{"id":"W7026499825","doi":"","title":"Advanced Stochastic Programming and Machine Learning
\\nModels for Healthcare Planning, Scheduling, and
\\nPrediction Problems","year":2024,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Material Selection and Properties","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Scheduling (production processes); Stochastic programming; Stochastic modelling; Stochastic optimization; Linear programming; Dynamic programming; Service (business); Health care; Job shop scheduling","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0007350334,0.0003502313,0.0004342944,0.0007529277,0.001402407,0.0005889076,0.0002644632,0.0003631527,0.00003298074],"category_scores_gemma":[0.0001028769,0.0003527059,0.00008658366,0.0004350568,0.0002198834,0.0003608383,0.0001397474,0.0008647721,0.000009814665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003146379,"about_ca_system_score_gemma":0.0004382917,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01325504,"about_ca_topic_score_gemma":0.01426155,"domain_scores_codex":[0.9970706,0.0003797055,0.0003304042,0.0009794667,0.0005090002,0.0007308301],"domain_scores_gemma":[0.9988065,0.0001275633,0.0002022621,0.0002191652,0.0003266096,0.0003178651],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.01926088,0.0004275392,0.01373933,0.03031351,0.0007234117,0.0005997769,0.03372503,0.01120481,0.8551463,0.007638135,0.000884618,0.02633668],"study_design_scores_gemma":[0.01199247,0.02815701,0.01433148,0.01637221,0.001448372,0.0006982438,0.0612743,0.1156795,0.3658723,0.01115947,0.3654197,0.007594943],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895378,0.003824748,0.000587481,0.0002711686,0.001752427,0.001616246,0.00003571462,0.000407886,0.00196648],"genre_scores_gemma":[0.96722,0.0003271544,0.000100502,0.000004902514,0.0003532426,0.00006142759,0.000201656,0.00007687901,0.03165425],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.489274,"threshold_uncertainty_score":0.9998977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03793973722100782,"score_gpt":0.2972362077039553,"score_spread":0.2592964704829475,"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."}}