{"id":"W7020295918","doi":"","title":"La désinformation climatique en contexte québécois : état des lieux et pistes de réflexion","year":2023,"lang":"fr","type":"other","venue":"Knowledge UdeS (Institutional Deposit of the University of Sherbrooke)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Population; Context (archaeology); Government (linguistics); Limiting","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":[0.003307894,0.000479189,0.0004559235,0.002776738,0.01615212,0.01157319,0.001075288,0.0017056,0.01384926],"category_scores_gemma":[0.006392093,0.0002644024,0.0002590389,0.004672089,0.01373034,0.005390813,0.003705464,0.003420912,0.000556209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.06742851,"about_ca_system_score_gemma":0.055425,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9649242,"about_ca_topic_score_gemma":0.9792709,"domain_scores_codex":[0.997434,0.001190672,0.00005333504,0.000283395,0.0005680635,0.0004705215],"domain_scores_gemma":[0.9928524,0.002715286,0.0005476113,0.0002500137,0.002455935,0.001178769],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00007052007,0.00003868297,0.03699726,0.000659647,0.00006915761,0.0009784047,0.7051041,0.0003781981,0.0008498501,0.1650775,0.04256169,0.04721501],"study_design_scores_gemma":[0.000008728476,0.0000245126,0.06084638,0.001198362,0.00005246704,0.0001436089,0.6123044,0.0004294298,0.0003723734,0.008963645,0.3155584,0.00009775044],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4497231,0.0242794,0.00394498,0.1547413,0.001202962,0.0001289509,0.001598894,0.00009028478,0.3642901],"genre_scores_gemma":[0.9518998,0.006000196,0.000723247,0.004282719,0.000109431,0.0000582412,0.0001699207,0.00003681285,0.03671964],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06742851,"threshold_uncertainty_score":0.4892304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0124079891201674,"score_gpt":0.2305437685962953,"score_spread":0.2181357794761279,"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."}}