{"id":"W4254101099","doi":"10.22215/etd/2009-09363","title":"Juror judgments across description inconsistencies and multiple identifications","year":2009,"lang":"en","type":"dissertation","venue":"","topic":"Jury Decision Making Processes","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Computer science; Information retrieval; Psychology; Humanities; Philosophy","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":[0.09096266,0.000485009,0.001689442,0.006071988,0.003028114,0.007615561,0.002133457,0.002718493,0.01262217],"category_scores_gemma":[0.5353405,0.0007817973,0.001497107,0.003315724,0.002488671,0.007957101,0.007050383,0.003712886,0.001254636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003843704,"about_ca_system_score_gemma":0.005541998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005363151,"about_ca_topic_score_gemma":0.005406821,"domain_scores_codex":[0.9083014,0.04579046,0.01013161,0.007917808,0.02476573,0.00309309],"domain_scores_gemma":[0.4676756,0.4186275,0.06334104,0.01777538,0.02814941,0.004431063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01688681,0.001393944,0.4509877,0.00191697,0.002094687,0.001731102,0.03053768,0.01394996,0.005161952,0.04238551,0.02323788,0.4097158],"study_design_scores_gemma":[0.001366933,0.003505955,0.6833202,0.003033204,0.002527087,0.001896592,0.05285766,0.06459425,0.01462135,0.1488781,0.02259075,0.0008078202],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.931006,0.0022943,0.02239031,0.006262715,0.0005337592,0.0007398541,0.001129987,0.000153034,0.03549007],"genre_scores_gemma":[0.9895287,0.0003944244,0.006426814,0.0004231116,0.0001384356,0.0002244749,0.0007212164,0.00003894707,0.002103723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09096266,"threshold_uncertainty_score":0.4810622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06900778351564711,"score_gpt":0.3992436453903021,"score_spread":0.330235861874655,"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."}}