{"id":"W4396826004","doi":"10.1016/j.jmir.2024.03.017","title":"Creating a Tool to Maximize Scheduling Efficiency on a Magnetic Resonance Linear Accelerator Unit","year":2024,"lang":"en","type":"article","venue":"Journal of medical imaging and radiation sciences","topic":"Superconducting Materials and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Health Sciences Centre; Sunnybrook Health Science Centre","funders":"","keywords":"Unit (ring theory); Scheduling (production processes); Computer science; Nuclear magnetic resonance; Engineering; Physics; Operations management; Mathematics","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":[0.001357482,0.001187644,0.0004612144,0.001066392,0.0008409194,0.001772102,0.002124187,0.0008527425,0.005193067],"category_scores_gemma":[0.004732094,0.000692383,0.0005006839,0.0008792639,0.0003991651,0.00136091,0.001123201,0.0009579713,0.001379939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008097335,"about_ca_system_score_gemma":0.002205807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00268893,"about_ca_topic_score_gemma":0.004674631,"domain_scores_codex":[0.999073,0.0002953838,0.00006573083,0.0001421871,0.0002529331,0.0001706623],"domain_scores_gemma":[0.9976377,0.0009679921,0.0002641814,0.000331071,0.0004806169,0.0003185181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001906435,0.0009359235,0.01238842,0.0004421923,0.0002410346,0.0007711648,0.0006566475,0.2350304,0.1281863,0.01249368,0.02549113,0.5814567],"study_design_scores_gemma":[0.0001928491,0.0004949439,0.001543919,0.00003106131,0.0001263318,0.0002094475,0.0001820411,0.909689,0.06280199,0.004798594,0.01985853,0.0000712714],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1153154,0.0002961714,0.8368507,0.0004648649,0.0001806792,0.0003065086,0.000175221,0.035807,0.01060348],"genre_scores_gemma":[0.4244432,0.0001078114,0.5692281,0.0001735509,0.00004219949,0.0001497392,0.0002306605,0.002364871,0.003259904],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005193067,"threshold_uncertainty_score":0.01737249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02469999882379979,"score_gpt":0.3096879940225751,"score_spread":0.2849879951987753,"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."}}