{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009712278,0.0001150195,0.0002700461,0.00005816855,0.0001296672,0.00004303303,0.0005593594,0.00006274635,0.0000302811],"category_scores_gemma":[0.0003927316,0.00008545481,0.00009139163,0.0007219721,0.0007345708,0.000764886,0.0001670987,0.0001965412,0.00002246168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004021064,"about_ca_system_score_gemma":0.0001222616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002135616,"about_ca_topic_score_gemma":0.00009990601,"domain_scores_codex":[0.9975909,0.00007722438,0.0005505432,0.0001902322,0.001226129,0.0003649886],"domain_scores_gemma":[0.9987302,0.00008307568,0.0004953576,0.0002178968,0.000101181,0.0003722669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004696771,0.00005689357,0.9056867,0.000008985234,0.000005453657,0.00002053866,0.0000894964,0.08827694,0.004265319,0.0001446587,0.0005921094,0.0008059293],"study_design_scores_gemma":[0.000414358,0.0005549051,0.7129784,0.00009738992,0.00002074737,0.0002462898,0.0000348703,0.2840392,0.0004040951,0.0006204812,0.0005073361,0.00008184946],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929061,0.000043416,0.00547572,0.0001080433,0.001081479,0.0001204916,0.00000249335,0.000009353989,0.0002529237],"genre_scores_gemma":[0.9954505,0.000003980205,0.004155187,0.00002892853,0.0003233654,7.491339e-7,1.756024e-7,0.000008808498,0.00002829881],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1957623,"threshold_uncertainty_score":0.3484746,"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."}}