{"id":"W2083251578","doi":"10.1109/crv.2010.29","title":"Flame Region Detection Based on Histogram Backprojection","year":2010,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Histogram; Fire detection; Artificial intelligence; Computer vision; Computer science; Detector; Projection (relational algebra); Smoke; False alarm; Pattern recognition (psychology); Algorithm; Engineering; Image (mathematics)","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":[0.0004954681,0.0005696535,0.0005100654,0.001406028,0.0002662585,0.00066681,0.0007212778,0.0004768283,0.002567917],"category_scores_gemma":[0.001423034,0.0002948016,0.0003280009,0.0007269214,0.0004473962,0.0009352441,0.0006019455,0.0005576222,0.0009092366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002109931,"about_ca_system_score_gemma":0.0004789145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00186458,"about_ca_topic_score_gemma":0.001987841,"domain_scores_codex":[0.9995802,0.00006549925,0.00001485535,0.00006196644,0.0002413977,0.00003606922],"domain_scores_gemma":[0.9994405,0.0002216674,0.00004668704,0.00005749975,0.0001992509,0.00003440091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000319133,0.0001235746,0.00170506,0.0001729772,0.0000489593,0.000168143,0.0001013456,0.0148383,0.2502961,0.003697241,0.00147954,0.7270496],"study_design_scores_gemma":[0.00005820254,0.0003160631,0.007536381,0.00002957707,0.00004298861,0.001091226,0.00006991702,0.583928,0.3990127,0.002795775,0.005030904,0.0000881077],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03151686,0.0002026501,0.9650025,0.00005961792,0.00005172512,0.00006621935,0.00005539984,0.001607334,0.001437564],"genre_scores_gemma":[0.1892207,0.0004238413,0.8073648,0.00004128671,0.00004426964,0.00007593166,0.0001316716,0.0001116834,0.002585912],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002567917,"threshold_uncertainty_score":0.008590519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005867017714848875,"score_gpt":0.1770134905702745,"score_spread":0.1711464728554257,"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."}}