{"id":"W4307581782","doi":"10.1177/15330338221123108","title":"Forecasting Institutional LINAC Utilization in Response to Varying Workload","year":2022,"lang":"en","type":"article","venue":"Technology in Cancer Research & Treatment","topic":"Radiation Dose and Imaging","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto","funders":"","keywords":"Linear particle accelerator; Linear regression; Computer science; Workload; Statistics; Mathematics; Machine learning; Physics; Optics","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":[0.0009712133,0.0005554582,0.0003022253,0.0007167315,0.0001587326,0.0005928978,0.0006838992,0.0005083237,0.001045539],"category_scores_gemma":[0.003410757,0.0002104695,0.000383357,0.0008727895,0.000138627,0.0006373998,0.0003472362,0.0005648825,0.0003671178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009285663,"about_ca_system_score_gemma":0.0006488755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02197217,"about_ca_topic_score_gemma":0.02103069,"domain_scores_codex":[0.9996965,0.00007011285,0.00002050284,0.0001011274,0.00005897888,0.00005282649],"domain_scores_gemma":[0.9988247,0.00046046,0.0002673569,0.0001177218,0.0002494018,0.0000803871],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000130561,0.00007659847,0.07911748,0.00003392346,0.00004133597,0.00008799823,0.00005034155,0.8954938,0.001877636,0.0002432037,0.001233762,0.02161339],"study_design_scores_gemma":[0.000003235478,0.00003536822,0.01611124,0.000004481123,0.000007643575,0.00001704471,0.00004091848,0.9821078,0.001129183,0.0001817509,0.0003531458,0.000008124052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9487196,0.0001739086,0.04636665,0.0002660422,0.0000372182,0.00004587239,0.002281238,0.0008134496,0.001296115],"genre_scores_gemma":[0.9905338,0.00007712687,0.007081983,0.00002102521,0.00001010868,0.00002679653,0.001642741,0.00002616895,0.0005800973],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02197217,"threshold_uncertainty_score":0.0436886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2259066982906033,"score_gpt":0.4639980075934114,"score_spread":0.2380913093028081,"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."}}