{"id":"W2777022249","doi":"10.1145/3086512.3086516","title":"Scenario analytics","year":2017,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thomson Reuters (Canada)","funders":"","keywords":"Jury; Warrant; Computer science; Analytics; Context (archaeology); Set (abstract data type); Legal case; Data science; Key (lock); Work (physics); Business; Computer security; Law; Political science; Engineering","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":[0.004003555,0.001988225,0.0008594233,0.008171503,0.001167952,0.00692182,0.003303173,0.001460319,0.06196246],"category_scores_gemma":[0.02313217,0.0007907508,0.002221842,0.006481837,0.0006401956,0.008092984,0.004482606,0.002092554,0.02100023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00167889,"about_ca_system_score_gemma":0.002486918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005086889,"about_ca_topic_score_gemma":0.005011342,"domain_scores_codex":[0.9959409,0.001011916,0.0005652085,0.0007647329,0.001487491,0.00022973],"domain_scores_gemma":[0.9889725,0.005165054,0.0006833046,0.002767732,0.001921259,0.0004902447],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005008286,0.0003882495,0.01041801,0.002344681,0.0003624859,0.0009930356,0.001900276,0.03406774,0.002654455,0.1814159,0.3670007,0.3979537],"study_design_scores_gemma":[0.00009020585,0.00008329417,0.002801681,0.0006849315,0.00008694948,0.0006324511,0.001900922,0.1062961,0.003770092,0.1686878,0.7148629,0.0001025129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01196702,0.001823595,0.5263627,0.004888402,0.0006320885,0.003783951,0.199514,0.06646226,0.184566],"genre_scores_gemma":[0.1298159,0.002472795,0.5717179,0.001105542,0.0002188895,0.002374307,0.2650049,0.003962648,0.02332718],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06196246,"threshold_uncertainty_score":0.207285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1855599255626866,"score_gpt":0.4584493448837019,"score_spread":0.2728894193210154,"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."}}