{"id":"W3215510267","doi":"10.1002/acm2.13477","title":"Topic modeling of maintenance logs for linac failure modes and trends identification","year":2021,"lang":"en","type":"article","venue":"Journal of Applied Clinical Medical Physics","topic":"Quality and Safety in Healthcare","field":"Health Professions","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; Kelowna General Hospital; University of British Columbia","funders":"","keywords":"Computer science; Identification (biology); Ranking (information retrieval); Downtime; Linear particle accelerator; Failure mode and effects analysis; Data science; Reliability engineering; Artificial intelligence; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003438883,0.001096604,0.0006839393,0.00504095,0.0006077558,0.001297189,0.0009408977,0.0008717999,0.001352411],"category_scores_gemma":[0.00948686,0.0003389237,0.001788526,0.002344272,0.000341099,0.001570145,0.0007290012,0.001154204,0.0007692482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000952842,"about_ca_system_score_gemma":0.00106524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007667289,"about_ca_topic_score_gemma":0.008005724,"domain_scores_codex":[0.9980921,0.0006701023,0.0002074413,0.0006117195,0.0002929585,0.0001256963],"domain_scores_gemma":[0.9912701,0.006791799,0.000700263,0.0003563929,0.0007461686,0.0001354082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001790191,0.001227127,0.1472014,0.001798143,0.0006897945,0.001374914,0.006643211,0.1826767,0.02957942,0.0109808,0.01820195,0.5978363],"study_design_scores_gemma":[0.00003643008,0.00014784,0.02679081,0.00006003779,0.000132292,0.0003012073,0.0006462166,0.9600512,0.003058164,0.004503829,0.004221122,0.00005075534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.360707,0.001707726,0.6196635,0.0008629959,0.0001894924,0.0007644568,0.009930195,0.004033559,0.002140992],"genre_scores_gemma":[0.8287625,0.0005622957,0.1564579,0.00007720959,0.0001861439,0.0008738957,0.01139684,0.0001321043,0.001550974],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007667289,"threshold_uncertainty_score":0.01818681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2050213516941972,"score_gpt":0.5277297247776579,"score_spread":0.3227083730834607,"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."}}