{"id":"W4285019381","doi":"10.22215/etd/2022-15127","title":"Prediction of Bridge Fires Characteristics Using Machine Learning","year":2022,"lang":"en","type":"dissertation","venue":"","topic":"Fire effects on concrete materials","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Bridge (graph theory); Vulnerability (computing); Transport engineering; Firefighting; Engineering; Forensic engineering; Artificial neural network; Civil engineering; Environmental science; Computer science; Geography; Cartography; Artificial intelligence; Computer security","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000173676,0.0002274707,0.0004053942,0.0001538279,0.00006355315,0.00003003119,0.0001170906,0.0001560534,0.002144324],"category_scores_gemma":[0.00008306188,0.0002583691,0.00007482035,0.0001071898,0.000007374564,0.00006618651,0.00002154956,0.0003058887,0.000007523126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009922637,"about_ca_system_score_gemma":0.00002548212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003391933,"about_ca_topic_score_gemma":0.00001801411,"domain_scores_codex":[0.99898,0.00006084275,0.0004181118,0.0001612778,0.0002220006,0.0001577269],"domain_scores_gemma":[0.9995541,0.00006504665,0.000153409,0.0001577659,0.00003904969,0.00003061151],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001318433,0.00001895141,0.002823915,0.005619307,0.0003715753,0.00002510527,0.001322216,0.008832389,0.9709918,0.00007683642,0.0006726219,0.009113445],"study_design_scores_gemma":[0.0005247899,0.0002393634,0.06196525,0.0004904589,0.000354261,0.0000176567,0.0001573912,0.7766817,0.1466904,0.00001436424,0.01204867,0.0008157672],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928653,0.0003229181,0.0001897825,8.696753e-7,0.003183301,0.0002260144,0.0004102217,0.0004389667,0.002362578],"genre_scores_gemma":[0.9915652,0.00012819,0.0002722608,0.000003341964,0.0001876247,0.00002344054,0.006039079,0.0001285955,0.001652287],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8243014,"threshold_uncertainty_score":0.9999868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01684174047188645,"score_gpt":0.2325245936640344,"score_spread":0.215682853192148,"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."}}