{"id":"W3121801449","doi":"10.7892/boris.145660","title":"The Optimal Amount of Falsified Testimony","year":2020,"lang":"en","type":"preprint","venue":"Cahiers de recherche","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; Ask price; Law and economics; Computer science; Political science; Economics; Computer security; Law","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":[0.01543571,0.001063528,0.002167379,0.002412269,0.001209366,0.004836194,0.002545037,0.005122033,0.01457436],"category_scores_gemma":[0.1093051,0.001160448,0.0005188311,0.0009542284,0.003804926,0.008106392,0.003292635,0.0027573,0.002188877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002102543,"about_ca_system_score_gemma":0.002899042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006362888,"about_ca_topic_score_gemma":0.0005548923,"domain_scores_codex":[0.9874086,0.006673559,0.0007945574,0.001855648,0.002114668,0.001153035],"domain_scores_gemma":[0.8976366,0.08141595,0.00479516,0.008013386,0.00474134,0.00339755],"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":[0.003683526,0.0005633137,0.00340935,0.001271915,0.0002437579,0.0007281399,0.00053992,0.1073236,0.01084321,0.6798261,0.009012593,0.1825546],"study_design_scores_gemma":[0.0004341338,0.0005258362,0.002987831,0.0008946114,0.0001539881,0.0006608931,0.0005712165,0.1393296,0.008420081,0.8374683,0.008460274,0.00009315443],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3088773,0.005986773,0.4775319,0.03150295,0.001057353,0.0009660778,0.002132251,0.001114288,0.1708312],"genre_scores_gemma":[0.9334596,0.001376989,0.05680571,0.0007782105,0.0004347915,0.0002566139,0.000195348,0.0001146855,0.006578023],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01543571,"threshold_uncertainty_score":0.08163279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2170361790164869,"score_gpt":0.3109306664797946,"score_spread":0.09389448746330761,"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."}}