{"id":"W2784820820","doi":"","title":"TransCanada's Use of Pipeline Simulations to Support Short Notice Services","year":2007,"lang":"en","type":"article","venue":"PSIG Annual Meeting","topic":"Radiology practices and education","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"TransCanada (Canada)","funders":"","keywords":"Notice; Pipeline (software); Computer science; Business; Political science; Operating system","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":[],"consensus_categories":[],"category_scores_codex":[0.0006034065,0.00009219359,0.000190626,0.0001299278,0.00007419429,0.000009737294,0.00005927728,0.00007487249,0.00007615578],"category_scores_gemma":[0.0003559642,0.00008707363,0.00004236771,0.0002462836,0.00002095381,0.0002215814,0.00001142323,0.0001234102,0.00001053574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003291975,"about_ca_system_score_gemma":0.00006949176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001833551,"about_ca_topic_score_gemma":0.001994138,"domain_scores_codex":[0.9990029,0.00002874945,0.0003654606,0.0001783568,0.0001818802,0.0002426127],"domain_scores_gemma":[0.9988142,0.000478008,0.00007914898,0.0001763627,0.0002751736,0.0001770519],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001262571,0.0006284322,0.8104286,0.0005999521,0.0002106746,0.00007437682,0.02502819,0.01479631,0.06170842,0.0001418362,0.005666573,0.0794541],"study_design_scores_gemma":[0.001232694,0.001371041,0.6636934,0.0004938825,0.0009680908,0.0001901101,0.0172833,0.01051689,0.02337205,0.00002422768,0.2802497,0.0006045289],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935612,0.00003587082,0.001179393,0.001991214,0.0002674575,0.0002210609,0.00003076454,0.00003039218,0.002682627],"genre_scores_gemma":[0.9910513,0.000005352197,0.006732952,0.001288963,0.0003259838,0.000001865152,0.00005398247,0.00001479026,0.0005247965],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2745832,"threshold_uncertainty_score":0.355076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0429688568362267,"score_gpt":0.3484372368710336,"score_spread":0.3054683800348069,"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."}}