{"id":"W2085413799","doi":"10.1071/wf13042","title":"Use of night vision goggles for aerial forest fire detection","year":2014,"lang":"en","type":"article","venue":"International Journal of Wildland Fire","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Forest Research Institute; National Research Council Canada; Ministry of Natural Resources and Forestry; Natural Resources Canada; York University","funders":"","keywords":"Context (archaeology); Environmental science; Night vision; Terrain; Daytime; Computer science; Remote sensing; Geography; Artificial intelligence; Cartography; Atmospheric sciences; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006846912,0.000474707,0.0003875075,0.0004486565,0.0002336372,0.0004241083,0.0005435385,0.0003758508,0.001982393],"category_scores_gemma":[0.002190557,0.0001825944,0.0002643666,0.0001683981,0.0002939053,0.0004181337,0.0005294156,0.0003757084,0.0004069663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003320701,"about_ca_system_score_gemma":0.0003827333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002705198,"about_ca_topic_score_gemma":0.009662133,"domain_scores_codex":[0.99955,0.0001947536,0.00001895932,0.00009642775,0.0000835383,0.00005623647],"domain_scores_gemma":[0.9983959,0.0007653272,0.0002672694,0.0001946799,0.000153056,0.0002236877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.01388758,0.004288129,0.1603316,0.001365144,0.0003368015,0.0006778797,0.001642694,0.003589367,0.4388786,0.0004604443,0.003541637,0.3710001],"study_design_scores_gemma":[0.0003094644,0.02209419,0.8351951,0.0001935248,0.0002282679,0.00137483,0.001099201,0.01339063,0.116439,0.0006427224,0.008798085,0.0002350586],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9908614,0.0003099214,0.006476885,0.00004141087,0.00002119917,0.0001301613,0.0002124429,0.0002798748,0.00166668],"genre_scores_gemma":[0.9822559,0.0001522674,0.01574991,0.00009828647,0.000005633752,0.0001285604,0.0002751963,0.00003847611,0.001295711],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002705198,"threshold_uncertainty_score":0.006631732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01131556759745397,"score_gpt":0.2287592262978817,"score_spread":0.2174436587004278,"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."}}