{"id":"W3146243673","doi":"","title":"The optimal amount of distorted testimony when the arbiter can and cannot commit","year":2013,"lang":"en","type":"article","venue":"Chapters","topic":"Law, Economics, and Judicial Systems","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Arbiter; Commit; Adjudication; Computer science; Law and economics; Political science; Economics; Computer security; Law; Computer network","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":[0.01374096,0.0008889828,0.001707316,0.001825846,0.001768741,0.00544406,0.002331047,0.004656182,0.01342858],"category_scores_gemma":[0.1095422,0.001012576,0.0004839092,0.000926515,0.004783728,0.008911097,0.004379352,0.003589776,0.001951783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003529907,"about_ca_system_score_gemma":0.004447782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001556678,"about_ca_topic_score_gemma":0.001659864,"domain_scores_codex":[0.9870516,0.006080885,0.0009034904,0.001680397,0.002583967,0.001699749],"domain_scores_gemma":[0.9354468,0.04814491,0.004121871,0.006051171,0.003475811,0.002759489],"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":[0.002787439,0.000387416,0.003413081,0.0005690457,0.0001887624,0.001041655,0.0007702617,0.0565215,0.006391495,0.7913641,0.008487688,0.1280775],"study_design_scores_gemma":[0.0005928627,0.0004982858,0.003629635,0.0006333825,0.0001861569,0.0009292237,0.001544262,0.07290407,0.006378089,0.9023038,0.01026339,0.0001369032],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3442052,0.005054446,0.3222536,0.05039108,0.00108534,0.001066133,0.001242538,0.0006540344,0.2740476],"genre_scores_gemma":[0.9610081,0.0007637606,0.03062702,0.000908614,0.0002664097,0.0002049341,0.0001014328,0.00004954568,0.006070268],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01374096,"threshold_uncertainty_score":0.07266998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02226277518714949,"score_gpt":0.1828694145921238,"score_spread":0.1606066394049743,"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."}}