847658e-8,0.0000012781334,0.000008733627,0.00026044998],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984332,0.00011076339,0.00040967896,0.00014696801,0.0007435048,0.00015588335],"domain_scores_gemma":[0.9991175,0.00019220568,0.00041569697,0.0001069727,0.00010040216,0.000067229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045748416,0.00011876545,0.00020976202,0.00007953572,0.00008039425,0.000051492596,0.0002537437,0.000060554372,0.000085622225],"category_scores_gemma":[0.0002080169,0.00011086423,0.00013384565,0.00017007407,0.00007123068,0.00012226147,0.00010989825,0.00017843214,0.00006214482],"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":[0.00061453256,0.00006385014,0.16845557,0.000016562448,0.00013476235,0.00026891768,0.0002018669,0.17081702,0.1161793,0.0000024307844,0.0002208832,0.5430243],"study_design_scores_gemma":[0.000836518,0.00013387305,0.082929805,0.00074922224,0.0000251014,0.00014818818,0.000034713838,0.89471716,0.019279666,0.00007496657,0.00095631636,0.0001144455],"about_ca_topic_score_codex":0.00043719186,"about_ca_topic_score_gemma":0.00004004748,"teacher_disagreement_score":0.72390014,"about_ca_system_score_codex":0.0011142634,"about_ca_system_score_gemma":0.000028276543,"threshold_uncertainty_score":0.45209122},"labels":[],"label_agreement":null},{"id":"W4415644801","doi":"10.1080/01431161.2025.2580780","title":"Managing methane concentrations in western Canada: climate actions towards a net-zero target","year":2025,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Atmospheric and Environmental Gas Dynamics","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":"Memorial University of Newfoundland","funders":"","keywords":"Methane; Methane emissions; Climate change; Greenhouse gas; Global warming","score_opus":0.00876718224040734,"score_gpt":0.2497699382728134,"score_spread":0.24100275603240606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415644801","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64938474,0.00006666865,0.33608907,0.005259264,0.00143858,0.00007787434,0.0000032326907,0.000008864639,0.0076717236],"genre_scores_gemma":[0.9254052,0.00018596202,0.072987825,0.00093484635,0.000047715017,3.3746115e-8,0.0000030140918,0.000008116935,0.0004273046],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885404,0.000036724363,0.00038472246,0.0001261775,0.00040313616,0.00019518011],"domain_scores_gemma":[0.9996041,0.000032492997,0.00019769439,0.000082073806,0.000021047745,0.000062572224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020207759,0.000103927116,0.00014872682,0.00003240573,0.000052455303,0.00003356616,0.00020867943,0.00003620513,0.00006887743],"category_scores_gemma":[0.000030339172,0.00010268765,0.00005707377,0.00015082913,0.000068719484,0.00020632231,0.00012643123,0.00021248107,0.0000042685597],"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":[0.00010022923,0.000047163412,0.06261192,0.000006099889,0.000137524,0.00058968127,0.00036167869,0.50712967,0.0034979428,0.00007884418,0.0005252559,0.42491397],"study_design_scores_gemma":[0.0022339604,0.00006652939,0.28947067,0.0005076445,0.00009365352,0.0008521526,0.0013811105,0.6181257,0.002607767,0.0061246045,0.07801382,0.00052239],"about_ca_topic_score_codex":0.09200858,"about_ca_topic_score_gemma":0.09841483,"teacher_disagreement_score":0.4243916,"about_ca_system_score_codex":0.0013568009,"about_ca_system_score_gemma":0.00009902287,"threshold_uncertainty_score":0.91803676},"labels":[],"label_agreement":null},{"id":"W4415938325","doi":"10.1080/01431161.2025.2583600","title":"Enhancing tree species composition mapping using Sentinel-2 and multi-seasonal deep learning fusion","year":2025,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Wood and Agarwood Research","field":"Chemistry","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":"Ontario Forest Research Institute; University of British Columbia","funders":"","keywords":"Deep learning; Fusion; Sensor fusion; Pattern recognition (psychology); Tree (set theory)","score_opus":0.024793312481480295,"score_gpt":0.3014829452476513,"score_spread":0.276689632766171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415938325","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8762014,0.00046560602,0.1205233,0.0005646615,0.00034096677,0.000019488389,8.6056207e-7,0.000017503231,0.0018662461],"genre_scores_gemma":[0.950562,0.00010042037,0.04801348,0.00006127803,0.00061685237,7.021708e-9,0.000007324975,0.000013102421,0.0006255302],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986072,0.000052398507,0.00044445347,0.00016089689,0.00053735205,0.00019769877],"domain_scores_gemma":[0.99881494,0.00015570836,0.00030668604,0.00006257925,0.0005872396,0.0000728497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034796758,0.00013265664,0.00020506374,0.0003234771,0.00019321419,0.00020065934,0.00015419151,0.000077635515,0.000034392422],"category_scores_gemma":[0.00020228454,0.00012733928,0.000120156416,0.00011892504,0.00006142151,0.00018127948,0.0001574092,0.00052241236,0.0000018315337],"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":[0.00006285865,0.000017208235,0.0014660987,0.000041610252,0.00013106801,0.0001905027,0.00025618708,0.00028303108,0.85608786,0.000010559044,0.000008821602,0.1414442],"study_design_scores_gemma":[0.0018397998,0.000013827843,0.004569547,0.0035838345,0.000051681272,0.0013446392,0.0019330701,0.58299327,0.40137383,0.00017271533,0.001901502,0.00022230546],"about_ca_topic_score_codex":0.000024204146,"about_ca_topic_score_gemma":0.000014606319,"teacher_disagreement_score":0.5827102,"about_ca_system_score_codex":0.0001903489,"about_ca_system_score_gemma":0.00007322172,"threshold_uncertainty_score":0.51927453},"labels":[],"label_agreement":null},{"id":"W4416296533","doi":"10.1080/01431161.2025.2574517","title":"ACIX-III Aqua: evaluation of atmospheric correction for hyperspectral PRISMA imagery over inland and coastal waters","year":2025,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Marine and coastal ecosystems","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 Ottawa","funders":"Agenzia Spaziale Italiana; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; European Space Agency","keywords":"Hyperspectral imaging; Atmospheric correction; Multispectral image; Satellite; Radiometer; In situ; Satellite imagery; Reference data","score_opus":0.010992547355954015,"score_gpt":0.2514862098896618,"score_spread":0.2404936625337078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416296533","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97125524,0.00015876378,0.022675274,0.00025497502,0.0027034977,0.000107427695,0.000008087273,0.0000040787895,0.0028326607],"genre_scores_gemma":[0.99282223,0.000043052172,0.0066304547,0.000056138575,0.00021271184,1.0738836e-8,0.00001364659,0.0000021692115,0.00021960969],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902576,0.000055914337,0.00034018006,0.000093281786,0.00039323044,0.00009160373],"domain_scores_gemma":[0.998954,0.000118651544,0.00028073418,0.00004267447,0.0005699778,0.00003394198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006832289,0.0000733979,0.00015284338,0.00006878365,0.00004175439,0.000056987803,0.000071786475,0.000036021418,0.000029241422],"category_scores_gemma":[0.00013164268,0.000060728038,0.00007423394,0.000080027625,0.00002979115,0.00020284414,0.000012404707,0.00008560437,6.635094e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033099152,0.000007229449,0.019090801,0.0000157315,0.00013716349,0.000008337341,0.00020092097,0.0025301806,0.0008345979,0.0000055854284,0.0006323094,0.9762061],"study_design_scores_gemma":[0.0015800509,0.00014026799,0.075121365,0.00020723304,0.00009777016,0.00032193982,0.0006189944,0.9174911,0.001327182,0.0011160685,0.0018868406,0.00009117957],"about_ca_topic_score_codex":0.0014933263,"about_ca_topic_score_gemma":0.00060085265,"teacher_disagreement_score":0.976115,"about_ca_system_score_codex":0.000019874149,"about_ca_system_score_gemma":0.000112264854,"threshold_uncertainty_score":0.24764176},"labels":[],"label_agreement":null},{"id":"W7117292933","doi":"10.1080/01431161.2025.2603691","title":"GMFE-Net: point cloud semantic segmentation with general multi-feature fusion and extraction","year":2025,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"3D Shape Modeling and Analysis","field":"Engineering","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 Waterloo","funders":"Major Discipline Academic and Technical Leaders Training Program of Jiangxi Province; Natural Science Foundation of Jiangxi Province; National Natural Science Foundation of China; Double Thousand Plan of Jiangxi Province","keywords":"Segmentation; Fusion; Point cloud; Extraction (chemistry); Point (geometry); Cloud computing; Pattern recognition (psychology); Class (philosophy)","score_opus":0.007482525580005695,"score_gpt":0.2585447667251928,"score_spread":0.2510622411451871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117292933","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4533263,0.0002382737,0.54526156,0.0005035355,0.0005285942,0.000018697378,6.970841e-7,0.000017361586,0.000104959465],"genre_scores_gemma":[0.8833169,0.0002711694,0.115812145,0.00009271889,0.0002876697,9.850662e-9,0.000004745915,0.000010983825,0.00020367837],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993687,0.000019957448,0.00021998573,0.00008224206,0.00022844046,0.00008066922],"domain_scores_gemma":[0.999535,0.000024586396,0.00010177157,0.000049749826,0.00025496422,0.00003391698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014110409,0.000095405165,0.00012861026,0.00027057575,0.000042151456,0.00007798655,0.00005562391,0.000048631828,0.0000031202944],"category_scores_gemma":[0.000019303277,0.000079598816,0.000056562367,0.000094465286,0.000013426921,0.00015434454,0.000012864195,0.00020227305,9.861883e-7],"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":[0.00010032443,0.000016283597,0.00026307523,0.000033717693,0.00056368596,0.00017664192,0.0004678818,0.2890848,0.14446367,0.000010624942,0.0005311307,0.56428814],"study_design_scores_gemma":[0.000617403,0.000017993043,0.0006893666,0.00037369362,0.000081887694,0.00042308442,0.00017389582,0.9871914,0.009936755,0.00012967114,0.00028129652,0.00008354779],"about_ca_topic_score_codex":0.000037224483,"about_ca_topic_score_gemma":0.00004209832,"teacher_disagreement_score":0.6981066,"about_ca_system_score_codex":0.000103141436,"about_ca_system_score_gemma":0.000018323715,"threshold_uncertainty_score":0.32459456},"labels":[],"label_agreement":null}]}