{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007650972,0.0006118515,0.0005595477,0.0011129,0.0004355533,0.0004555827,0.0007418551,0.0006159378,0.0007535969],"category_scores_gemma":[0.002238262,0.0005518656,0.0002741291,0.0007881795,0.0006608971,0.0008617582,0.0004047696,0.0003444047,0.000266214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005195267,"about_ca_system_score_gemma":0.000569464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004472301,"about_ca_topic_score_gemma":0.004364029,"domain_scores_codex":[0.9992002,0.0001154001,0.00002645703,0.0001691354,0.0004321983,0.00005668197],"domain_scores_gemma":[0.9993469,0.0002636003,0.0001615503,0.00003884376,0.0001615207,0.0000275328],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001152225,0.0001797298,0.008664322,0.000430709,0.00004397939,0.0003287988,0.0003751951,0.1825946,0.4236534,0.002826769,0.001114602,0.3786356],"study_design_scores_gemma":[0.00005888837,0.0003715267,0.01015657,0.0000152059,0.00002058491,0.0003105574,0.00004092876,0.8517104,0.1331531,0.002177986,0.001920467,0.0000638582],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2048538,0.0003657263,0.7903705,0.0000638172,0.00002015818,0.00006074283,0.0001024629,0.003099601,0.001063246],"genre_scores_gemma":[0.794631,0.0001649028,0.2034744,0.00001757859,0.00001263008,0.00004416818,0.0001134424,0.0001178497,0.001424039],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004472301,"threshold_uncertainty_score":0.008892596,"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."}}