{"id":"W7071754527","doi":"","title":"Sursis, récidive et réinsertion sociale : un équilibre précaire","year":2009,"lang":"fr","type":"article","venue":"Project Muse (Johns Hopkins University)","topic":"QR Code Applications and Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Punitive damages; Sentence; Odds; Recidivism; Rehabilitation; Conditional probability","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002724783,0.0003313427,0.0004132974,0.0007702719,0.001214789,0.002356142,0.0008429459,0.0007758451,0.005657279],"category_scores_gemma":[0.01474554,0.000201719,0.0003978509,0.0005021392,0.001692622,0.001210807,0.001831574,0.0009938608,0.0003235158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004428944,"about_ca_system_score_gemma":0.003207055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05242676,"about_ca_topic_score_gemma":0.06760974,"domain_scores_codex":[0.9968197,0.001347993,0.0001565654,0.0004221506,0.0007050947,0.0005483737],"domain_scores_gemma":[0.9938813,0.002150597,0.001882801,0.0004108845,0.0008540459,0.0008203752],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001509268,0.0007603166,0.8344718,0.0002969166,0.0002780058,0.0008669311,0.007879588,0.00268652,0.005051812,0.01346146,0.002237035,0.1305003],"study_design_scores_gemma":[0.00001984288,0.0006376796,0.9854575,0.00008569749,0.00006202346,0.0003820707,0.004145228,0.00208058,0.0009787134,0.002755655,0.003357265,0.00003782436],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9862608,0.0007650696,0.002141828,0.001621528,0.00003332475,0.00007460122,0.0002726724,0.00003527238,0.008794982],"genre_scores_gemma":[0.9982588,0.00009417393,0.0005622946,0.00006815268,0.00001228035,0.00004056377,0.0000703478,0.000006144714,0.0008872855],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05242676,"threshold_uncertainty_score":0.1042432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02424299461871821,"score_gpt":0.2343735970331016,"score_spread":0.2101306024143834,"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."}}