{"id":"W3150188978","doi":"","title":"Monitoring Forest Fire with MODIS-NDVI Images in Beijing","year":2006,"lang":"en","type":"article","venue":"中国林业科技(英文版)","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Normalized Difference Vegetation Index; Environmental science; Beijing; Remote sensing; Meteorology; Moisture; Geography; Geology; China; Climate change","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002685143,0.0002586567,0.0002829206,0.0006665511,0.0002893067,0.0003021179,0.0003378227,0.0001854861,0.0004903061],"category_scores_gemma":[0.0005743549,0.0001786768,0.00009911832,0.0009195966,0.0001192949,0.0004224168,0.0002464078,0.0001142648,0.0001400903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001331118,"about_ca_system_score_gemma":0.00035148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0489631,"about_ca_topic_score_gemma":0.103053,"domain_scores_codex":[0.9998569,0.00002307634,0.00001285642,0.00003641893,0.0000492023,0.00002142406],"domain_scores_gemma":[0.9997943,0.00002347326,0.0000545056,0.00002332673,0.00006240165,0.00004193414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005863219,0.0002748929,0.7935104,0.000178779,0.0001959302,0.0006919895,0.0005021701,0.01729344,0.0419697,0.0002684434,0.002354606,0.1421733],"study_design_scores_gemma":[0.00002346981,0.00004311517,0.9637107,0.000004902594,0.00004078047,0.0000741023,0.0001465827,0.03043976,0.004684795,0.00007435729,0.0007451769,0.00001231208],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977015,0.00007574123,0.0005919588,0.00002865638,0.000002522539,0.00001453452,0.0004210699,0.00004546318,0.001118666],"genre_scores_gemma":[0.9969534,0.00006610903,0.001620239,0.000006801713,0.000003167697,0.00001474726,0.0008123725,0.000005209283,0.0005178862],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0489631,"threshold_uncertainty_score":0.0973562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00673728150171315,"score_gpt":0.1793674073610103,"score_spread":0.1726301258592972,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}