{"meta":{"query_hash":"188b880ad508","filters":{"venue":"GIScience & Remote Sensing"},"cohort_total":36,"direct_labels_cover":0,"predictions_cover":36,"exported":36,"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/188b880ad508","api":"https://metacan.xera.ac/api/v1/cohort?venue=GIScience+%26+Remote+Sensing"},"results":[{"id":"W1532834990","doi":"10.1080/15481603.2013.808459","title":"An assessment of spatial models for daily minimum and maximum air temperature","year":2013,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Urban Heat Island Mitigation","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":"Thompson Rivers University","funders":"","keywords":"Remote sensing; Environmental science; Air temperature; Ranging; Similarity (geometry); Meteorology; Geography; Computer science; Artificial intelligence; Geodesy","score_opus":0.011341973840227964,"score_gpt":0.2581464385667566,"score_spread":0.24680446472652864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1532834990","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7973063,0.00068810035,0.19311965,0.0006817666,0.00003656525,0.000097235345,0.0010543694,0.00043492118,0.0065810047],"genre_scores_gemma":[0.9789433,0.00020431081,0.019469287,0.00002729259,0.0000131822235,0.00006144307,0.0005011051,0.000039067585,0.00074100774],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998884,0.00058334065,0.000056175908,0.00019813146,0.00022177865,0.000056591998],"domain_scores_gemma":[0.9943175,0.004174875,0.0004007821,0.00040497808,0.00063242327,0.00006959555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036635082,0.0006444566,0.0005410273,0.0008113713,0.00048836146,0.00082512666,0.0013876574,0.0006313551,0.00090627116],"category_scores_gemma":[0.009638349,0.0004508106,0.0011320211,0.0007984035,0.00033716136,0.0014439896,0.00060786906,0.00046705102,0.00026630948],"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":[0.00003203497,0.000022672197,0.011864894,0.000022528733,0.00006717998,0.000020055424,0.000049386024,0.9802422,0.00020316782,0.0013774845,0.000105856896,0.0059924736],"study_design_scores_gemma":[0.0000054536386,0.0000306026,0.0028144154,0.000007337889,0.000015856447,0.00002135298,0.00005773899,0.9956483,0.00016928374,0.000933648,0.00028738848,0.000008546223],"about_ca_topic_score_codex":0.0376762,"about_ca_topic_score_gemma":0.031474646,"teacher_disagreement_score":0.0376762,"about_ca_system_score_codex":0.0015602398,"about_ca_system_score_gemma":0.0011937955,"threshold_uncertainty_score":0.07491386},"labels":[],"label_agreement":null},{"id":"W1988879009","doi":"10.2747/1548-1603.49.2.182","title":"Patterns of Aboveground Biomass Regeneration in Post-Fire Coastal Scrub Communities","year":2012,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":14,"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 Toronto","keywords":"Synthetic aperture radar; Environmental science; Shrub; Biomass (ecology); Ecosystem; Remote sensing; Correlation coefficient; Forestry; Geography; Ecology; Biology; Mathematics","score_opus":0.020362956400073868,"score_gpt":0.24970898446753015,"score_spread":0.22934602806745627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988879009","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.999874,0.000008568289,0.000009509028,0.0000013569742,1.10598954e-7,7.7161826e-7,0.000027835884,8.6300855e-7,0.000076939985],"genre_scores_gemma":[0.99974936,0.000008421499,0.000033124164,0.0000018089241,3.2204855e-7,0.0000014478405,0.00009444732,3.8357004e-7,0.00011074956],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998914,0.000017345636,0.000007685608,0.000028866787,0.000023954995,0.00003076633],"domain_scores_gemma":[0.99925095,0.00010862954,0.00022726048,0.000043886328,0.00021586593,0.00015336866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020289194,0.00011294127,0.000118227246,0.0010130608,0.0002616437,0.00029871048,0.00016004768,0.0001442314,0.00063853565],"category_scores_gemma":[0.00048822485,0.00009585658,0.000112291054,0.0004108969,0.00027713642,0.00013469627,0.00020353291,0.00009550694,0.00015536055],"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":[0.000121092824,0.000032189477,0.9857594,0.000012607806,0.000021547175,0.00011829528,0.00050118304,0.00010608643,0.009651954,0.00001852409,0.00003854641,0.0036186394],"study_design_scores_gemma":[5.719265e-7,0.000020020792,0.99955076,8.939847e-7,0.0000014283871,0.000029875479,0.0001610863,0.00008349757,0.00012885428,0.0000028082507,0.000019348383,8.895062e-7],"about_ca_topic_score_codex":0.019861536,"about_ca_topic_score_gemma":0.06549063,"teacher_disagreement_score":0.019861536,"about_ca_system_score_codex":0.00027473978,"about_ca_system_score_gemma":0.00017230355,"threshold_uncertainty_score":0.03949189},"labels":[],"label_agreement":null},{"id":"W2002552825","doi":"10.2747/1548-1603.47.3.321","title":"Using Stereo Satellite Imagery for Topographic and Transportation Applications: An Accuracy Assessment","year":2010,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Satellite Image Processing and Photogrammetry","field":"Engineering","cited_by":40,"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","funders":"","keywords":"Remote sensing; Photogrammetry; Satellite; Digital elevation model; Terrain; Orthophoto; Computer science; Artificial intelligence; Computer vision; Geography; Elevation (ballistics); Pixel; Affine transformation; Satellite imagery; Cartography; Mathematics; Engineering","score_opus":0.0270707687604645,"score_gpt":0.318155228087157,"score_spread":0.2910844593266925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002552825","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7646548,0.0016583535,0.2251057,0.00028813997,0.00006462119,0.00017824421,0.0011793927,0.0007463131,0.006124341],"genre_scores_gemma":[0.8605349,0.0006891619,0.13695274,0.000041877563,0.000026296886,0.000042025997,0.0009799721,0.000047902366,0.0006851421],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99756783,0.0005683511,0.00012765266,0.00012756177,0.0015345935,0.00007407265],"domain_scores_gemma":[0.9960949,0.0012520711,0.00039392232,0.0006449233,0.0015772702,0.000037002683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026804286,0.00047740765,0.00028593693,0.0018871067,0.00024553048,0.0006273435,0.0005048448,0.0007048878,0.00082411966],"category_scores_gemma":[0.007069001,0.00016502249,0.0003554854,0.0013075909,0.00036875554,0.0008661039,0.00047760882,0.00020220056,0.00040646456],"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":[0.0011441462,0.00033198213,0.14500238,0.00060989097,0.00032283037,0.00038521877,0.00050005526,0.08198263,0.15760736,0.0019524571,0.0018838476,0.60827714],"study_design_scores_gemma":[0.00013048282,0.0017832905,0.21989328,0.00015921862,0.00035826577,0.00131438,0.0010410098,0.48528296,0.27691716,0.002122014,0.010850953,0.00014692868],"about_ca_topic_score_codex":0.0030265916,"about_ca_topic_score_gemma":0.005281407,"teacher_disagreement_score":0.0030265916,"about_ca_system_score_codex":0.0003567432,"about_ca_system_score_gemma":0.00025777135,"threshold_uncertainty_score":0.014175653},"labels":[],"label_agreement":null},{"id":"W2007282093","doi":"10.1080/15481603.2015.1033809","title":"Spatial data, analysis approaches, and information needs for spatial ecosystem service assessments: a review","year":2015,"lang":"en","type":"review","venue":"GIScience & Remote Sensing","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":139,"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 Victoria; Natural Resources Canada; University of British Columbia; Canadian Forest Service","funders":"","keywords":"Ecosystem services; Spatial analysis; Computer science; Environmental resource management; Data science; Spatial ecology; Service (business); Geospatial analysis; Geography; Ecosystem; Cartography; Remote sensing; Ecology; Environmental science","score_opus":0.09224832804001247,"score_gpt":0.3227015255466644,"score_spread":0.23045319750665194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007282093","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019580296,0.9952791,0.0014399847,0.0011837933,0.00016727213,0.00002552969,0.00021631907,0.00001586628,0.0014764107],"genre_scores_gemma":[0.0012241838,0.995678,0.0024131301,0.0002505656,0.00007927236,0.000035269743,0.0001680433,0.000006527022,0.00014509218],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972517,0.0008465368,0.00059306406,0.00027305583,0.0009577765,0.0000778267],"domain_scores_gemma":[0.96934634,0.024706544,0.0013987882,0.0005916967,0.0037357549,0.00022079464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009137299,0.0012095971,0.002473744,0.01123033,0.00056444283,0.0025128508,0.002383025,0.0016111739,0.00485515],"category_scores_gemma":[0.02513306,0.0008035991,0.0016060392,0.015647827,0.0015311823,0.0058908784,0.0014864038,0.0018682219,0.0014096993],"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":[0.000024829233,0.000036554327,0.0006947064,0.064184815,0.0002118747,0.000096932556,0.0002655966,0.0009616955,0.00027091405,0.01616304,0.022868572,0.8942205],"study_design_scores_gemma":[0.000015265108,0.00004516794,0.002495667,0.082170755,0.00063160365,0.00053242344,0.0005801667,0.00051224494,0.00039131835,0.015756909,0.8967877,0.00008077167],"about_ca_topic_score_codex":0.013079295,"about_ca_topic_score_gemma":0.017995354,"teacher_disagreement_score":0.013079295,"about_ca_system_score_codex":0.00292401,"about_ca_system_score_gemma":0.008163782,"threshold_uncertainty_score":0.048323274},"labels":[],"label_agreement":null},{"id":"W2027165049","doi":"10.1080/15481603.2014.926650","title":"Recent applications of unmanned aerial imagery in natural resource management","year":2014,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":208,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre de Géomatique du Québec; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Remote sensing; Computer science; Aerial imagery; Satellite imagery; Aerial survey; Systems engineering; Drone; Geography; Engineering","score_opus":0.007015255025460905,"score_gpt":0.2291673900078925,"score_spread":0.22215213498243158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027165049","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021492831,0.90909,0.038981218,0.0012991112,0.00032162544,0.000059472597,0.00009822379,0.00008378778,0.028573675],"genre_scores_gemma":[0.09080356,0.8588694,0.0445186,0.00036955412,0.0005771467,0.0000312318,0.00018810557,0.000024939041,0.0046175723],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99961495,0.00008045347,0.000035419194,0.00008347683,0.00016433383,0.00002136922],"domain_scores_gemma":[0.99867225,0.00065255695,0.00015207336,0.0000524462,0.0004380822,0.00003266876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011074746,0.0003854171,0.0002868787,0.0012825126,0.00017795134,0.00077948533,0.00040466528,0.00041015277,0.0013700402],"category_scores_gemma":[0.001311062,0.00019972742,0.00023414743,0.00303444,0.00037514648,0.0010429686,0.00042289816,0.00035996272,0.0004279736],"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":[0.00004058246,0.000043823227,0.0017120966,0.0039974595,0.00005237611,0.00013726094,0.000286203,0.0041020312,0.012290295,0.0063895956,0.0031942157,0.9677541],"study_design_scores_gemma":[0.000012186249,0.0005875427,0.019009804,0.002600415,0.00029243148,0.0013053205,0.0009819785,0.012651185,0.032997932,0.013935209,0.91551644,0.00010960127],"about_ca_topic_score_codex":0.0012157891,"about_ca_topic_score_gemma":0.0020196952,"teacher_disagreement_score":0.0013700402,"about_ca_system_score_codex":0.0003907951,"about_ca_system_score_gemma":0.00047504896,"threshold_uncertainty_score":0.005856931},"labels":[],"label_agreement":null},{"id":"W2039819429","doi":"10.2747/1548-1603.47.2.260","title":"Multi-sensor Analyses of Vegetation Indices in a Semi-arid Environment","year":2010,"lang":"en","type":"article","venue":"GIScience & 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":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Comparability; Remote sensing; Vegetation (pathology); Arid; Scale (ratio); Environmental science; Geography; Cartography; Mathematics; Ecology","score_opus":0.02231385875835208,"score_gpt":0.27536475309873165,"score_spread":0.2530508943403796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039819429","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98627764,0.00016565627,0.012398387,0.000013968086,0.000011561753,0.00001677832,0.0002295796,0.000028074826,0.00085844635],"genre_scores_gemma":[0.9918143,0.000052031937,0.007760747,0.000005337086,0.0000032233024,0.000009895989,0.00016473589,0.00000662136,0.00018310577],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997012,0.000120201286,0.000023752136,0.000047114303,0.000089881454,0.00001789023],"domain_scores_gemma":[0.99944824,0.00029186448,0.00009960273,0.00003638832,0.0001033939,0.000020488565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000762615,0.00026740503,0.00022740386,0.001019872,0.00016164071,0.00031386683,0.00015019615,0.00013988168,0.0004900005],"category_scores_gemma":[0.0012453722,0.00011581725,0.0003112609,0.00088755705,0.00009755889,0.00047913622,0.0002360352,0.0001166087,0.00008029692],"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":[0.0017034644,0.00043639287,0.49889067,0.00052332296,0.0013805361,0.0004135745,0.00087389885,0.075671345,0.22335155,0.0015519513,0.0006631523,0.19454016],"study_design_scores_gemma":[0.00001849583,0.00044853595,0.81004035,0.000021550688,0.00018529205,0.00027078416,0.00071973674,0.15658955,0.029163564,0.0012180785,0.0012757784,0.000048273763],"about_ca_topic_score_codex":0.0013800833,"about_ca_topic_score_gemma":0.0035637163,"teacher_disagreement_score":0.0013800833,"about_ca_system_score_codex":0.00015050611,"about_ca_system_score_gemma":0.000083591956,"threshold_uncertainty_score":0.0040331483},"labels":[],"label_agreement":null},{"id":"W2315460111","doi":"10.1080/15481603.2015.1137112","title":"Land change attribution based on Landsat time series and integration of ancillary disturbance data in the Athabasca oil sands region of Canada","year":2016,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Natural Resources Canada","funders":"Canadian Forest Service; Canadian Space Agency","keywords":"Disturbance (geology); Environmental science; Land cover; Ecosystem; Physical geography; Flooding (psychology); Environmental change; Change detection; Climate change; Land use, land-use change and forestry; Hydrology (agriculture); Logging; Land use; Geography; Ecology; Remote sensing; Forestry; Geology","score_opus":0.02144578206542079,"score_gpt":0.21216582015296462,"score_spread":0.19072003808754384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2315460111","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98719317,0.00012298158,0.0019655682,0.000048671598,0.000009730671,0.000079597354,0.008176772,0.00018673697,0.002216622],"genre_scores_gemma":[0.98292625,0.00011751133,0.00579407,0.000015879757,0.0000040553646,0.000023240287,0.010214476,0.000019322666,0.0008851723],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994821,0.000030471636,0.0000305218,0.00011046008,0.00024884424,0.00009764667],"domain_scores_gemma":[0.9987147,0.00009374434,0.00019070483,0.00007609493,0.0008073888,0.00011731997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006146225,0.00039982816,0.000250633,0.002682118,0.0004792465,0.0010387562,0.00043137584,0.00017858026,0.00043196062],"category_scores_gemma":[0.0018916324,0.00016692457,0.00031872283,0.0042403545,0.0002859162,0.00036490685,0.00039861168,0.00023129553,0.00013578596],"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":[0.00013270987,0.00007762663,0.9132168,0.00005190416,0.0001546252,0.00013194764,0.00029738748,0.040818546,0.0026004007,0.0002723687,0.0016295078,0.04061618],"study_design_scores_gemma":[0.000009177981,0.000012176116,0.91364104,0.000017242772,0.000030849336,0.000019121351,0.0003727265,0.08298101,0.0009863449,0.00006583607,0.0018403966,0.000023989905],"about_ca_topic_score_codex":0.97255963,"about_ca_topic_score_gemma":0.9843201,"teacher_disagreement_score":0.02744037,"about_ca_system_score_codex":0.009761717,"about_ca_system_score_gemma":0.005595987,"threshold_uncertainty_score":0.07082659},"labels":[],"label_agreement":null},{"id":"W2331266404","doi":"10.1080/15481603.2016.1141448","title":"Forest fragmentation in Massachusetts, USA: a town-level assessment using Morphological spatial pattern analysis and affinity propagation","year":2016,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Science Foundation","keywords":"Fragmentation (computing); Geography; Forest fragmentation; Biodiversity; Spatial ecology; Forestry; Cartography; Ecology; Physical geography; Biology","score_opus":0.03173300213924473,"score_gpt":0.2706084929180028,"score_spread":0.23887549077875808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2331266404","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.996953,0.00017911929,0.0008208625,0.00005583983,0.0000022940465,0.000025495705,0.00078795955,0.000024505747,0.0011509414],"genre_scores_gemma":[0.9959929,0.00019548574,0.0021782166,0.000013800105,0.000005517003,0.000042410782,0.0009012919,0.000004313006,0.00066603],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998191,0.00004446535,0.000014418655,0.000048720358,0.000054204218,0.000019040468],"domain_scores_gemma":[0.99895227,0.00016567101,0.0003798451,0.00009018835,0.00029719115,0.00011475893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000341108,0.00020688216,0.00012924413,0.002111771,0.0005648435,0.00060807186,0.00043834824,0.00017578212,0.0010816354],"category_scores_gemma":[0.0012521584,0.00018099547,0.00018107402,0.002549006,0.0002638172,0.00047842515,0.0006378234,0.00018316421,0.00022889001],"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":[0.000035340585,0.000028779517,0.9832525,0.000025858164,0.000047948186,0.0001814732,0.0013004392,0.00030692032,0.0013104968,0.0001088029,0.00049614353,0.012905223],"study_design_scores_gemma":[0.0000018019248,0.000029797688,0.9978708,0.0000055631544,0.000016449885,0.00007870699,0.0005699289,0.00056517636,0.00011400595,0.000029061852,0.00071541674,0.0000032760786],"about_ca_topic_score_codex":0.12460311,"about_ca_topic_score_gemma":0.4103379,"teacher_disagreement_score":0.12460311,"about_ca_system_score_codex":0.001086733,"about_ca_system_score_gemma":0.00042231035,"threshold_uncertainty_score":0.24775565},"labels":[],"label_agreement":null},{"id":"W2561750000","doi":"10.1080/15481603.2016.1276255","title":"Crop classification and acreage estimation in North Korea using phenology features","year":2017,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","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":"Agriculture and Agri-Food Canada","funders":"National Geospatial-Intelligence Agency; National Natural Science Foundation of China","keywords":"Normalized Difference Vegetation Index; Moderate-resolution imaging spectroradiometer; Ground truth; Spectroradiometer; Estimation; Vegetation (pathology); Remote sensing; Crop; Phenology; Enhanced vegetation index; Geography; Thematic map; Crop yield; Environmental science; Mathematics; Cartography; Vegetation Index; Leaf area index; Satellite; Computer science; Forestry; Agronomy; Reflectivity; Artificial intelligence","score_opus":0.024915978776866937,"score_gpt":0.27108093492959406,"score_spread":0.24616495615272713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2561750000","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99339205,0.0001639267,0.005747239,0.0000143002235,0.0000033876984,0.000008907922,0.0002112468,0.000058564638,0.00040029836],"genre_scores_gemma":[0.99144495,0.00012167308,0.007623807,0.000007650146,0.000002206177,0.000010982014,0.0003550871,0.000008987482,0.00042466496],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998778,0.000018155612,0.000011830294,0.00004907053,0.000024652198,0.0000184405],"domain_scores_gemma":[0.99975866,0.0000334549,0.00008844294,0.00001857296,0.0000766575,0.0000242551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002699096,0.00041994205,0.00018069819,0.0015181683,0.0001460421,0.00038584045,0.00016305,0.00012294814,0.0002411861],"category_scores_gemma":[0.00031315544,0.00016345846,0.00023094691,0.000924032,0.00012173895,0.0005160359,0.00026966882,0.00009136313,0.000098922945],"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":[0.00022054296,0.00010397116,0.76768607,0.0001878295,0.00014213477,0.00068307697,0.0005694866,0.030087652,0.043383382,0.00027172428,0.0008637587,0.15580037],"study_design_scores_gemma":[0.000011102964,0.000053601383,0.86076504,0.000020420697,0.000066164765,0.00021869122,0.0006374586,0.1309058,0.005634511,0.00018900464,0.0014607277,0.00003746493],"about_ca_topic_score_codex":0.01790317,"about_ca_topic_score_gemma":0.030851003,"teacher_disagreement_score":0.01790317,"about_ca_system_score_codex":0.0002768127,"about_ca_system_score_gemma":0.00024148545,"threshold_uncertainty_score":0.03559792},"labels":[],"label_agreement":null},{"id":"W2617645388","doi":"10.1080/15481603.2017.1331510","title":"Wetland classification in Newfoundland and Labrador using multi-source SAR and optical data integration","year":2017,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":95,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Memorial University of Newfoundland; Centre For Cold Ocean Resources Engineering","funders":"Environment and Climate Change Canada; Natural Sciences and Engineering Research Council of Canada; Department of Environment and Conservation, Government of Newfoundland and Labrador; Government of Canada","keywords":"Wetland; Remote sensing; Confusion; Synthetic aperture radar; Geography; Aerial imagery; Environmental science; Satellite imagery; Environmental resource management; Cartography; Ecology","score_opus":0.07366591244811731,"score_gpt":0.31803048024898994,"score_spread":0.24436456780087262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2617645388","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99611133,0.00021051285,0.0012361781,0.00003934516,0.0000055980217,0.000030786592,0.0009447131,0.000084288,0.0013372859],"genre_scores_gemma":[0.98984015,0.00019579534,0.005932049,0.00003192914,0.00000433371,0.000022446044,0.002639335,0.000013570179,0.001320372],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997129,0.000036266116,0.000018329569,0.000066088316,0.000059414317,0.00010688693],"domain_scores_gemma":[0.99948186,0.0000760766,0.00009899074,0.000037738733,0.00024543473,0.000059921305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050427584,0.00038114857,0.00022509848,0.0019423214,0.00048529322,0.00085351686,0.00035349975,0.0002009735,0.00051423087],"category_scores_gemma":[0.00058022223,0.00018695077,0.00028830612,0.0014938947,0.00038136207,0.00032956467,0.00042704708,0.00016741073,0.00018439471],"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":[0.00064114435,0.00023079469,0.7817986,0.00019129044,0.00028213716,0.0011324378,0.0008805976,0.011812372,0.042893834,0.00021551446,0.0036284025,0.15629284],"study_design_scores_gemma":[0.0000162347,0.000034575383,0.98237014,0.000019815774,0.00006605971,0.000090064925,0.00075661845,0.012157965,0.0031959596,0.000012573087,0.0012643936,0.000015513153],"about_ca_topic_score_codex":0.8027333,"about_ca_topic_score_gemma":0.935032,"teacher_disagreement_score":0.1972667,"about_ca_system_score_codex":0.003015917,"about_ca_system_score_gemma":0.0018767051,"threshold_uncertainty_score":0.39685684},"labels":[],"label_agreement":null},{"id":"W2768915379","doi":"10.1080/15481603.2017.1408930","title":"Optimal spatial resolution of Unmanned Aerial Vehicle (UAV)-acquired imagery for species classification in a heterogeneous grassland ecosystem","year":2017,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":48,"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":"Remote sensing; Image resolution; Shadow (psychology); Vegetation (pathology); Aerial imagery; Satellite imagery; Spatial ecology; Vegetation classification; Geography; Cartography; Environmental science; Computer science; Artificial intelligence; Ecology; Biology","score_opus":0.024684374833664407,"score_gpt":0.24844188944897327,"score_spread":0.22375751461530888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768915379","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95046586,0.0008514246,0.045873523,0.0000931571,0.00003007063,0.00003553572,0.00039150348,0.00038942526,0.0018696308],"genre_scores_gemma":[0.9363959,0.00027597605,0.062559165,0.000040110706,0.000009220157,0.000021213411,0.0004908883,0.00003257847,0.00017482099],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9997814,0.000038475147,0.000016628825,0.000057816895,0.000059016467,0.00004660526],"domain_scores_gemma":[0.9996809,0.0000995113,0.000059673053,0.000032741507,0.00010285143,0.000024329427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004823474,0.000272686,0.00022763599,0.0009106142,0.00016996427,0.0005017261,0.00022844851,0.0002474975,0.0004480142],"category_scores_gemma":[0.0011050281,0.0001548419,0.00031358594,0.00058699975,0.00013501928,0.0005813783,0.00023538008,0.00021501954,0.000209993],"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":[0.00081954076,0.0003440308,0.10145416,0.00061023876,0.00023437425,0.000979944,0.00066572486,0.049902808,0.5626228,0.001138225,0.0025846765,0.27864352],"study_design_scores_gemma":[0.000047316807,0.00033390181,0.46899417,0.00017776637,0.00038428896,0.0010767265,0.0012562086,0.33758685,0.18269382,0.0012206786,0.0061114193,0.0001168738],"about_ca_topic_score_codex":0.00273902,"about_ca_topic_score_gemma":0.004946704,"teacher_disagreement_score":0.00273902,"about_ca_system_score_codex":0.00015313484,"about_ca_system_score_gemma":0.00020747479,"threshold_uncertainty_score":0.005446136},"labels":[],"label_agreement":null},{"id":"W2888773612","doi":"10.1080/15481603.2018.1513444","title":"Monitoring surface changes in discontinuous permafrost terrain using small baseline SAR interferometry, object-based classification, and geological features: a case study from Mayo, Yukon Territory, Canada","year":2018,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"","keywords":"Permafrost; Interferometric synthetic aperture radar; Terrain; Geology; Remote sensing; GNSS augmentation; Land cover; Baseline (sea); Vegetation (pathology); Synthetic aperture radar; Interferometry; Arctic; Subsidence; Physical geography; Geodesy; Cartography; Geomorphology; Geography; Global Positioning System; Land use","score_opus":0.059466770617165154,"score_gpt":0.2739050385330868,"score_spread":0.21443826791592163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888773612","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99820864,0.00011383851,0.00029429575,0.000048232836,0.0000021955213,0.00004447449,0.00037233368,0.00001414384,0.00090187887],"genre_scores_gemma":[0.9972088,0.00018438017,0.0013246166,0.000026769845,0.000001821664,0.000012142335,0.00050039944,0.000005696266,0.00073552347],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997522,0.000014446897,0.000010221295,0.000045717014,0.00009411733,0.000083236744],"domain_scores_gemma":[0.99973315,0.000026340473,0.000021836997,0.000012841986,0.00015557792,0.000050288574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017214147,0.0004177301,0.00025708313,0.0009612257,0.0013594539,0.0007823797,0.0006796748,0.00043614497,0.00029462547],"category_scores_gemma":[0.0003744915,0.0001741349,0.000252837,0.0025555305,0.00064529397,0.00019467053,0.0003878453,0.0002674272,0.000067295754],"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":[0.00021559604,0.00033891373,0.90670145,0.0001708515,0.00016672796,0.011572326,0.004052912,0.0086254245,0.025809042,0.0003897045,0.0016127244,0.040344182],"study_design_scores_gemma":[0.000017614426,0.000047156092,0.9793744,0.000021043661,0.00005127227,0.0005348908,0.0055754893,0.011553328,0.0013699199,0.000046888286,0.001379635,0.000028303411],"about_ca_topic_score_codex":0.9733708,"about_ca_topic_score_gemma":0.9920363,"teacher_disagreement_score":0.02662921,"about_ca_system_score_codex":0.0066953874,"about_ca_system_score_gemma":0.0072165728,"threshold_uncertainty_score":0.053572},"labels":[],"label_agreement":null},{"id":"W2915486752","doi":"10.1080/15481603.2019.1581475","title":"A literature synthesis of LiDAR applications in transportation: feature extraction and geometric assessments of highways","year":2019,"lang":"en","type":"article","venue":"GIScience & 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":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Alberta Innovates","keywords":"Lidar; Ranging; Computer science; Resource (disambiguation); Remote sensing; Field (mathematics); Data mining; Geography","score_opus":0.008355515885533687,"score_gpt":0.25434586667219605,"score_spread":0.24599035078666237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2915486752","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004931038,0.9598997,0.017729482,0.0019814675,0.00088097475,0.000082247025,0.0006903807,0.0001454258,0.013659229],"genre_scores_gemma":[0.01834539,0.9634345,0.013843539,0.00065954926,0.0007729129,0.000071398215,0.0008307137,0.000041023384,0.0020008767],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9992136,0.00018059489,0.00015862612,0.00019809026,0.00020744145,0.000041576335],"domain_scores_gemma":[0.99230134,0.005151962,0.00034923904,0.00020962588,0.0018824274,0.00010543828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016858468,0.00090605405,0.00096199394,0.015379407,0.0007997067,0.00238504,0.0009566192,0.001393955,0.0055874656],"category_scores_gemma":[0.006773471,0.0005288215,0.001237934,0.015488234,0.0006762739,0.0033422254,0.0009346945,0.00079016597,0.0014527846],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055120763,0.00007202656,0.0021257403,0.030909773,0.0001390979,0.00021432812,0.0006409814,0.002509108,0.0012221363,0.00963063,0.022527706,0.92995334],"study_design_scores_gemma":[0.000013046261,0.00023799783,0.014687357,0.06992408,0.0007239271,0.0012573285,0.002747581,0.004832299,0.0027520705,0.014782281,0.8879178,0.00012412571],"about_ca_topic_score_codex":0.004980225,"about_ca_topic_score_gemma":0.0075204647,"teacher_disagreement_score":0.015379407,"about_ca_system_score_codex":0.0009894143,"about_ca_system_score_gemma":0.0026119333,"threshold_uncertainty_score":0.018691897},"labels":[],"label_agreement":null},{"id":"W2944750402","doi":"10.1080/15481603.2019.1613804","title":"Detecting advanced stages of winter wheat yellow rust and aphid infection using RapidEye data in North China Plain","year":2019,"lang":"en","type":"article","venue":"GIScience & 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":"Agriculture and Agri-Food Canada","funders":"National Natural Science Foundation of China","keywords":"Aphid; Rust (programming language); Soybean rust; PEST analysis; Agronomy; Biology; Geography; Cartography; Remote sensing; Horticulture; Computer science","score_opus":0.012851303943712889,"score_gpt":0.2399484103567922,"score_spread":0.2270971064130793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944750402","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99853253,0.0000357965,0.0005579722,0.000014936398,0.0000043971763,0.000014430648,0.0003367311,0.000037043355,0.00046612808],"genre_scores_gemma":[0.9962663,0.000037943537,0.0023054306,0.000010441969,0.0000040445516,0.000014441875,0.0009848446,0.00000416326,0.00037241788],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99985754,0.000015221414,0.0000102172835,0.000049660594,0.00003864161,0.000028678538],"domain_scores_gemma":[0.99968946,0.00003954566,0.000057701964,0.00002826651,0.00014805747,0.00003703365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003841357,0.0003721978,0.0002434744,0.0011650675,0.00026792692,0.0003211973,0.00020794474,0.00027610024,0.0003450969],"category_scores_gemma":[0.00032314367,0.00015673199,0.00025854865,0.0005491171,0.0001624919,0.00040165466,0.000224487,0.00014063934,0.00011904424],"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":[0.0004106428,0.00031144105,0.80891955,0.00013444248,0.00009279827,0.0005393915,0.0006251444,0.007684804,0.1035343,0.0001262456,0.00090148934,0.07671966],"study_design_scores_gemma":[0.000015697704,0.000080354126,0.9647611,0.000009484328,0.0000352604,0.00008776812,0.00040283817,0.02595675,0.008108099,0.00004351322,0.0004812808,0.000017855218],"about_ca_topic_score_codex":0.029302077,"about_ca_topic_score_gemma":0.08196646,"teacher_disagreement_score":0.029302077,"about_ca_system_score_codex":0.00042670913,"about_ca_system_score_gemma":0.00035233382,"threshold_uncertainty_score":0.058263063},"labels":[],"label_agreement":null},{"id":"W2961186686","doi":"10.1080/15481603.2019.1643530","title":"Separability analysis of wetlands in Canada using multi-source SAR data","year":2019,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Flood Risk Assessment and Management","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":"Centre For Cold Ocean Resources Engineering","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wetland; Remote sensing; Swamp; Marsh; Environmental science; Synthetic aperture radar; Geology; Ecology","score_opus":0.03082083102172585,"score_gpt":0.279000637754777,"score_spread":0.24817980673305118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2961186686","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9960748,0.00014863409,0.0014364076,0.000059274364,0.0000037519433,0.000022459637,0.0009894684,0.00006292873,0.0012022861],"genre_scores_gemma":[0.99432385,0.00010773886,0.002809578,0.000012413119,0.0000017986596,0.0000062089953,0.0018368063,0.000007721157,0.00089398114],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997961,0.000008825923,0.00000840954,0.000035476056,0.000088216104,0.00006294158],"domain_scores_gemma":[0.99950266,0.00004449568,0.000034459757,0.000017667759,0.0003454002,0.000055356762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022945792,0.00030318782,0.00019626871,0.0023887542,0.0008257739,0.00066016614,0.00030194424,0.00016665008,0.0005009634],"category_scores_gemma":[0.0006053085,0.00013373526,0.00034352802,0.001652399,0.00025605963,0.00029692848,0.00030296648,0.00016987532,0.00009379078],"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":[0.00042838405,0.00018080494,0.73427385,0.00015319581,0.0001433088,0.0009496858,0.00093577313,0.028879892,0.043409344,0.0008329819,0.0020344923,0.18777823],"study_design_scores_gemma":[0.000012066603,0.000019545632,0.9405744,0.000015365687,0.00003873244,0.00008144969,0.00075553014,0.053856928,0.0029460806,0.000095803385,0.0015777671,0.000026437647],"about_ca_topic_score_codex":0.9498479,"about_ca_topic_score_gemma":0.9731102,"teacher_disagreement_score":0.050152123,"about_ca_system_score_codex":0.004840878,"about_ca_system_score_gemma":0.00525448,"threshold_uncertainty_score":0.10089493},"labels":[],"label_agreement":null},{"id":"W2996028473","doi":"10.1080/15481603.2019.1695406","title":"Feature selection for airborne LiDAR data filtering: a mutual information method with Parzon window optimization","year":2019,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Remote Sensing and LiDAR Applications","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":"Dalhousie University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Feature selection; Lidar; Mutual information; Artificial intelligence; Point cloud; Data mining; Feature (linguistics); Relevance (law); Ranging; Pattern recognition (psychology); Machine learning; Remote sensing; Geography","score_opus":0.014188406270678421,"score_gpt":0.25852384699271036,"score_spread":0.24433544072203195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996028473","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":[0.004906443,0.00010650654,0.9945564,0.000032525,0.000010505914,0.000025026411,0.000019822268,0.00019428504,0.00014856696],"genre_scores_gemma":[0.16241017,0.0002257905,0.8353893,0.000093518735,0.00007464501,0.00027664803,0.00030672416,0.00019464137,0.0010284683],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990368,0.0002785695,0.000060966882,0.00022494321,0.00030103629,0.00009771959],"domain_scores_gemma":[0.99921596,0.00042375177,0.00007912548,0.000055514258,0.00019920258,0.000026470296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017066802,0.00093151967,0.0018664232,0.0011223353,0.0005377872,0.00081513525,0.0015928751,0.00095227244,0.0013478448],"category_scores_gemma":[0.0027497683,0.00067937846,0.0017456864,0.0012758019,0.00050783786,0.0011141474,0.0008504243,0.000998647,0.00040665508],"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":[0.0002876334,0.00021025715,0.0017153563,0.00022458838,0.00023174199,0.00016463596,0.00017034361,0.49412233,0.020766713,0.011270159,0.0038122898,0.467024],"study_design_scores_gemma":[0.0000059552026,0.000025336089,0.00023436555,0.0000034316638,0.000012008656,0.0000152835,0.000005711784,0.99750537,0.0010168805,0.00076056493,0.0004088494,0.000006220919],"about_ca_topic_score_codex":0.0044659413,"about_ca_topic_score_gemma":0.0035251167,"teacher_disagreement_score":0.0044659413,"about_ca_system_score_codex":0.0005658603,"about_ca_system_score_gemma":0.0010807274,"threshold_uncertainty_score":0.009025872},"labels":[],"label_agreement":null},{"id":"W3093978280","doi":"10.1080/15481603.2020.1829377","title":"Peatland leaf-area index and biomass estimation with ultra-high resolution remote sensing","year":2020,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":41,"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":"Academy of Finland; Helsingin Yliopisto","keywords":"Hyperspectral imaging; Remote sensing; Leaf area index; Environmental science; Lidar; Biomass (ecology); Vegetation (pathology); Spatial ecology; Geography; Ecology","score_opus":0.012133733866217248,"score_gpt":0.20935413011878903,"score_spread":0.1972203962525718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093978280","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97885096,0.00009773265,0.0200128,0.00001450984,0.0000035166865,0.0000103831835,0.00025943137,0.00015122716,0.00059948035],"genre_scores_gemma":[0.983001,0.000030732077,0.016417595,0.0000039612023,0.0000019864717,0.000009385831,0.00029901293,0.0000057639327,0.0002305638],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998882,0.000026575271,0.000006101997,0.000042801254,0.000022451088,0.0000139271615],"domain_scores_gemma":[0.99981874,0.00006243924,0.00004338226,0.000026607406,0.000036685982,0.000012121925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003672692,0.00034673407,0.00017584547,0.0007461118,0.00010903012,0.00026120103,0.0002914034,0.00017933738,0.00040949075],"category_scores_gemma":[0.0006339593,0.00016471076,0.0003753733,0.000480262,0.000090040776,0.0004648465,0.00019954526,0.000120429526,0.00016097625],"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":[0.00033510558,0.00025338243,0.6100388,0.00011061754,0.00022031614,0.00025605425,0.00014828429,0.16565494,0.045564644,0.00045060285,0.00043979665,0.17652743],"study_design_scores_gemma":[0.000015069505,0.000059581158,0.37929657,0.0000109591065,0.00004191203,0.000115023366,0.000073031006,0.61322236,0.006375338,0.00039169588,0.0003727589,0.000025771358],"about_ca_topic_score_codex":0.018169312,"about_ca_topic_score_gemma":0.028832795,"teacher_disagreement_score":0.018169312,"about_ca_system_score_codex":0.00034719997,"about_ca_system_score_gemma":0.00021008692,"threshold_uncertainty_score":0.03612709},"labels":[],"label_agreement":null},{"id":"W3101676849","doi":"10.1080/15481603.2020.1846948","title":"A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: a case study in Newfoundland","year":2020,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":160,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Institut National de la Recherche Scientifique; Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"","keywords":"Wetland; Swamp; Climate change; Environmental science; Normalized Difference Vegetation Index; Marsh; Context (archaeology); Geography; Satellite imagery; Physical geography; Remote sensing; Vegetation (pathology); Geospatial analysis; Hydrology (agriculture); Ecology; Geology","score_opus":0.029039588414777155,"score_gpt":0.2505494762025153,"score_spread":0.2215098877877381,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3101676849","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99808717,0.00010007843,0.00024519605,0.00008525472,0.0000032197383,0.000033852917,0.0007748965,0.000016943732,0.0006533235],"genre_scores_gemma":[0.9968828,0.0001530491,0.0011590217,0.00003510742,0.0000025544005,0.000014693673,0.0007797398,0.0000054993498,0.0009676475],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99971074,0.000032816264,0.000016011347,0.000060449824,0.00007732653,0.00010260909],"domain_scores_gemma":[0.9991393,0.00015342057,0.0001470258,0.00006223422,0.00036964906,0.00012835197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035948536,0.00024414645,0.0001814738,0.0007495207,0.00096301275,0.0006558263,0.000528184,0.00027267015,0.0002864925],"category_scores_gemma":[0.0008424918,0.00014890677,0.00027530614,0.0015086189,0.0005719403,0.0003097724,0.00035674762,0.00033071157,0.00006608793],"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":[0.00021389383,0.00025247212,0.9347594,0.00014223148,0.00014078079,0.0059664543,0.0035338022,0.006350909,0.006434697,0.0001994771,0.0024175365,0.039588414],"study_design_scores_gemma":[0.0000043974806,0.000037240425,0.9907766,0.000016397644,0.000032264543,0.00021346753,0.003276831,0.0038354872,0.00072124664,0.000014245025,0.0010585692,0.000013194876],"about_ca_topic_score_codex":0.9664455,"about_ca_topic_score_gemma":0.990355,"teacher_disagreement_score":0.033554494,"about_ca_system_score_codex":0.00868893,"about_ca_system_score_gemma":0.0041944254,"threshold_uncertainty_score":0.06750417},"labels":[],"label_agreement":null},{"id":"W3117236311","doi":"10.1080/15481603.2020.1857123","title":"Microwave-based vegetation descriptors in the parameterization of water cloud model at L-band for soil moisture retrieval over croplands","year":2020,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":45,"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":"Remote sensing; Environmental science; Water content; Normalized Difference Vegetation Index; Synthetic aperture radar; Vegetation (pathology); Enhanced vegetation index; Radar; Leaf area index; Soil science; Vegetation Index; Geography; Geology; Agronomy; Computer science","score_opus":0.020322138062365543,"score_gpt":0.22725655847779563,"score_spread":0.2069344204154301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3117236311","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90500695,0.0002921966,0.09342401,0.000034380726,0.000013527395,0.000045905166,0.00023272715,0.00021195992,0.0007383407],"genre_scores_gemma":[0.98693687,0.00008720914,0.012549827,0.000009012332,0.000004040232,0.00001719221,0.00024075704,0.000013900388,0.00014129585],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999325,0.000015609668,0.0000038958387,0.000017062735,0.000017455142,0.0000134014845],"domain_scores_gemma":[0.99990475,0.000035099212,0.00001881074,0.000013173105,0.000020583213,0.000007531594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025989412,0.00029039333,0.00021225215,0.00041879708,0.00011265269,0.00033457478,0.00036332777,0.00021292674,0.00021560337],"category_scores_gemma":[0.0005063615,0.00013650607,0.00038605,0.00043699416,0.0001281831,0.00050614093,0.00022363653,0.0002500804,0.00008920793],"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":[0.00017819426,0.0002033248,0.035647396,0.00009260356,0.00009656928,0.00014578647,0.00007240503,0.8169588,0.06332979,0.0011895631,0.00038429644,0.08170124],"study_design_scores_gemma":[0.000008786348,0.000015584626,0.007056254,0.0000029489047,0.000008698074,0.000013746369,0.000015365149,0.98941326,0.0031836603,0.00011806699,0.00015635652,0.000007225709],"about_ca_topic_score_codex":0.009207146,"about_ca_topic_score_gemma":0.008780084,"teacher_disagreement_score":0.009207146,"about_ca_system_score_codex":0.00034983043,"about_ca_system_score_gemma":0.00032637012,"threshold_uncertainty_score":0.01830715},"labels":[],"label_agreement":null},{"id":"W3128033494","doi":"10.1080/15481603.2020.1868198","title":"Recent land deformation detected by Sentinel-1A InSAR data (2016–2020) over Hanoi, Vietnam, and the relationship with groundwater level change","year":2021,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","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 Toronto","funders":"National Foundation for Science and Technology Development","keywords":"Interferometric synthetic aperture radar; Geography; Groundwater; Remote sensing; Geology; Synthetic aperture radar","score_opus":0.038715423363918075,"score_gpt":0.24200263348381218,"score_spread":0.2032872101198941,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128033494","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99764085,0.00007673835,0.00015677468,0.000045884746,0.0000046807977,0.0000064002224,0.0014971205,0.00000758278,0.00056392344],"genre_scores_gemma":[0.99706286,0.000113617876,0.00017662211,0.000014306821,0.0000043168034,0.0000080110085,0.002275746,0.000001992835,0.0003424946],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999119,0.0000061482365,0.000008012024,0.000026882632,0.000023085888,0.000023999162],"domain_scores_gemma":[0.99981815,0.00001622415,0.000062993415,0.00000932576,0.000059928436,0.000033295666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018436095,0.00020123506,0.000105421735,0.00047179704,0.0001774603,0.00031870915,0.00016297805,0.0001247134,0.0004852969],"category_scores_gemma":[0.00024147349,0.00010180918,0.0001332921,0.00072683516,0.00019964612,0.00026641588,0.00023070615,0.00011308266,0.00007570958],"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":[0.00007510566,0.000054316017,0.9740757,0.00005514438,0.00006848524,0.00045384964,0.00043350767,0.0058466163,0.0047826073,0.00019864299,0.0010068902,0.012949229],"study_design_scores_gemma":[0.0000034624838,0.00002764623,0.990694,0.000009355735,0.000020867355,0.000098752906,0.00075612863,0.006251449,0.00066394417,0.000038566894,0.0014280439,0.000007886482],"about_ca_topic_score_codex":0.111706816,"about_ca_topic_score_gemma":0.14613526,"teacher_disagreement_score":0.111706816,"about_ca_system_score_codex":0.0007079452,"about_ca_system_score_gemma":0.0005686274,"threshold_uncertainty_score":0.22211325},"labels":[],"label_agreement":null},{"id":"W3128618451","doi":"10.1080/15481603.2021.1877435","title":"Investigating different versions of PROSPECT and PROSAIL for estimating spectral and biophysical properties of photosynthetic and non-photosynthetic vegetation in mixed grasslands","year":2021,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":33,"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; University of Windsor; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Simon Fraser University","keywords":"Mean squared error; Canopy; Vegetation (pathology); Photosynthesis; Grassland ecosystem; Environmental science; Leaf area index; Remote sensing; Atmospheric radiative transfer codes; Radiative transfer; Atmospheric sciences; Mathematics; Geography; Ecosystem; Physics; Ecology; Botany; Statistics; Biology","score_opus":0.01126960147225656,"score_gpt":0.2114671474297644,"score_spread":0.20019754595750786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128618451","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9656662,0.00021975218,0.030553509,0.00013283164,0.00004124165,0.000052905554,0.00033060403,0.0012846588,0.0017183301],"genre_scores_gemma":[0.9757912,0.00006519396,0.023070555,0.000049419185,0.000009258974,0.000038828242,0.00048014178,0.00008761911,0.00040769164],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964154,0.00010496112,0.000023233768,0.000105057254,0.00007617449,0.00004900156],"domain_scores_gemma":[0.9992975,0.00033150698,0.00006382542,0.000080486534,0.00016287267,0.000063771884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014975707,0.00095995044,0.0005843081,0.00042728,0.00028227913,0.0008529433,0.0012799291,0.0010816997,0.00069541996],"category_scores_gemma":[0.0018476538,0.0004099876,0.000858523,0.00030325458,0.00040126484,0.001525185,0.000674971,0.0007152168,0.00018396972],"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":[0.0006650284,0.00034680095,0.02548022,0.00021292407,0.00023342845,0.0001511088,0.00020629635,0.9187582,0.014842937,0.0010644579,0.0006353388,0.037403293],"study_design_scores_gemma":[0.000028253315,0.00015823022,0.0041004396,0.000007994196,0.000027662862,0.000030711704,0.000048106136,0.9928964,0.002234446,0.00018243633,0.00026373198,0.000021633376],"about_ca_topic_score_codex":0.012134897,"about_ca_topic_score_gemma":0.008703946,"teacher_disagreement_score":0.012134897,"about_ca_system_score_codex":0.00043283112,"about_ca_system_score_gemma":0.0006824365,"threshold_uncertainty_score":0.024128556},"labels":[],"label_agreement":null},{"id":"W3151655794","doi":"10.1080/15481603.2021.1906056","title":"The synergistic use of microwave coarse-scale measurements and two adopted high-resolution indices driven from long-term T-V scatter plot for fine-scale soil moisture estimation","year":2021,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","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":"Downscaling; Water content; Environmental science; Remote sensing; Moisture; Radiometer; Mean squared error; Soil science; Scale (ratio); Correlation coefficient; Microwave; Meteorology; Mathematics; Geography; Statistics; Geology; Precipitation; Computer science","score_opus":0.026057262679263955,"score_gpt":0.2469818902257801,"score_spread":0.22092462754651615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3151655794","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":[0.46993944,0.00058755954,0.5254494,0.0000699233,0.000055713914,0.00007036172,0.00042442855,0.00095133105,0.002451839],"genre_scores_gemma":[0.8036057,0.00020522099,0.19507085,0.000023870352,0.00003629624,0.000046378722,0.0004223618,0.000044783123,0.0005446344],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999838,0.00003797505,0.000007887324,0.00006243699,0.000041915897,0.0000117937325],"domain_scores_gemma":[0.999814,0.000049172282,0.00003428669,0.000040712337,0.000049750768,0.000012085218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035237143,0.0005175151,0.00030521912,0.0008674378,0.00013869735,0.00043876102,0.00027594736,0.00025909228,0.00041630733],"category_scores_gemma":[0.0005774585,0.00015162361,0.00041670457,0.0010082077,0.00012575509,0.0006518203,0.00038188903,0.00025364078,0.00023724792],"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":[0.00024193681,0.00022974353,0.106993444,0.00031916177,0.00033374832,0.00017968273,0.00019398684,0.068524815,0.2608448,0.0012367311,0.00076779246,0.5601341],"study_design_scores_gemma":[0.00005580558,0.00039784046,0.20315237,0.00004033895,0.00031570726,0.00037844776,0.0002315275,0.7142315,0.074430235,0.001730557,0.0049168877,0.00011883116],"about_ca_topic_score_codex":0.0011916148,"about_ca_topic_score_gemma":0.0041966964,"teacher_disagreement_score":0.0011916148,"about_ca_system_score_codex":0.00012887765,"about_ca_system_score_gemma":0.0002232192,"threshold_uncertainty_score":0.0023693442},"labels":[],"label_agreement":null},{"id":"W3194048014","doi":"10.1080/15481603.2021.1952541","title":"Wetland mapping with multi-temporal sentinel-1 &amp; -2 imagery (2017 – 2020) and LiDAR data in the grassland natural region of alberta","year":2021,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","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":"Alberta Environment and Protected Areas","funders":"","keywords":"Wetland; Swamp; Marsh; Remote sensing; Environmental science; Random forest; Ground truth; Grassland; Habitat; Topographic Wetness Index; Synthetic aperture radar; Lidar; Geography; Hydrology (agriculture); Ecology; Digital elevation model; Geology","score_opus":0.029833335992671326,"score_gpt":0.26193086611786165,"score_spread":0.23209753012519033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194048014","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9877967,0.00024108011,0.00066103187,0.00011860931,0.000017504053,0.000022089665,0.008439605,0.00018292721,0.0025203095],"genre_scores_gemma":[0.9710768,0.00019742441,0.0034879085,0.000036951602,0.000013464684,0.000011419072,0.02347666,0.000019547368,0.0016799715],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99974865,0.00001593399,0.0000096930235,0.000050954648,0.00009688757,0.00007793908],"domain_scores_gemma":[0.99967456,0.000026714162,0.0000324281,0.000024675102,0.0001825641,0.000059110807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004869983,0.0003948061,0.00017356548,0.0015085054,0.00050136517,0.00069952547,0.0005373824,0.00020370723,0.0004370352],"category_scores_gemma":[0.00050733256,0.00013238931,0.00026331714,0.0017509417,0.00030060147,0.0002621953,0.00034581716,0.00023267353,0.00016890638],"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":[0.0009984911,0.00037195423,0.69137055,0.00023420813,0.00026509052,0.0011936078,0.0006315712,0.088546544,0.0260258,0.0011456822,0.02020127,0.16901524],"study_design_scores_gemma":[0.00005000032,0.000036803423,0.8815543,0.000050993684,0.0000900843,0.00014369644,0.0012605379,0.10338488,0.0041274806,0.00020310028,0.009055754,0.000042298478],"about_ca_topic_score_codex":0.9024361,"about_ca_topic_score_gemma":0.94357085,"teacher_disagreement_score":0.09756392,"about_ca_system_score_codex":0.0043847486,"about_ca_system_score_gemma":0.0034191862,"threshold_uncertainty_score":0.19627696},"labels":[],"label_agreement":null},{"id":"W3197221586","doi":"10.1080/15481603.2021.1952542","title":"Oil spill detection from Synthetic Aperture Radar Earth observations: a meta-analysis and comprehensive review","year":2021,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Oil Spill Detection and Mitigation","field":"Environmental Science","cited_by":69,"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; Institut National de la Recherche Scientifique; Centre For Cold Ocean Resources Engineering; Government of Canada; Memorial University of Newfoundland","funders":"","keywords":"Remote sensing; Synthetic aperture radar; Oil spill; Geology; Earth observation; Meteorology; Environmental science; Geography; Engineering; Satellite; Petroleum engineering; Aerospace engineering","score_opus":0.046422948671754775,"score_gpt":0.250245786244724,"score_spread":0.2038228375729692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197221586","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017772691,0.9947089,0.0017373915,0.0003213937,0.00011254716,0.000026465816,0.0007120909,0.000029211631,0.00057465734],"genre_scores_gemma":[0.014024633,0.98148835,0.0030188055,0.00020486972,0.00013997508,0.000035573223,0.00090761995,0.000020982838,0.00015929119],"study_design_codex":"design_other","study_design_gemma":"meta_analysis","domain_scores_codex":[0.9986218,0.0003732511,0.00036852667,0.00024802543,0.00032547401,0.00006282313],"domain_scores_gemma":[0.98962516,0.0075838044,0.0009828751,0.00035696372,0.0013296353,0.00012153942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003897974,0.001669623,0.0028465579,0.012179443,0.00025841803,0.002678018,0.0014240614,0.0010165796,0.0022340606],"category_scores_gemma":[0.012810639,0.0006221869,0.0061188214,0.010613529,0.0003726155,0.0020527812,0.0008265576,0.0007916752,0.00068543747],"study_design_candidate":"meta_analysis","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027252556,0.00012930385,0.012352407,0.2852639,0.020250006,0.00047117507,0.0005004187,0.0049943966,0.0022706774,0.0022974093,0.012348918,0.6588488],"study_design_scores_gemma":[0.00012126552,0.001087895,0.057677507,0.25422755,0.16640145,0.0022472937,0.001970489,0.0075170565,0.0055385325,0.0069409916,0.49569523,0.0005746725],"about_ca_topic_score_codex":0.0050300914,"about_ca_topic_score_gemma":0.0076181088,"teacher_disagreement_score":0.012179443,"about_ca_system_score_codex":0.00072731706,"about_ca_system_score_gemma":0.0035703105,"threshold_uncertainty_score":0.020614743},"labels":[],"label_agreement":null},{"id":"W3200616406","doi":"10.1080/15481603.2021.1965399","title":"Deep Forest classifier for wetland mapping using the combination of Sentinel-1 and Sentinel-2 data","year":2021,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":52,"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 For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"","keywords":"Random forest; Gradient boosting; Artificial intelligence; Classifier (UML); Boosting (machine learning); Decision tree; Computer science; Wetland; Machine learning; Naive Bayes classifier; Convolutional neural network; Remote sensing; Deep learning; Statistical classification; Pattern recognition (psychology); Support vector machine; Geography; Ecology; Biology","score_opus":0.05014478185543613,"score_gpt":0.28543198219975774,"score_spread":0.2352872003443216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200616406","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52152973,0.0031067382,0.44644004,0.00077850296,0.0009215796,0.00036838354,0.006509361,0.011219796,0.009125867],"genre_scores_gemma":[0.74690205,0.00067955593,0.23370674,0.00032099916,0.00012783452,0.00020288516,0.012887316,0.00018951346,0.0049830275],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996275,0.00004438142,0.000022194934,0.00009064254,0.00011536976,0.00009991736],"domain_scores_gemma":[0.99971277,0.00006214191,0.000027351742,0.000024872998,0.00014691263,0.000025862098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089848065,0.0010437067,0.00072562404,0.0016060851,0.00040617387,0.0004154223,0.0007759354,0.00063149614,0.0013550783],"category_scores_gemma":[0.0009865214,0.00019783001,0.00087897165,0.00094321114,0.00015616407,0.0008432596,0.0004985927,0.00074138504,0.000835702],"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":[0.00088099815,0.0006258841,0.024572821,0.00030422336,0.00025553245,0.0004883915,0.00012799531,0.09999926,0.041160848,0.0013254604,0.030688528,0.7995701],"study_design_scores_gemma":[0.000047065874,0.00013825911,0.008308932,0.00003218776,0.00006300501,0.0001323464,0.00010744274,0.9738588,0.012031375,0.0010580595,0.0041904794,0.000032074015],"about_ca_topic_score_codex":0.012090005,"about_ca_topic_score_gemma":0.015994573,"teacher_disagreement_score":0.012090005,"about_ca_system_score_codex":0.0003862602,"about_ca_system_score_gemma":0.00084723433,"threshold_uncertainty_score":0.024039268},"labels":[],"label_agreement":null},{"id":"W4224232894","doi":"10.1080/15481603.2022.2060596","title":"Gap-filling MODIS daily aerosol optical depth products by developing a spatiotemporal fitting algorithm","year":2022,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":41,"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":"Ministry of Innovation and Advanced Education","keywords":"Moderate-resolution imaging spectroradiometer; Remote sensing; Residual; Aerosol; Environmental science; Pixel; Atmospheric correction; Spectroradiometer; Image resolution; Smoothing; Algorithm; Meteorology; Computer science; Geography; Mathematics; Satellite; Artificial intelligence; Statistics","score_opus":0.018545212223598343,"score_gpt":0.23387727390121074,"score_spread":0.2153320616776124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224232894","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":[0.038316607,0.00014447358,0.9588316,0.00006683465,0.000044688568,0.000053373722,0.00023891512,0.0018515788,0.00045192902],"genre_scores_gemma":[0.15210676,0.00017367407,0.84474987,0.00007038782,0.0000349605,0.00012306988,0.0014043096,0.00035054662,0.0009863708],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994887,0.000047608934,0.00006516946,0.00019350495,0.00016751744,0.000037478247],"domain_scores_gemma":[0.9993968,0.0001086566,0.00008709555,0.000103846796,0.0002808188,0.00002282393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007752174,0.00066785625,0.00061482284,0.0010406224,0.00041697064,0.0007405554,0.0011612999,0.0006416298,0.0011629842],"category_scores_gemma":[0.0025565263,0.00040490605,0.0012182676,0.0014022005,0.0002483989,0.0013139178,0.0010374216,0.00072559086,0.00068145],"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":[0.00016446204,0.00018038091,0.012708458,0.00017842653,0.0001243414,0.00026112475,0.00035839155,0.32983246,0.031994406,0.0048800833,0.0049580587,0.61435944],"study_design_scores_gemma":[0.000008572469,0.000020107922,0.0011944482,0.0000052129994,0.000012371107,0.00005206196,0.000031124582,0.99224186,0.0034486055,0.0008612072,0.002113742,0.000010743265],"about_ca_topic_score_codex":0.010586465,"about_ca_topic_score_gemma":0.0075231567,"teacher_disagreement_score":0.010586465,"about_ca_system_score_codex":0.00045205033,"about_ca_system_score_gemma":0.0011439931,"threshold_uncertainty_score":0.021049678},"labels":[],"label_agreement":null},{"id":"W4306929210","doi":"10.1080/15481603.2022.2134620","title":"Tracking transient boreal wetland inundation with Sentinel-1 SAR: Peace-Athabasca Delta, Alberta and Yukon Flats, Alaska","year":2022,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Peatlands and Wetlands Ecology","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":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Wetland; Boreal; Environmental science; Vegetation (pathology); Remote sensing; Synthetic aperture radar; Taiga; Hydrology (agriculture); Physical geography; Geology; Geography; Ecology; Forestry","score_opus":0.00855713010651026,"score_gpt":0.21177787870302772,"score_spread":0.20322074859651745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306929210","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99693894,0.00006325221,0.0005086805,0.000032139447,0.0000056930407,0.000011873352,0.0011929017,0.000053712178,0.0011927574],"genre_scores_gemma":[0.9947596,0.00009021689,0.001712945,0.000015125331,0.0000037927634,0.000014006933,0.0026084122,0.0000048379807,0.0007911292],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999057,0.0000064892783,0.0000041338703,0.000024135994,0.000033743672,0.000025774796],"domain_scores_gemma":[0.999845,0.0000111524305,0.000022567754,0.000007744463,0.000076580385,0.000036925143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021354949,0.00028146806,0.00012154919,0.0006654581,0.00043189325,0.00041025892,0.00037131464,0.00012759068,0.0002966345],"category_scores_gemma":[0.00023626824,0.00007882946,0.00012312882,0.0008519353,0.00020827015,0.00017688879,0.00027963318,0.00010570742,0.000064113054],"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":[0.00016922291,0.000099333316,0.95051754,0.00003323051,0.000046803754,0.00036969755,0.0005620196,0.010681514,0.0068589067,0.00021373999,0.0021644179,0.028283617],"study_design_scores_gemma":[0.000015848906,0.00002886651,0.9638842,0.000012937115,0.000031426174,0.00008644485,0.0015772353,0.031378075,0.0012863877,0.0000899802,0.001593603,0.000014871228],"about_ca_topic_score_codex":0.700805,"about_ca_topic_score_gemma":0.84834933,"teacher_disagreement_score":0.299195,"about_ca_system_score_codex":0.001198798,"about_ca_system_score_gemma":0.0017032129,"threshold_uncertainty_score":0.601914},"labels":[],"label_agreement":null},{"id":"W4377862149","doi":"10.1080/15481603.2023.2214994","title":"Contribution of topographic features and categorization uncertainty for a tree species classification in the boreal biome of Northern Ontario","year":2023,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"York University","funders":"Ministry of Agriculture, Food and Rural Affairs; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Agriculture, Food and Rural Affairs; Ontario Ministry of Natural Resources and Forestry; Ministry of Natural Resources","keywords":"Biome; Taiga; Boreal; Categorization; Geography; Physical geography; Remote sensing; Forestry; Cartography; Environmental science; Ecology; Archaeology; Ecosystem; Computer science; Artificial intelligence; Biology","score_opus":0.014888074303567101,"score_gpt":0.22987898322497247,"score_spread":0.21499090892140538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377862149","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99792373,0.00018939754,0.00079065666,0.000060852046,0.0000036274214,0.000010245581,0.00026339066,0.000016159825,0.00074191496],"genre_scores_gemma":[0.9991779,0.000035070523,0.00039547123,0.000004905893,0.000002197653,0.0000016483083,0.00021668588,0.0000022725535,0.00016394288],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99942976,0.00006607093,0.000039246243,0.00014677065,0.00021611224,0.00010199447],"domain_scores_gemma":[0.9982315,0.00068168953,0.0002836337,0.00010084004,0.0005929014,0.00010944956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007064689,0.00029298765,0.00025797,0.0007596843,0.00095791847,0.0014061636,0.00037066333,0.0003219739,0.00042705765],"category_scores_gemma":[0.0049658087,0.00015298217,0.00028398188,0.00071841,0.00039219053,0.0005677801,0.0004820426,0.00022325879,0.000071042974],"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":[0.00022723601,0.000027991198,0.92728925,0.00006732032,0.00011514278,0.00029944506,0.00067134236,0.019521458,0.0055091875,0.0002320274,0.0004179633,0.045621727],"study_design_scores_gemma":[0.0000051514107,0.000024373174,0.94259083,0.00002011609,0.000053892723,0.00012940158,0.00095403043,0.054492008,0.0008231793,0.00023518551,0.00064657297,0.000025386184],"about_ca_topic_score_codex":0.67283744,"about_ca_topic_score_gemma":0.80663705,"teacher_disagreement_score":0.32716256,"about_ca_system_score_codex":0.0040572616,"about_ca_system_score_gemma":0.002623872,"threshold_uncertainty_score":0.65817857},"labels":[],"label_agreement":null},{"id":"W4404276897","doi":"10.1080/15481603.2024.2427326","title":"Airborne lidar intensity correction for mapping snow cover extent and effective grain size in mountainous terrain","year":2024,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Remote Sensing and LiDAR Applications","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":"Geological Survey of Canada; Natural Resources Canada; University of Northern British Columbia","funders":"National Aeronautics and Space Administration; National Oceanic and Atmospheric Administration; National Science Foundation","keywords":"Terrain; Lidar; Snow cover; Remote sensing; Snow; Geography; Intensity (physics); Physical geography; Environmental science; Cover (algebra); Cartography; Meteorology","score_opus":0.008643202064637255,"score_gpt":0.2419766821646696,"score_spread":0.23333348010003235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404276897","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83699614,0.0005243142,0.15189211,0.00015951718,0.00005222146,0.00013581043,0.0017226116,0.003428924,0.0050884373],"genre_scores_gemma":[0.8426139,0.00020684705,0.15358311,0.00005924267,0.000022151009,0.00007556202,0.0020381317,0.0001944414,0.0012065863],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984443,0.00001277876,0.000006623239,0.000042584943,0.00006943363,0.000024004064],"domain_scores_gemma":[0.99981064,0.000023732953,0.00002950655,0.000023734403,0.00010324473,0.000009163894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026134626,0.00029093315,0.00021667521,0.00066347665,0.00034609556,0.0004207659,0.00044404794,0.00021321786,0.00096419937],"category_scores_gemma":[0.0005162083,0.00021392018,0.00028954513,0.0007359466,0.00008916022,0.0003385383,0.00037469665,0.0002855076,0.00039945712],"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":[0.00028315696,0.00019702733,0.11027747,0.0003254911,0.00014776824,0.00042642935,0.0009243199,0.030645767,0.4826653,0.0009758422,0.0046440666,0.36848745],"study_design_scores_gemma":[0.00011304782,0.00026673998,0.27291206,0.000077465054,0.00018242652,0.00055084104,0.0009153342,0.5399446,0.16556586,0.0016046073,0.017764868,0.000102158556],"about_ca_topic_score_codex":0.012893366,"about_ca_topic_score_gemma":0.026249267,"teacher_disagreement_score":0.012893366,"about_ca_system_score_codex":0.00027943304,"about_ca_system_score_gemma":0.00046102935,"threshold_uncertainty_score":0.025636673},"labels":[],"label_agreement":null},{"id":"W4404366123","doi":"10.1080/15481603.2024.2427309","title":"Diversity of lakes and ponds in the forest-tundra ecozone: from limnicity to limnodiversity","year":2024,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Peatlands and Wetlands Ecology","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":"Université Laval; Center for Northern Studies","funders":"Universidade de Lisboa; Foundation for Science and Technology; Université Laval","keywords":"Tundra; Geography; Diversity (politics); Environmental science; Ecology; Physical geography; Remote sensing; Ecosystem; Biology","score_opus":0.013437255722396573,"score_gpt":0.22139118222623477,"score_spread":0.2079539265038382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404366123","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9996443,0.000030682,0.000042630738,0.000003904802,2.3522303e-7,0.0000027078693,0.00011430718,0.0000028125608,0.00015835238],"genre_scores_gemma":[0.99952567,0.000024990448,0.00017902954,0.0000039946294,4.901621e-7,0.0000030727915,0.00018654477,9.796238e-7,0.00007528886],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999856,0.00002692839,0.0000117065165,0.000048872418,0.000022254528,0.000034261495],"domain_scores_gemma":[0.9997429,0.000035948407,0.00007920798,0.000019025747,0.000060926075,0.00006192832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025007277,0.00015670956,0.00015443993,0.0011702351,0.0004315953,0.00050919,0.00020433226,0.00012248103,0.0005215963],"category_scores_gemma":[0.0003985507,0.0001358379,0.00015548224,0.0009176692,0.00039577734,0.00024911494,0.0005194559,0.00011144354,0.000087891196],"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":[0.000017859498,0.000005809649,0.99629885,0.0000047507724,0.000016604012,0.00001769613,0.0002985046,0.00006281036,0.0012837122,0.000013554422,0.000019542302,0.0019602324],"study_design_scores_gemma":[6.2052663e-7,0.0000048398547,0.9994973,0.00000142557,0.0000030810672,0.000021183168,0.00023274933,0.0001323144,0.000054455842,0.00000465052,0.000046504254,0.0000010095597],"about_ca_topic_score_codex":0.17704467,"about_ca_topic_score_gemma":0.43661705,"teacher_disagreement_score":0.17704467,"about_ca_system_score_codex":0.00066375046,"about_ca_system_score_gemma":0.00048349684,"threshold_uncertainty_score":0.35202837},"labels":[],"label_agreement":null},{"id":"W4410119516","doi":"10.1080/15481603.2025.2498188","title":"Multi-sensor near-realtime burnt area monitoring using a superpixel-based graph convolutional network approach","year":2025,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":5,"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":"Graph; Computer science; Geography; Remote sensing; Wireless sensor network; Cartography; Real-time computing; Environmental science; Artificial intelligence; Computer network; Theoretical computer science","score_opus":0.023790845152495715,"score_gpt":0.25303352891978564,"score_spread":0.22924268376728993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410119516","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":[0.31750768,0.00075538753,0.66899616,0.00032379408,0.000106639185,0.000106722815,0.0014640667,0.005390041,0.0053496263],"genre_scores_gemma":[0.85539854,0.0002482617,0.13745224,0.00015294914,0.00003674601,0.00005155655,0.002499638,0.00012645441,0.0040336666],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997764,0.000023203,0.00000779369,0.00009469302,0.000056790217,0.000041195173],"domain_scores_gemma":[0.99980956,0.00004006255,0.000033241162,0.00002942769,0.00006991519,0.00001772818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003033046,0.0008411161,0.00044999566,0.0011313257,0.00018967473,0.0005071662,0.0009247098,0.0005417309,0.0009869556],"category_scores_gemma":[0.0005020112,0.00030611435,0.0005829678,0.00074595254,0.00022629884,0.00066842645,0.00046982148,0.0005482801,0.0002841379],"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":[0.00030104682,0.00023747553,0.00887432,0.000088951936,0.00020163966,0.0002233698,0.00006842567,0.6681562,0.027514916,0.0014684162,0.0032492988,0.2896158],"study_design_scores_gemma":[0.0000024995904,0.000012779151,0.0013402799,0.0000023300722,0.0000095318555,0.000015955653,0.000006180295,0.99633884,0.0015594553,0.0004935577,0.00021498126,0.0000037030368],"about_ca_topic_score_codex":0.026942816,"about_ca_topic_score_gemma":0.045692336,"teacher_disagreement_score":0.026942816,"about_ca_system_score_codex":0.0008491623,"about_ca_system_score_gemma":0.0006571825,"threshold_uncertainty_score":0.053572},"labels":[],"label_agreement":null},{"id":"W4412138355","doi":"10.1080/15481603.2025.2527990","title":"Nighttime satellite land surface temperature for urban applications: achievements, challenges, and future prospects","year":2025,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Urban Heat Island Mitigation","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":"Artificial Intelligence in Medicine (Canada)","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; National Research Foundation of Korea; Korea Meteorological Administration; Ulsan National Institute of Science and Technology; National Research Foundation","keywords":"Satellite; Remote sensing; Urban heat island; Geography; Environmental science; Meteorology; Climatology; Geology; Engineering","score_opus":0.008186870721646144,"score_gpt":0.22388794654404742,"score_spread":0.21570107582240128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412138355","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029675616,0.9831101,0.004708281,0.0038559746,0.0012132998,0.000020072843,0.00034580362,0.000060168095,0.0037188174],"genre_scores_gemma":[0.027180772,0.9623103,0.0060321973,0.0011294951,0.0016033899,0.000036437494,0.00044202735,0.00005499217,0.0012103859],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990115,0.0003948085,0.000094578536,0.000174522,0.0002694461,0.000055027194],"domain_scores_gemma":[0.9945807,0.0029504693,0.0003830533,0.00024144567,0.0017049492,0.00013941045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034788605,0.0005609973,0.0008043806,0.0014431573,0.00025473887,0.0022641777,0.00093112834,0.0010444372,0.0023475382],"category_scores_gemma":[0.0043284986,0.00019602913,0.0011421704,0.0033302864,0.0006711787,0.0023791012,0.0008013044,0.0011778309,0.00067973306],"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":[0.00008891682,0.00003629128,0.0057265153,0.016838655,0.00023668047,0.00012438378,0.00041866704,0.0025193498,0.0039920127,0.011533835,0.022346336,0.9361383],"study_design_scores_gemma":[0.000012905769,0.00013219188,0.013544894,0.008186131,0.00043017245,0.00035849243,0.0011315574,0.0034421755,0.0034772996,0.012632317,0.9565531,0.0000987595],"about_ca_topic_score_codex":0.0044874046,"about_ca_topic_score_gemma":0.0058489596,"teacher_disagreement_score":0.0044874046,"about_ca_system_score_codex":0.00070863136,"about_ca_system_score_gemma":0.0023964948,"threshold_uncertainty_score":0.018398166},"labels":[],"label_agreement":null},{"id":"W4415568553","doi":"10.1080/15481603.2025.2571244","title":"Optimizing GAIN model to improve AOD imputation using MODIS MAIAC data and multi-source data fusion as an example","year":2025,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","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":"Goddard Space Flight Center; National Science Foundation","keywords":"Missing data; Imputation (statistics); Moderate-resolution imaging spectroradiometer; Mean squared error; Leverage (statistics); Data modeling; Data quality","score_opus":0.0529087944506252,"score_gpt":0.29601914505654436,"score_spread":0.24311035060591918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415568553","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":[0.14390558,0.00084319257,0.8480414,0.0013537949,0.00015963921,0.0001121404,0.00068356167,0.00216,0.0027406102],"genre_scores_gemma":[0.8889702,0.000254948,0.105030455,0.00059879397,0.00006825883,0.00014030749,0.00153976,0.00017196163,0.0032252648],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992042,0.0002568074,0.000040041352,0.00023999806,0.00013520454,0.0001237227],"domain_scores_gemma":[0.9977798,0.0012961244,0.00015117245,0.0002098013,0.00049469405,0.0000683701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037626815,0.0011586574,0.0009078291,0.0006185719,0.0005785032,0.00088908116,0.0016363949,0.0013323849,0.0015041162],"category_scores_gemma":[0.007179818,0.0004230784,0.0012425222,0.00057835405,0.0007288192,0.001503265,0.0015068128,0.002222966,0.0005411142],"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":[0.00006525459,0.000043150394,0.0037104434,0.0000312098,0.000049565868,0.000060353963,0.00005518709,0.9636301,0.000939576,0.0018217267,0.0013424784,0.028250936],"study_design_scores_gemma":[0.0000042606634,0.000015140223,0.0003399887,0.0000048380675,0.0000073511937,0.000010885684,0.000007002587,0.9977095,0.0004442852,0.0012261879,0.00022531314,0.0000052495093],"about_ca_topic_score_codex":0.0161206,"about_ca_topic_score_gemma":0.014790631,"teacher_disagreement_score":0.0161206,"about_ca_system_score_codex":0.0012372673,"about_ca_system_score_gemma":0.0013970461,"threshold_uncertainty_score":0.03205359},"labels":[],"label_agreement":null},{"id":"W4416827870","doi":"10.1080/15481603.2025.2587939","title":"A new framework for mapping rubber plantations through the combination of semiautomatic sample migration, dynamic phenology, and change detection variables based on time series Landsat images","year":2025,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Phenology; Natural rubber; Change detection; Sample (material); Climate change; Variable (mathematics)","score_opus":0.010040303995085605,"score_gpt":0.24112319859634956,"score_spread":0.23108289460126397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416827870","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":[0.011633559,0.00008970393,0.9865628,0.000050510218,0.000016449705,0.00005255523,0.00026940904,0.00091332797,0.00041167546],"genre_scores_gemma":[0.1303296,0.00012691302,0.8675368,0.00004125598,0.000038802635,0.00015653904,0.00095743564,0.00007958914,0.0007331392],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996019,0.00005770825,0.000025481932,0.0001706908,0.000104898914,0.00003931662],"domain_scores_gemma":[0.9996735,0.000056609184,0.00007902676,0.000039953786,0.00012423434,0.000026835422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006245908,0.0008484138,0.00058204535,0.0029597145,0.00037705246,0.00078313274,0.0010125557,0.00037215612,0.00055802846],"category_scores_gemma":[0.00087415817,0.00028710853,0.0009835421,0.002069427,0.00040025054,0.0009319884,0.0008121336,0.000615334,0.00023242143],"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":[0.00009276981,0.0002405981,0.018586444,0.00019615148,0.00019353762,0.0002279141,0.00040379964,0.18505809,0.035157077,0.011525274,0.0040944223,0.74422395],"study_design_scores_gemma":[0.000012686807,0.000080073885,0.012239762,0.000021817183,0.000052123167,0.00018075536,0.00013019382,0.9706886,0.005146131,0.005812531,0.0055858702,0.000049598435],"about_ca_topic_score_codex":0.018225122,"about_ca_topic_score_gemma":0.027601136,"teacher_disagreement_score":0.018225122,"about_ca_system_score_codex":0.0005341012,"about_ca_system_score_gemma":0.0009985617,"threshold_uncertainty_score":0.036238074},"labels":[],"label_agreement":null},{"id":"W4417495640","doi":"10.1080/15481603.2025.2602215","title":"A spatiotemporal adaptive local search method for tracking congestion propagation in dynamic networks","year":2025,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Adaptability; Adjacency list; Scalability; Traffic congestion; Adjacency matrix; Network congestion; Robustness (evolution); Graph","score_opus":0.015103199290243527,"score_gpt":0.2906513671243586,"score_spread":0.27554816783411507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417495640","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":[0.036506865,0.00040417703,0.9597558,0.00024383259,0.00004919678,0.000066700435,0.00020166485,0.0014537266,0.0013179918],"genre_scores_gemma":[0.64222807,0.0002768376,0.3522705,0.0002959588,0.00007659446,0.0002823924,0.0009647233,0.0002816705,0.0033233014],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996226,0.00008459108,0.000025680947,0.000115085124,0.00010543838,0.0000465724],"domain_scores_gemma":[0.9989795,0.00047913703,0.00014688904,0.00007775682,0.00024960708,0.00006718231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007948414,0.0008779985,0.00090119475,0.0014942901,0.00048534223,0.00071918406,0.0018232942,0.00089908397,0.0016404736],"category_scores_gemma":[0.0037101882,0.00044340352,0.00075026,0.0014680338,0.0005313987,0.0013199075,0.000947666,0.0008184981,0.0004211999],"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":[0.00012973927,0.00009190333,0.0033643895,0.00010193826,0.00010898719,0.00013011335,0.000107784865,0.8643835,0.0059958715,0.004874669,0.003408669,0.117302336],"study_design_scores_gemma":[0.0000047171015,0.0000070944297,0.000084182706,0.0000016816008,0.000003874746,0.000007721773,0.0000050488816,0.9989712,0.0002122283,0.0005774242,0.00012268736,0.0000021525116],"about_ca_topic_score_codex":0.01463278,"about_ca_topic_score_gemma":0.020347798,"teacher_disagreement_score":0.01463278,"about_ca_system_score_codex":0.0008141537,"about_ca_system_score_gemma":0.0014095939,"threshold_uncertainty_score":0.029095232},"labels":[],"label_agreement":null},{"id":"W7117471169","doi":"10.1080/15481603.2025.2609352","title":"All-sky hourly estimation over East Asia using Himawari-8 AHI and multi-source data: investigating the main climatic drivers of afternoon depression and intraday variability in gross primary productivity","year":2025,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Meteorological Phenomena and Simulations","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":"Artificial Intelligence in Medicine (Canada)","funders":"Korea Environmental Industry and Technology Institute","keywords":"Geostationary orbit; Cloud cover; Estimation; Productivity; Consistency (knowledge bases); Primary production; Latent heat; Land cover; Sensible heat","score_opus":0.046334712864766966,"score_gpt":0.2760356163120472,"score_spread":0.22970090344728022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117471169","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9928635,0.00016544905,0.0045049093,0.000075166136,0.000019035522,0.000015678397,0.0015867824,0.00011498901,0.0006545001],"genre_scores_gemma":[0.99275655,0.000108047214,0.0044964906,0.00001698908,0.000016585118,0.000011257575,0.0023981738,0.000015688283,0.00018019647],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99985695,0.000030582207,0.000011724907,0.000051326555,0.000022923723,0.000026528354],"domain_scores_gemma":[0.99966323,0.00007410319,0.000055052482,0.000055824486,0.000108852255,0.000043016054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005055204,0.0006648289,0.0003253487,0.00084491534,0.0002480607,0.0007000216,0.00048605984,0.00034917353,0.00040320258],"category_scores_gemma":[0.0008296548,0.0002596623,0.0008955257,0.0012661386,0.00016683132,0.0005920838,0.0004402664,0.0003199775,0.00015164298],"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":[0.00030337015,0.00024346462,0.755961,0.00018859762,0.0009542234,0.0006663565,0.00066685677,0.16027087,0.018333845,0.00090802426,0.0020946402,0.059408784],"study_design_scores_gemma":[0.0000334241,0.00004239875,0.48254395,0.00002390543,0.00023976105,0.00007480367,0.0004395781,0.51155984,0.0033319541,0.00032830134,0.0013354452,0.000046668305],"about_ca_topic_score_codex":0.049732417,"about_ca_topic_score_gemma":0.044257153,"teacher_disagreement_score":0.049732417,"about_ca_system_score_codex":0.00044442763,"about_ca_system_score_gemma":0.0006693039,"threshold_uncertainty_score":0.098885894},"labels":[],"label_agreement":null}]}