{"meta":{"query_hash":"b5923592a998","filters":{"venue":"Advances in Remote Sensing"},"cohort_total":10,"direct_labels_cover":0,"predictions_cover":10,"exported":10,"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/b5923592a998","api":"https://metacan.xera.ac/api/v1/cohort?venue=Advances+in+Remote+Sensing"},"results":[{"id":"W2041554075","doi":"10.4236/ars.2013.22012","title":"Internet-Based Spectral Database for Different Land Covers in Egypt","year":2013,"lang":"en","type":"article","venue":"Advances in 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":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Database; Metadata; Remote sensing; Land cover; Computer science; Spectroradiometer; Hyperspectral imaging; The Internet; Spectral signature; Vegetation (pathology); NetCDF; Land use; Geography; Reflectivity; World Wide Web; Engineering","score_opus":0.009446042890642933,"score_gpt":0.2378195304841502,"score_spread":0.22837348759350726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041554075","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1283135,0.0010480629,0.03279804,0.00023121183,0.000052059273,0.0006909211,0.7968432,0.015936082,0.024086965],"genre_scores_gemma":[0.107275106,0.0006241579,0.028950484,0.00007277088,0.00001921536,0.00038060662,0.85489017,0.00057465525,0.0072128223],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997466,0.000019697629,0.00006378491,0.00006016557,0.00007293307,0.000036796577],"domain_scores_gemma":[0.9990946,0.00007154029,0.00010662907,0.00024910763,0.00039176233,0.00008639699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045689283,0.0005472422,0.00054260914,0.006399808,0.00037001833,0.0011010492,0.0010259442,0.00042402645,0.013012233],"category_scores_gemma":[0.0011168651,0.00022771652,0.00033066334,0.008221758,0.00012848891,0.0016797355,0.00090810674,0.0002960508,0.009969107],"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":[0.0019530427,0.0006908937,0.0694648,0.0026845403,0.00027373614,0.0017941718,0.0012738785,0.009649977,0.036445703,0.00821999,0.2879125,0.5796369],"study_design_scores_gemma":[0.00028154204,0.00012319184,0.18322124,0.00031081148,0.00021552535,0.0014052336,0.0020961384,0.019476535,0.038460236,0.0049745347,0.74921507,0.00021997205],"about_ca_topic_score_codex":0.008064936,"about_ca_topic_score_gemma":0.006755524,"teacher_disagreement_score":0.013012233,"about_ca_system_score_codex":0.00067232654,"about_ca_system_score_gemma":0.00086042215,"threshold_uncertainty_score":0.043530226},"labels":[],"label_agreement":null},{"id":"W2118417501","doi":"10.4236/ars.2013.22020","title":"Monitoring Wheat and Rapeseed by Using Synchronous Optical and Radar Satellite Data—From Temporal Signatures to Crop Parameters Estimation","year":2013,"lang":"en","type":"article","venue":"Advances in Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":119,"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":"Japan Aerospace Exploration Agency; Canadian Space Agency; Centre National d’Etudes Spatiales; European Space Agency","keywords":"Remote sensing; Satellite; Radar; Environmental science; C band; Normalized Difference Vegetation Index; Microwave; Backscatter (email); Geology; Physics; Climate change; Computer science","score_opus":0.012647926432388856,"score_gpt":0.267942607145278,"score_spread":0.25529468071288913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118417501","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9942514,0.00018039792,0.004985005,0.000012465893,0.0000047364947,0.00000727943,0.00010918057,0.000024146666,0.00042546738],"genre_scores_gemma":[0.9957354,0.00013357043,0.0037022259,0.0000073981687,0.000011799882,0.0000068866966,0.00027013713,0.0000040769264,0.00012857317],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998455,0.000028160652,0.000009782103,0.000052734395,0.000045272034,0.000018474617],"domain_scores_gemma":[0.99971074,0.000079344085,0.00009498717,0.000036378446,0.000054886714,0.000023643566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033116873,0.0002639196,0.00015670808,0.00074473163,0.000079250414,0.0003071908,0.00012949838,0.0002171161,0.0001764996],"category_scores_gemma":[0.00058231974,0.0001367506,0.00020339285,0.00044805201,0.0000825125,0.00027550547,0.00024083935,0.00007900992,0.00007103959],"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":[0.00095012,0.0002865813,0.38525707,0.00017086182,0.00033498995,0.00029017244,0.0001945311,0.029946454,0.39835966,0.00022951726,0.0002517182,0.18372834],"study_design_scores_gemma":[0.000027543412,0.00045617786,0.8632594,0.000013690538,0.00018597116,0.00031831473,0.00021144237,0.08162141,0.052552674,0.00018084624,0.0011451126,0.0000275138],"about_ca_topic_score_codex":0.0012246957,"about_ca_topic_score_gemma":0.002343847,"teacher_disagreement_score":0.0012246957,"about_ca_system_score_codex":0.00011367144,"about_ca_system_score_gemma":0.00009059353,"threshold_uncertainty_score":0.0024351478},"labels":[],"label_agreement":null},{"id":"W2526163493","doi":"10.4236/ars.2016.53016","title":"Segment-Based Terrain Filtering Technique for Elevation-Based Building Detection in VHR Remote Sensing Images","year":2016,"lang":"en","type":"article","venue":"Advances in Remote Sensing","topic":"Automated Road and Building Extraction","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 New Brunswick","funders":"Ministère de l'Education Nationale, de l'Enseignement Superieur et de la Recherche","keywords":"Elevation (ballistics); Terrain; Digital elevation model; Remote sensing; Computer science; Artificial intelligence; Segmentation; Computer vision; Filter (signal processing); Geology; Geography; Cartography; Engineering","score_opus":0.007775928232371057,"score_gpt":0.2603961357303429,"score_spread":0.25262020749797187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2526163493","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12400346,0.00019727788,0.8708574,0.000052104813,0.000030521547,0.000099614685,0.00044877618,0.002899218,0.0014116286],"genre_scores_gemma":[0.31106398,0.00027069316,0.6847706,0.00003848694,0.000025994474,0.00008028133,0.0019339325,0.00016957139,0.0016465168],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997774,0.000028220888,0.000014313956,0.000046149824,0.00010403049,0.000029870083],"domain_scores_gemma":[0.9996866,0.00007040806,0.00004080148,0.00008099244,0.00011026816,0.000011043164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003844467,0.00043512278,0.00034432576,0.0015989578,0.00026077355,0.00044364858,0.00040192986,0.00033846937,0.001649366],"category_scores_gemma":[0.00065909995,0.0001965054,0.0006018196,0.0010459786,0.00020723694,0.00045333133,0.0002860967,0.00034262857,0.0010218988],"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":[0.0002117283,0.00014743047,0.007977016,0.00019297026,0.00013075088,0.00020477442,0.0002556816,0.021706816,0.3097706,0.0015581694,0.0025823393,0.6552617],"study_design_scores_gemma":[0.000034552977,0.00037998855,0.055706866,0.000032928543,0.00016997744,0.0012321831,0.0003084288,0.6176088,0.30775177,0.0017952452,0.014909177,0.0000701446],"about_ca_topic_score_codex":0.0026396813,"about_ca_topic_score_gemma":0.005587018,"teacher_disagreement_score":0.0026396813,"about_ca_system_score_codex":0.000196166,"about_ca_system_score_gemma":0.00035906903,"threshold_uncertainty_score":0.005517721},"labels":[],"label_agreement":null},{"id":"W2560653914","doi":"10.4236/ars.2016.54023","title":"Improvement of Bare Soil Semi-Empirical Radar Backscattering Models (Oh and Dubois) with SAR Multi-Spectral Satellite Data (X-, C- and L-Bands)","year":2016,"lang":"en","type":"article","venue":"Advances in Remote Sensing","topic":"Soil Moisture and Remote Sensing","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":"Japan Aerospace Exploration Agency; Canadian Space Agency; Centre National d’Etudes Spatiales; European Space Agency","keywords":"Remote sensing; L band; Surface roughness; Surface finish; Water content; Satellite; Radar; Multispectral image; Empirical modelling; Correlation coefficient; C band; Microwave; X band; Environmental science; Soil texture; Soil science; Materials science; Mathematics; Geology; Optics; Physics; Computer science; Soil water; Statistics; Telecommunications; Simulation","score_opus":0.02075264309085736,"score_gpt":0.2693099193224874,"score_spread":0.24855727623163001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2560653914","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8623813,0.0006564458,0.13370416,0.00010854504,0.000047242956,0.00004691431,0.00021561883,0.0011994994,0.00164021],"genre_scores_gemma":[0.97022265,0.00015988883,0.02873641,0.000028863476,0.000010190643,0.00002137496,0.00027817505,0.000074033334,0.0004684089],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951696,0.00020618417,0.00003408162,0.000101611215,0.000092645736,0.000048493173],"domain_scores_gemma":[0.9991032,0.00035013966,0.00011347024,0.00020633657,0.00019557867,0.000031353076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010952266,0.0010219354,0.0005526548,0.00042800847,0.00013222026,0.00070034456,0.00068007136,0.0004651734,0.00044994574],"category_scores_gemma":[0.0022835135,0.0002793912,0.000628849,0.00028771412,0.00017306196,0.0007086067,0.00047810265,0.0003578013,0.0002545903],"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":[0.00020901395,0.0001916557,0.01148187,0.000092663795,0.00017911292,0.000075057316,0.000073433795,0.8897669,0.014436792,0.00028974938,0.0003620334,0.082841724],"study_design_scores_gemma":[0.000013350769,0.000075355165,0.0039495523,0.000007752888,0.000031070016,0.000025764877,0.000023166407,0.990733,0.004548522,0.000095799005,0.00048159674,0.000014997222],"about_ca_topic_score_codex":0.0072657624,"about_ca_topic_score_gemma":0.0045471475,"teacher_disagreement_score":0.0072657624,"about_ca_system_score_codex":0.00026740454,"about_ca_system_score_gemma":0.00038084656,"threshold_uncertainty_score":0.014446974},"labels":[],"label_agreement":null},{"id":"W2576410327","doi":"10.4236/ars.2017.61001","title":"Building Change Detection Improvement Using Topographic Correction Models","year":2017,"lang":"en","type":"article","venue":"Advances in Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","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":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Change detection; Brightness; Pixel; Remote sensing; Nadir; Computer science; Computer vision; Artificial intelligence; Brightness temperature; Geology; Optics; Physics; Satellite","score_opus":0.03985178315254148,"score_gpt":0.294565496400842,"score_spread":0.2547137132483005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2576410327","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12624963,0.0004243967,0.8674637,0.000113149785,0.00006538224,0.0000823092,0.00012526786,0.0025908065,0.002885474],"genre_scores_gemma":[0.76971966,0.00043047447,0.22686182,0.00004513664,0.00002963082,0.000038373295,0.00039124058,0.00020308146,0.002280559],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966395,0.00004540034,0.000015706555,0.00010245927,0.00013191007,0.000040567098],"domain_scores_gemma":[0.99952245,0.00010298706,0.00008030865,0.00007955385,0.00020144743,0.000013195575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046129726,0.00063450244,0.00040754513,0.0012384775,0.00019466644,0.0006708509,0.0005965116,0.0003558616,0.00086600747],"category_scores_gemma":[0.0015862626,0.00027389894,0.00076966657,0.0010387542,0.00020888489,0.0009241765,0.00039179315,0.0004869899,0.0005673054],"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":[0.00015366443,0.00012633631,0.010646181,0.0001718708,0.00013948593,0.00022281957,0.00016641378,0.3520232,0.050901562,0.0019830687,0.0017141501,0.5817513],"study_design_scores_gemma":[0.00000421048,0.000036820707,0.0040329145,0.0000055120113,0.00003426547,0.00010491254,0.000028105695,0.9812258,0.012596754,0.00036269307,0.0015515512,0.000016573948],"about_ca_topic_score_codex":0.00643931,"about_ca_topic_score_gemma":0.009200842,"teacher_disagreement_score":0.00643931,"about_ca_system_score_codex":0.0002903648,"about_ca_system_score_gemma":0.0005326914,"threshold_uncertainty_score":0.012803614},"labels":[],"label_agreement":null},{"id":"W2621697025","doi":"10.4236/ars.2017.62008","title":"Optical-Elevation Data Co-Registration and Classification-Based Height Normalization for Building Detection in Stereo VHR Images","year":2017,"lang":"en","type":"article","venue":"Advances in Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Ministère de l'Education Nationale, de l'Enseignement Superieur et de la Recherche","keywords":"Elevation (ballistics); Normalization (sociology); Remote sensing; Computer science; Artificial intelligence; Computer vision; Digital elevation model; Terrain; Change detection; Image registration; Image (mathematics); Geology; Geography; Cartography; Mathematics","score_opus":0.034270367543064076,"score_gpt":0.334146370179973,"score_spread":0.29987600263690894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2621697025","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08878627,0.00013916472,0.904788,0.000050213337,0.00006500803,0.00012855606,0.00039432713,0.0033608803,0.0022875573],"genre_scores_gemma":[0.34854823,0.00016944794,0.64788115,0.00004498616,0.000036845227,0.00012736874,0.0014329529,0.00023886513,0.001520173],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903727,0.000119104516,0.000047405698,0.00020531367,0.00048687903,0.00010406984],"domain_scores_gemma":[0.9994367,0.000057010835,0.0000981987,0.00017982704,0.00020619374,0.000022058086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005937441,0.000544178,0.00043112223,0.001904023,0.0002875759,0.000572471,0.0007475092,0.00035341314,0.0015556797],"category_scores_gemma":[0.0010914999,0.0002767855,0.00058260316,0.0015728882,0.00030299334,0.0007410047,0.00079426175,0.00049923017,0.0014353298],"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":[0.00020118654,0.0002699323,0.007941688,0.00022373864,0.00008236617,0.0001315998,0.00019548861,0.018224437,0.2793827,0.0017096977,0.0027288157,0.68890834],"study_design_scores_gemma":[0.000043030268,0.00023111787,0.05321238,0.00004164699,0.000110601504,0.0008032917,0.00026691108,0.5076031,0.41951483,0.0018440862,0.016236888,0.00009211062],"about_ca_topic_score_codex":0.0015971304,"about_ca_topic_score_gemma":0.0036680922,"teacher_disagreement_score":0.001904023,"about_ca_system_score_codex":0.00023856788,"about_ca_system_score_gemma":0.0006055503,"threshold_uncertainty_score":0.0052042603},"labels":[],"label_agreement":null},{"id":"W3117554305","doi":"10.4236/ars.2020.93006","title":"Complementarity of Sentinel-1 and Sentinel-2 Data for Mapping Agricultural Areas in Senegal","year":2020,"lang":"en","type":"article","venue":"Advances in Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Normalized Difference Vegetation Index; Growing season; Radar; Agriculture; Remote sensing; Geography; Agronomy; Environmental science; Physical geography; Leaf area index; Biology","score_opus":0.03255295615436498,"score_gpt":0.27533638429074875,"score_spread":0.24278342813638376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3117554305","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9920697,0.00021645865,0.0034870396,0.00009381007,0.00001647952,0.00004854856,0.0012564949,0.0001104395,0.0027010152],"genre_scores_gemma":[0.9918418,0.000106621774,0.006780425,0.000031364896,0.000004243563,0.000043461296,0.00074536924,0.000011430509,0.0004353281],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99931645,0.0003151677,0.00003155453,0.00012906436,0.00008660279,0.00012111381],"domain_scores_gemma":[0.9996246,0.0000999531,0.000048421956,0.000055764864,0.00013853537,0.00003272212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010455397,0.0006147194,0.00028898448,0.0012669137,0.0002545194,0.00054566993,0.00032176616,0.00018165229,0.00095242314],"category_scores_gemma":[0.0012905729,0.00022136488,0.00023619099,0.001468391,0.00021243072,0.0005743546,0.00075010984,0.0001406832,0.0001968069],"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":[0.001560166,0.000209851,0.6019938,0.0003870953,0.0006371113,0.0023074422,0.0015004034,0.052911438,0.09464517,0.0010369414,0.0018988214,0.24091177],"study_design_scores_gemma":[0.00009908432,0.00036663422,0.83467066,0.00013011463,0.00033962526,0.000838768,0.0052431053,0.13102941,0.016038762,0.0007498185,0.010373209,0.000120760466],"about_ca_topic_score_codex":0.03208667,"about_ca_topic_score_gemma":0.0795959,"teacher_disagreement_score":0.03208667,"about_ca_system_score_codex":0.00037623974,"about_ca_system_score_gemma":0.00047965097,"threshold_uncertainty_score":0.0637998},"labels":[],"label_agreement":null},{"id":"W3203024476","doi":"10.4236/ars.2021.103007","title":"Fully Polarimetric Land Cover Classification Based on Hidden Markov Models Trained with Multiple Observations","year":2021,"lang":"en","type":"article","venue":"Advances in Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","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":"Polarimetry; Land cover; Hidden Markov model; Remote sensing; Computer science; Pixel; Pattern recognition (psychology); Scattering; Cover (algebra); Artificial intelligence; Novelty; Contextual image classification; Set (abstract data type); Geography; Land use; Image (mathematics); Physics; Engineering","score_opus":0.019087278422321798,"score_gpt":0.23575690418135375,"score_spread":0.21666962575903195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203024476","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13831313,0.00028011168,0.85799694,0.00015545294,0.00005135807,0.000065227774,0.00048214063,0.0014206014,0.0012350158],"genre_scores_gemma":[0.850367,0.00016019001,0.14471765,0.00007913012,0.000053705768,0.00011545857,0.0016248227,0.00007113555,0.002810831],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971527,0.000084434505,0.000017283208,0.00007448491,0.00005648977,0.0000519636],"domain_scores_gemma":[0.99891305,0.00071073876,0.00009836418,0.000093966875,0.00014878507,0.000035118483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008216434,0.0006179128,0.000657014,0.0006247328,0.00032103536,0.0006556004,0.0005445004,0.0004685359,0.0012515748],"category_scores_gemma":[0.0014715763,0.00034019817,0.0006610511,0.00042538653,0.00024723218,0.0008319804,0.00046867284,0.00082876906,0.00062954164],"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":[0.00045169226,0.0002302875,0.008152811,0.000067350746,0.00014738728,0.00013161797,0.00013625325,0.6371481,0.0109551335,0.0030593383,0.001601925,0.33791804],"study_design_scores_gemma":[0.0000021722713,0.000018728926,0.00080867996,0.0000031927093,0.000005920758,0.000008920541,0.0000055814694,0.9976077,0.0006154933,0.0008329704,0.00008567955,0.000004952643],"about_ca_topic_score_codex":0.0079760365,"about_ca_topic_score_gemma":0.009152785,"teacher_disagreement_score":0.0079760365,"about_ca_system_score_codex":0.0004897586,"about_ca_system_score_gemma":0.0006197583,"threshold_uncertainty_score":0.015859187},"labels":[],"label_agreement":null},{"id":"W4285184728","doi":"10.4236/ars.2022.112003","title":"Sustainable Land Use Prediction in Light of Agroforestry Systems in Response to the Changing Scenario of Land Cover","year":2022,"lang":"en","type":"article","venue":"Advances in Remote Sensing","topic":"Agricultural Systems and Practices","field":"Agricultural and Biological Sciences","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":"McGill University","funders":"","keywords":"Sustainability; Land use; Land cover; Agroforestry; Biodiversity; Natural resource; Cover (algebra); Environmental resource management; Resource (disambiguation); Environmental science; Remote sensing; Geography; Ecology; Computer science","score_opus":0.011644615698942802,"score_gpt":0.23037844432316787,"score_spread":0.21873382862422508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285184728","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99496317,0.00009987028,0.0018931023,0.00010960197,0.0000053124713,0.000017148639,0.0011183112,0.000030142528,0.0017633896],"genre_scores_gemma":[0.9985942,0.000052758485,0.00071418856,0.0000041232074,0.0000011385566,0.0000061629025,0.00041414125,0.0000024977792,0.00021068363],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989116,0.000022934566,0.000008285873,0.000026407093,0.000018366736,0.000032804255],"domain_scores_gemma":[0.99980956,0.000043324893,0.00004641801,0.000015265487,0.000054561973,0.00003094729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028060854,0.00020037673,0.0001288376,0.0007516988,0.00014851001,0.0006594726,0.00019944385,0.0002278629,0.00095206924],"category_scores_gemma":[0.0005087587,0.00008772256,0.00020365366,0.00090503675,0.0001385324,0.00041567325,0.00022311813,0.00019778173,0.00028186955],"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":[0.00014782179,0.00011517612,0.8654252,0.00010691671,0.00011155409,0.00041984176,0.00032254518,0.080712795,0.0058655823,0.0009065801,0.0016658977,0.0442],"study_design_scores_gemma":[0.000011014424,0.000089221896,0.73588187,0.00003257822,0.00003220549,0.0001268054,0.0017755481,0.25665882,0.001601512,0.0013512167,0.002414249,0.000024935936],"about_ca_topic_score_codex":0.018956693,"about_ca_topic_score_gemma":0.04416988,"teacher_disagreement_score":0.018956693,"about_ca_system_score_codex":0.000693652,"about_ca_system_score_gemma":0.0004140583,"threshold_uncertainty_score":0.037692726},"labels":[],"label_agreement":null},{"id":"W7114923453","doi":"10.4236/ars.2025.144013","title":"When Coconut Trees Die: Spatio-Temporal Land-Use Dynamics on Grand-Lahou Island, Côte d’Ivoire","year":2025,"lang":"","type":"article","venue":"Advances in Remote Sensing","topic":"Cocoa and Sweet Potato Agronomy","field":"Agricultural and Biological Sciences","cited_by":0,"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":"International Development Research Centre","keywords":"Cash crop; Threatened species; Subsistence agriculture; Crop; Agriculture; Tropics","score_opus":0.021950285200370647,"score_gpt":0.2649726727231769,"score_spread":0.24302238752280628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7114923453","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99872726,0.00009212242,0.000040665604,0.00004634716,0.0000035101089,0.0000064743467,0.00058142707,0.000003894417,0.0004983276],"genre_scores_gemma":[0.9983563,0.00009637975,0.00011301188,0.000016463366,0.000003645988,0.000011715775,0.00086886965,0.000001786876,0.0005317231],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999136,0.000009389676,0.0000034071818,0.00003021656,0.000013549078,0.000029750989],"domain_scores_gemma":[0.9998179,0.000025031964,0.000046895828,0.000007853322,0.000049668477,0.000052739022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014381844,0.0001452174,0.00013304091,0.00060504023,0.00025404277,0.00060823245,0.00029363896,0.00027005246,0.0009984486],"category_scores_gemma":[0.00031593288,0.0001054879,0.00015583355,0.0007613625,0.00029493295,0.00029658416,0.0003682579,0.00020112572,0.00012296514],"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":[0.00012298711,0.00006838439,0.9805222,0.00006949111,0.000054302436,0.00094727305,0.0025245985,0.0006765834,0.0042336383,0.00016315894,0.0013820037,0.009235292],"study_design_scores_gemma":[0.0000012853995,0.0000083262485,0.9971915,0.000009699037,0.0000049198898,0.000033803317,0.001410028,0.00066406804,0.00007614565,0.0000069012544,0.0005898412,0.0000034932857],"about_ca_topic_score_codex":0.3348765,"about_ca_topic_score_gemma":0.55660695,"teacher_disagreement_score":0.3348765,"about_ca_system_score_codex":0.0012471154,"about_ca_system_score_gemma":0.0005370332,"threshold_uncertainty_score":0.66585463},"labels":[],"label_agreement":null}]}