{"id":"W7084085371","doi":"10.64628/aap.76npnsxku","title":"Laïcité, vous dites? Séparez d’abord l’argent de l’État!","year":2019,"lang":"fr","type":"article","venue":"","topic":"Scientific Research and Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Set (abstract data type); Identification (biology); Relation (database); Feature (linguistics)","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001180435,0.0002488868,0.0002864934,0.0003128118,0.0001770487,0.0006061338,0.002393637,0.0002860461,0.007457351],"category_scores_gemma":[0.0002560038,0.0002287601,0.0001520232,0.001451846,0.0003588731,0.0006522922,0.001076006,0.0005220069,0.01769071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001752907,"about_ca_system_score_gemma":0.0005445445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005782421,"about_ca_topic_score_gemma":0.0001370113,"domain_scores_codex":[0.9960997,0.0001713959,0.0003460296,0.0009689152,0.0007318925,0.001682028],"domain_scores_gemma":[0.9973186,0.0002304686,0.00008821519,0.001614101,0.0002470639,0.0005015907],"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":[0.00001114908,0.0002840618,0.009379943,0.00006425449,0.00004656806,0.0002206185,0.0003278839,0.00005451478,0.002836813,0.6320269,0.1236302,0.2311172],"study_design_scores_gemma":[0.0005500229,0.0003874933,0.005323412,0.00004879244,0.000005526636,0.0001698636,0.000104857,0.07929063,0.01215332,0.0338458,0.8677513,0.000369004],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3762266,0.01263919,0.2492518,0.1067607,0.01774946,0.001328241,0.00004796414,0.001081408,0.2349147],"genre_scores_gemma":[0.5328122,0.0005698533,0.02599612,0.001643243,0.000268066,0.00003044723,0.000007503088,0.00002658526,0.438646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7441211,"threshold_uncertainty_score":0.99345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03205184457078251,"score_gpt":0.291058311951254,"score_spread":0.2590064673804715,"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."}}