{"id":"W2094480444","doi":"10.1007/s10111-002-0117-4","title":"Planning, scheduling and dispatching tasks in production control","year":2003,"lang":"en","type":"article","venue":"Cognition Technology & Work","topic":"Business Process Modeling and Analysis","field":"Business, Management and Accounting","cited_by":54,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Scheduling (production processes); Computer science; Automated planning and scheduling; Production planning; Dynamic priority scheduling; Speculation; Operations research; Artificial intelligence; Operations management; Production (economics); Engineering; Schedule; Business","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":[],"consensus_categories":[],"category_scores_codex":[0.0003037575,0.0001409766,0.0001966639,0.0007467197,0.0002087034,0.00009646629,0.0000788749,0.0001612096,0.00002671052],"category_scores_gemma":[0.0005078845,0.0001421226,0.00002719689,0.001462271,0.00009082288,0.0004535797,0.0000307046,0.0002745268,0.00004414806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001139259,"about_ca_system_score_gemma":0.000009513147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001192976,"about_ca_topic_score_gemma":0.000008861724,"domain_scores_codex":[0.9991017,0.000008409019,0.0002217112,0.0003450558,0.00009294479,0.0002301855],"domain_scores_gemma":[0.999571,0.00001594164,0.0001423297,0.0001277393,0.000136578,0.000006425029],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009726479,0.0002586219,0.8779674,0.0003078252,0.0001046512,0.00003989056,0.00009406169,0.01014894,0.001811712,0.01876793,0.000217168,0.09018458],"study_design_scores_gemma":[0.01203244,0.00005499805,0.06326091,0.006457506,0.001838848,0.0000922226,0.008277335,0.06754553,0.004457777,0.8135941,0.01848842,0.003899909],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9697254,0.001175218,0.02444435,0.002767877,0.000178142,0.000149823,3.602301e-7,0.0003110709,0.001247708],"genre_scores_gemma":[0.9986321,0.00002238312,0.0006247682,0.0004410575,0.0001603141,0.00005238043,0.00001020741,0.00001781295,0.00003891514],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8147064,"threshold_uncertainty_score":0.5795593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01224599461397289,"score_gpt":0.2232263976325169,"score_spread":0.2109804030185441,"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."}}