{"id":"W3204315204","doi":"10.2139/ssrn.3859034","title":"Data-Driven Platelet Inventory Management Under Uncertainty in the Remaining Shelf-Life of Units","year":2021,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canada Research Chairs; University of Toronto; University of Waterloo","funders":"","keywords":"Shelf life; Inventory management; Off the shelf; Operations management; Statistics; Computer science; Business; Reliability engineering; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0052922,0.00010396,0.0001962842,0.0001194938,0.000630325,0.00001935261,0.0004221311,0.0001114326,0.0000924601],"category_scores_gemma":[0.0003249268,0.00007802126,0.00002753438,0.0006441366,0.00002697399,0.0001712545,0.0001243878,0.002394449,0.00001344424],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006630001,"about_ca_system_score_gemma":0.008428825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000216198,"about_ca_topic_score_gemma":0.01185235,"domain_scores_codex":[0.9962013,0.00140736,0.0006707067,0.000206226,0.0003148169,0.001199615],"domain_scores_gemma":[0.9987205,0.000202021,0.0002357494,0.0004678937,0.0003009844,0.00007289],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00008932232,0.0002079361,0.01453765,0.0001883464,0.0003155453,0.0000507834,0.008005342,0.2084851,0.00003177543,0.7583236,0.002170629,0.007593965],"study_design_scores_gemma":[0.008287443,0.0005254972,0.01597836,0.001788975,0.0002258076,0.0003173518,0.6329803,0.2060027,0.000006109336,0.09133814,0.04168929,0.0008599621],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9358783,0.003030072,0.03132691,0.0221372,0.0007269779,0.0008778459,0.00004087402,0.00003668424,0.005945117],"genre_scores_gemma":[0.9900693,0.006061877,0.0008726792,0.001822658,0.0002080922,0.00001640443,0.0002028547,0.00001832884,0.0007277911],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6669855,"threshold_uncertainty_score":0.9999071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1189794174905065,"score_gpt":0.4037892580932315,"score_spread":0.284809840602725,"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."}}