{"id":"W1545660827","doi":"10.1002/9783527630844.app2","title":"Appendix B: Chance Constrained Programming","year":2010,"lang":"en","type":"other","venue":"","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Library science; Citation; Operations research; Computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009637304,0.001269838,0.0008159315,0.001180111,0.0006645119,0.001532674,0.001331297,0.001485797,0.2562952],"category_scores_gemma":[0.008334935,0.0004658039,0.0007340267,0.002186164,0.0004945952,0.00153114,0.00141351,0.002250372,0.04610785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001929973,"about_ca_system_score_gemma":0.003339785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01104389,"about_ca_topic_score_gemma":0.009350655,"domain_scores_codex":[0.9991245,0.0002728094,0.0000498464,0.0001086846,0.0003761092,0.00006805058],"domain_scores_gemma":[0.9965447,0.001551141,0.0001333666,0.0002588387,0.001421373,0.00009048149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003245162,0.00008578008,0.00042668,0.0004530938,0.00002509612,0.0002296965,0.0000604807,0.0441925,0.000326112,0.3178985,0.5598627,0.076407],"study_design_scores_gemma":[0.00003858128,0.00002280319,0.0006421867,0.0004326975,0.00001436304,0.0002746673,0.00006220047,0.06886256,0.0004803669,0.2963841,0.6327412,0.00004430247],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.001825511,0.005429557,0.5111939,0.009331366,0.003542696,0.0006452209,0.0348196,0.001312789,0.4318994],"genre_scores_gemma":[0.09435076,0.01415929,0.4914112,0.005202234,0.002839963,0.002980104,0.03868403,0.001824014,0.3485485],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.2562952,"threshold_uncertainty_score":0.8573928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008141916425967996,"score_gpt":0.2237972979717035,"score_spread":0.2156553815457355,"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."}}