{"id":"W2024392733","doi":"10.1115/ipc2004-0274","title":"Automatic, Non-Intrusive, Flame Detection in Pipelines","year":2004,"lang":"en","type":"article","venue":"2004 International Pipeline Conference, Volumes 1, 2, and 3","topic":"Water Systems and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Flammable liquid; Combustion; Noise (video); Filter (signal processing); Pipeline transport; Acoustics; Environmental science; Computer science; Engineering; Electrical engineering; Waste management; Physics; Artificial intelligence; Environmental engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000098891,0.0001838222,0.0001990393,0.0002597225,0.00004054903,0.0001136776,0.0001421999,0.0001087106,0.0001432088],"category_scores_gemma":[0.00003172002,0.0001802938,0.00004020509,0.0001440658,0.00003385509,0.0003036215,0.00002946937,0.0001387446,0.00006491478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001040342,"about_ca_system_score_gemma":0.00003495661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003125885,"about_ca_topic_score_gemma":0.002310617,"domain_scores_codex":[0.9990014,0.00001098781,0.0004058934,0.0002083869,0.0001804895,0.0001928747],"domain_scores_gemma":[0.9995924,0.00001153611,0.00005304173,0.0001152335,0.0001631531,0.00006468959],"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":[0.00009095595,0.0003360364,0.01351333,0.0005144458,0.0002253676,0.0001264105,0.00427396,0.7313176,0.008713382,0.004553272,0.01088205,0.2254532],"study_design_scores_gemma":[0.001253316,0.00002671,0.005875383,0.0001594661,0.000009358301,0.00003232771,0.0001383426,0.9845513,0.00227539,0.001213982,0.00420588,0.0002585628],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6289724,0.0003575338,0.354405,0.0005328421,0.002604299,0.0003812561,0.00003456467,0.0003629399,0.01234915],"genre_scores_gemma":[0.9966041,0.00018355,0.001216948,0.0000441832,0.0003940184,0.00002830761,0.00005022978,0.00002361476,0.001455085],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3676317,"threshold_uncertainty_score":0.7352169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006163129593171984,"score_gpt":0.2014975544930394,"score_spread":0.1953344248998674,"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."}}