{"id":"W4379144652","doi":"10.1007/978-3-031-30085-1_7","title":"Performance-Weighted Aggregation: Ferreting Out Wisdom Within the Crowd","year":2023,"lang":"en","type":"book-chapter","venue":"International series in management science/operations research/International series in operations research & management science","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Weighting; Computer science; Crowds; Probabilistic logic; Exploit; Consistency (knowledge bases); Artificial intelligence; Machine learning; Data mining; Computer security","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":[0.005710543,0.0008744369,0.001324951,0.001363363,0.002566548,0.008313156,0.00151509,0.002367951,0.01006968],"category_scores_gemma":[0.02296973,0.0005212757,0.0007701108,0.0025321,0.004172233,0.01214933,0.005835838,0.003369943,0.002876295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001634566,"about_ca_system_score_gemma":0.002091547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001517051,"about_ca_topic_score_gemma":0.001857123,"domain_scores_codex":[0.9972362,0.001204992,0.00009911698,0.0004806123,0.0007031563,0.000275963],"domain_scores_gemma":[0.9892749,0.0063838,0.0006550275,0.001812736,0.001107774,0.0007658394],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002029915,0.0001742278,0.002813023,0.0001978655,0.0001495182,0.0001957738,0.004226549,0.01791703,0.001627718,0.6218081,0.07499542,0.2756917],"study_design_scores_gemma":[0.00001827696,0.00003807811,0.0006786947,0.00006973105,0.00003171627,0.00005165374,0.001098332,0.03154088,0.0004832555,0.9400269,0.0259326,0.00002986762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09563021,0.01069999,0.4794992,0.05279907,0.00392558,0.0001739381,0.0003775095,0.001095716,0.3557988],"genre_scores_gemma":[0.8591003,0.003024573,0.08087791,0.003990992,0.002662319,0.0001627286,0.0002563613,0.0005153476,0.04940944],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01006968,"threshold_uncertainty_score":0.0336864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2296093935878131,"score_gpt":0.4808482212297488,"score_spread":0.2512388276419357,"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."}}