{"id":"W4309459153","doi":"10.3390/rs14225826","title":"Regional Variability and Driving Forces behind Forest Fires in Sweden","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Cooperation in Science and Technology; International Institute for Applied Systems Analysis; Klima- und Energiefonds","keywords":"Taiga; Geography; Boreal; Physical geography; Environmental science; Forestry","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":[0.0007062494,0.0002508637,0.0003711694,0.0009842915,0.0003748614,0.001162273,0.000304964,0.0003580877,0.0009187901],"category_scores_gemma":[0.001629285,0.0002335973,0.001017069,0.0008985222,0.0002262624,0.000313487,0.000626014,0.0003561155,0.0004043963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004055635,"about_ca_system_score_gemma":0.0004149248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02353526,"about_ca_topic_score_gemma":0.02334599,"domain_scores_codex":[0.999632,0.00006711236,0.00004193997,0.0001422952,0.00004707906,0.00006953641],"domain_scores_gemma":[0.9993023,0.0002799318,0.0001665746,0.00007717222,0.0001013715,0.00007274093],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007893499,0.00002710902,0.9881424,0.00002147293,0.0001544766,0.000252914,0.0001833703,0.005447912,0.0003096513,0.0001415774,0.0004067261,0.004833328],"study_design_scores_gemma":[0.000005041786,0.00001678894,0.9882026,0.00003816514,0.00006620061,0.0002269334,0.000939675,0.009297422,0.0001197319,0.0003119552,0.0007607717,0.00001467135],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998478,0.0002567672,0.0002392717,0.00004260184,0.000008546425,0.000002406833,0.0005555567,0.00002044583,0.0003963881],"genre_scores_gemma":[0.9986328,0.0001098792,0.0001597239,0.000006878431,0.000006879769,0.000003845887,0.0009479499,0.000008659899,0.0001232055],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02353526,"threshold_uncertainty_score":0.0467965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01044187100838153,"score_gpt":0.2219331313923074,"score_spread":0.2114912603839259,"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."}}