{"id":"W2269080556","doi":"","title":"PREDICTION OF FOREST FIRE USING WIRELESS SENSOR NETWORK","year":2015,"lang":"en","type":"article","venue":"JOURNAL OF TROPICAL FOREST SCIENCE","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Environmental science; Rating system; Fire prevention; Wildfire suppression; Fire detection; Meteorology; China; Firefighting; Forestry; Geography; Engineering; Cartography; Architectural engineering","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":[0.000330204,0.0006836415,0.0004390144,0.001099597,0.0001935109,0.0003524252,0.0003951442,0.0003179547,0.0004173454],"category_scores_gemma":[0.0009974207,0.0001881279,0.0003501659,0.0008000752,0.00008410737,0.0006140025,0.0002660506,0.0003217297,0.0001713128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003206395,"about_ca_system_score_gemma":0.0001912286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007922987,"about_ca_topic_score_gemma":0.006967216,"domain_scores_codex":[0.9998552,0.00002453598,0.00001618423,0.00004080182,0.00004441249,0.00001882434],"domain_scores_gemma":[0.9997242,0.0001139929,0.000055898,0.00001632112,0.00006918417,0.00002030036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004097606,0.0002275846,0.124705,0.0001422231,0.0001623536,0.0002216646,0.00003697611,0.7077947,0.007560058,0.0005837975,0.001802637,0.1563533],"study_design_scores_gemma":[0.000003198473,0.00002838611,0.007924111,0.000004101766,0.00001246159,0.00001758031,0.00001389932,0.9910223,0.0006640223,0.0001762715,0.0001290384,0.000004518681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8290627,0.001985724,0.1618437,0.0002980028,0.0002588253,0.0001124143,0.001677138,0.0007005787,0.004060987],"genre_scores_gemma":[0.9899204,0.0004698868,0.008637586,0.000009781442,0.00002030273,0.00002489859,0.0005116761,0.000004598626,0.0004009146],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007922987,"threshold_uncertainty_score":0.01575375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02569612027416556,"score_gpt":0.2428822158687486,"score_spread":0.217186095594583,"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."}}