{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003917596,0.00003419119,0.00005145096,0.00001414749,0.001958077,0.0003275316,0.0005078281,0.00004723742,0.002074077],"category_scores_gemma":[0.0006999581,0.00003098141,0.00003238954,0.00002780576,0.0006539524,0.0002617576,0.00005123488,0.0000498305,0.00106761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003560488,"about_ca_system_score_gemma":0.00008359501,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01461055,"about_ca_topic_score_gemma":0.04015249,"domain_scores_codex":[0.9994343,0.0000220739,0.00007867494,0.00009301348,0.0001878942,0.0001840414],"domain_scores_gemma":[0.9994323,0.00003940074,0.00005279491,0.0003276813,0.0000680876,0.00007973104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001291073,0.00001263487,0.01881197,4.076898e-7,0.000003749267,0.000003282561,0.001983307,0.000005773951,0.00002350906,0.9573483,0.004534631,0.01727107],"study_design_scores_gemma":[0.00002264347,0.00001240699,0.004486097,0.000006023224,0.000007941827,1.715923e-7,0.003636419,0.0005268495,0.001608061,0.05698621,0.9325557,0.0001514341],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.02504916,0.000005090802,0.001551262,0.006314836,0.0004394079,0.00005628329,3.421904e-7,0.00005877668,0.9665248],"genre_scores_gemma":[0.9558695,0.0000175568,0.000633095,0.0001841542,0.0002872392,9.749555e-7,1.161651e-7,0.000003000158,0.04300439],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9308203,"threshold_uncertainty_score":0.9997102,"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."}}