{"id":"W2601055531","doi":"","title":"Crimes et contrebande aux douanes canadiennes : première partie","year":2016,"lang":"fr","type":"article","venue":"Érudit (Université de Montréal)","topic":"European Criminal Justice and Data Protection","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Political science","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0004806594,0.0002488075,0.0002515888,0.0001060958,0.001364124,0.00005737936,0.0003932228,0.0001959241,0.0008358085],"category_scores_gemma":[0.0002156936,0.0002210541,0.0001395242,0.0002779435,0.0005994026,0.0008238189,0.0002070677,0.0001658348,0.0007444915],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004104712,"about_ca_system_score_gemma":0.0002728485,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2546467,"about_ca_topic_score_gemma":0.8406134,"domain_scores_codex":[0.9978676,0.000459423,0.0001906938,0.0004411645,0.0002970083,0.0007441307],"domain_scores_gemma":[0.9985967,0.0003062802,0.0001753423,0.0003322171,0.0001609421,0.000428518],"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":[0.0003275348,0.0001211143,0.0006280955,0.0001172061,0.0001662107,0.002525605,0.01092539,0.00002162848,0.00245605,0.4497736,0.2286659,0.3042717],"study_design_scores_gemma":[0.0006907458,0.0001641138,0.000462196,0.0002167486,0.0003702494,0.0001352585,0.01257319,0.00003176028,0.0004194573,0.001576775,0.9830242,0.0003353109],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4938785,0.08023085,0.002417435,0.3655753,0.002486414,0.0006391288,0.0005200153,0.0003200698,0.05393223],"genre_scores_gemma":[0.5809331,0.03004849,0.0002328245,0.00071326,0.0006561227,0.000006531878,0.00001741089,0.00004153005,0.3873507],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7543584,"threshold_uncertainty_score":0.999936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01957142863097668,"score_gpt":0.2161704217156921,"score_spread":0.1965989930847155,"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."}}