{"id":"W6945456713","doi":"10.25318/1210009001-fra","title":"Importations et exportations de marchandises, base douanière, pour tous les pays, selon les Grandes catégories économiques et les soixante principaux partenaires commerciaux","year":2019,"lang":"fr","type":"dataset","venue":"Statistics Canada Dissemination","topic":"Subterranean biodiversity and taxonomy","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Food consumption; Base (topology); Context (archaeology)","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":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006400865,0.0006433774,0.0005993452,0.0002798457,0.00150364,0.0003722052,0.0005125193,0.0003685697,0.003145604],"category_scores_gemma":[0.0006107666,0.0006843754,0.0001035951,0.0002356339,0.0003769049,0.0004361941,0.00005678226,0.0006959387,0.00005319746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002256176,"about_ca_system_score_gemma":0.002639217,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7586991,"about_ca_topic_score_gemma":0.9922791,"domain_scores_codex":[0.9966804,0.0005855608,0.0007928475,0.0006866649,0.0005491652,0.0007053026],"domain_scores_gemma":[0.9951491,0.002882475,0.0007475522,0.0004512765,0.0003610849,0.0004084478],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.000077747,0.0001063895,0.1228084,0.000530311,0.000166806,0.0001951139,0.0009059116,0.001028067,0.000003479077,0.002898829,0.8526879,0.01859108],"study_design_scores_gemma":[0.0004519374,0.0001829702,0.5513045,0.0003778602,0.0004646294,0.00004213388,0.007387802,0.001687443,0.00004688711,0.0005622839,0.4365695,0.0009220911],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.03248277,0.00124193,0.005844352,0.002604002,0.0007175882,0.0007084337,0.9560699,0.00004139123,0.0002895959],"genre_scores_gemma":[0.1662652,0.002830487,0.005040788,0.0003813018,0.0001097611,0.00003411828,0.8242098,0.00002137605,0.001107199],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4284961,"threshold_uncertainty_score":0.9997963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05740984544571404,"score_gpt":0.2623813318306465,"score_spread":0.2049714863849325,"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."}}