{"id":"W4396840563","doi":"10.1017/9781009282284.003","title":"Generalizing from Models","year":2024,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science","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":[0.005003583,0.002345272,0.001229654,0.001907302,0.001045153,0.004630263,0.003234631,0.002256043,0.02284688],"category_scores_gemma":[0.01887578,0.001003954,0.00339865,0.002214044,0.003552761,0.006499775,0.00526417,0.005585294,0.007239413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00260329,"about_ca_system_score_gemma":0.002225915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005493517,"about_ca_topic_score_gemma":0.004596437,"domain_scores_codex":[0.9961644,0.001715918,0.0002417672,0.000755063,0.0009473651,0.0001754362],"domain_scores_gemma":[0.994091,0.003974974,0.0002165865,0.001218257,0.0003931488,0.0001059737],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001071572,0.00002264421,0.0002884101,0.000337674,0.0000844989,0.0001851893,0.0003886577,0.01965892,0.0002009997,0.915316,0.01649028,0.04701602],"study_design_scores_gemma":[0.000003948113,0.000005443503,0.00005511576,0.0001253441,0.00001138135,0.00006717442,0.00004463248,0.0122711,0.00005351942,0.9474072,0.03994711,0.000007954268],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001940129,0.004707807,0.8822671,0.00864571,0.0005593072,0.0001900002,0.001048676,0.0008416246,0.09979962],"genre_scores_gemma":[0.1695312,0.02639608,0.7166173,0.008965988,0.002849096,0.001701115,0.004086695,0.002015836,0.06783671],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02284688,"threshold_uncertainty_score":0.07643044,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03040959727699255,"score_gpt":0.1924999437213576,"score_spread":0.1620903464443651,"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."}}