{"id":"W2903847803","doi":"10.12943/cnr.2017.00019","title":"CANDU FIRE DATABASE","year":2018,"lang":"en","type":"article","venue":"CNL Nuclear Review","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Nuclear power; Python (programming language); Database; Environmental science; Weighting; Engineering; Operations research; Computer science; Nuclear physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.001725605,0.000858586,0.001107495,0.00612647,0.001006895,0.002222258,0.003329742,0.001345257,0.04325671],"category_scores_gemma":[0.009986322,0.0003723105,0.001108703,0.006369323,0.0001969856,0.001834962,0.001047409,0.001161195,0.02802276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00184408,"about_ca_system_score_gemma":0.00406841,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03914826,"about_ca_topic_score_gemma":0.0462321,"domain_scores_codex":[0.9987904,0.0001314609,0.0002222832,0.0002378656,0.0005390645,0.00007901013],"domain_scores_gemma":[0.9948368,0.001078271,0.0003593511,0.001133609,0.002282077,0.0003098902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002950078,0.0001068173,0.004013913,0.001048363,0.0001403136,0.0001742488,0.00006357228,0.003348928,0.0005478217,0.007584385,0.9255327,0.05714399],"study_design_scores_gemma":[0.000109823,0.00003032661,0.004231593,0.0002454415,0.00006902669,0.0002113714,0.00006643273,0.00780431,0.001483385,0.004491312,0.9811871,0.00006989673],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.004447923,0.001810675,0.01003517,0.0005295857,0.0002339274,0.0005673771,0.9399449,0.01137165,0.03105865],"genre_scores_gemma":[0.01134612,0.001039705,0.02012571,0.000234051,0.00005964065,0.000675036,0.9582371,0.0008595255,0.007422969],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9608517,"threshold_uncertainty_score":0.1447082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2519902503271921,"score_gpt":0.4607025319494093,"score_spread":0.2087122816222172,"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."}}