{"id":"W7014637515","doi":"","title":"Prévision à court terme du besoin électrique québécois","year":2023,"lang":"fr","type":"other","venue":"Archipelago (Université du Québec à Montréal)","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Limiting; Statistical analysis; Sampling error; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001487146,0.0007305277,0.0006470886,0.001079731,0.001092302,0.00241556,0.001125068,0.0008469183,0.002826846],"category_scores_gemma":[0.004160736,0.0003630787,0.0007927797,0.001491757,0.0005479032,0.001160886,0.0006899438,0.0008223699,0.0004139149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005684948,"about_ca_system_score_gemma":0.00436549,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8736905,"about_ca_topic_score_gemma":0.8809668,"domain_scores_codex":[0.9993857,0.00008020781,0.00002743118,0.000225475,0.0001772799,0.0001038924],"domain_scores_gemma":[0.9981342,0.0005836883,0.0001260384,0.0001617251,0.0008906093,0.0001037865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005725892,0.0001044567,0.5210987,0.0003275091,0.0007698214,0.000425229,0.001519148,0.3835221,0.00834818,0.002514837,0.006549963,0.07424753],"study_design_scores_gemma":[0.00002600192,0.0001029157,0.45989,0.0001651392,0.0001807574,0.0001030355,0.001660496,0.5171485,0.004959221,0.001509504,0.01411692,0.0001376104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.966936,0.0008144975,0.01860607,0.00061667,0.0000620532,0.00005952041,0.005366103,0.0003867607,0.007152334],"genre_scores_gemma":[0.9859589,0.0002458979,0.005081928,0.00007240376,0.000008625076,0.00003078202,0.004443246,0.00006343611,0.004094802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1263095,"threshold_uncertainty_score":0.2541067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004501146344327939,"score_gpt":0.1558265182639188,"score_spread":0.1513253719195909,"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."}}