{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00004472999,0.0002005637,0.0002131097,0.0001029262,0.00001514609,0.00004797559,0.0001158152,0.0003004936,0.01864724],"category_scores_gemma":[0.00001500759,0.0001807672,0.0000500659,0.00008899051,0.00005304606,0.00001964926,0.00001678002,0.000256905,0.00131898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007661876,"about_ca_system_score_gemma":0.000006417283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000533435,"about_ca_topic_score_gemma":0.00005010784,"domain_scores_codex":[0.9993835,0.000003388438,0.0001400254,0.0001373657,0.00009977211,0.0002359395],"domain_scores_gemma":[0.9996664,0.00001225411,0.00002986307,0.0001949646,0.000008340458,0.00008820792],"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":[6.43175e-7,0.0000492149,0.000001271032,0.001064465,0.00011544,0.00001057957,0.00006228997,0.00008802579,0.0001130067,0.02969983,0.5908834,0.3779119],"study_design_scores_gemma":[0.0001048654,0.000004841635,3.373488e-8,0.00008071061,0.00001075641,0.000004398409,0.00001349891,0.006397488,0.00005665168,0.00006430512,0.9930319,0.0002305089],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[2.562129e-7,0.0001507481,0.1468925,0.0000155227,0.0003805749,0.0003208174,0.000004536898,0.002985104,0.8492499],"genre_scores_gemma":[0.0001918403,0.00005242274,0.2668567,0.00003521996,0.0002384806,0.00005800483,0.00008085219,0.000652901,0.7318336],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.4021486,"threshold_uncertainty_score":0.9994586,"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."}}