{"id":"W6949595606","doi":"10.5281/zenodo.3271524","title":"Summary Legal and Technical Report on Spent Convictions","year":2019,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Work (physics); Deliverable; Directive; Technical report; Legislation; Scheme (mathematics); Plain language; Compliance (psychology)","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":[0.01140649,0.001137654,0.0008038806,0.006305634,0.002432488,0.009562415,0.003226193,0.002146929,0.1401065],"category_scores_gemma":[0.03112198,0.001062938,0.001805098,0.005204394,0.0009956235,0.006060491,0.007380612,0.002945252,0.07547186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008556199,"about_ca_system_score_gemma":0.01167181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04466671,"about_ca_topic_score_gemma":0.01775804,"domain_scores_codex":[0.9791003,0.002863284,0.001142987,0.00188477,0.0136196,0.001389045],"domain_scores_gemma":[0.9820527,0.003029373,0.0009676017,0.004235812,0.008650704,0.001063801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003139279,0.0001561179,0.003151484,0.00102844,0.00004371861,0.0004096232,0.001657818,0.01011385,0.001751199,0.129783,0.6075172,0.2440737],"study_design_scores_gemma":[0.00001480305,0.00004104439,0.0015603,0.0003542858,0.00001392027,0.0001133858,0.0004732482,0.00188597,0.001315015,0.00720368,0.9869787,0.00004569122],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.01903524,0.003446698,0.1317602,0.01332427,0.004575352,0.001969054,0.1144468,0.01972437,0.691718],"genre_scores_gemma":[0.1006496,0.005590987,0.06228505,0.002317628,0.000798953,0.002299229,0.2211098,0.01346347,0.5914853],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1401065,"threshold_uncertainty_score":0.4687029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05205488222781358,"score_gpt":0.3197310430511747,"score_spread":0.2676761608233611,"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."}}