{"id":"W7006763481","doi":"","title":"Utilisation de l’apprentissage automatique pour approximer l’énergie d’échange-corrélation","year":2024,"lang":"fr","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; Compute Canada","keywords":"Derogation; Context (archaeology); ESPACE","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":["insufficient_payload"],"category_scores_codex":[0.001362874,0.0008108756,0.0006581487,0.0005888497,0.006372708,0.0005860025,0.001008786,0.0009246918,0.0012768],"category_scores_gemma":[0.0003770958,0.0009298651,0.0003782081,0.0007020759,0.0004836925,0.001037869,0.0003944495,0.0007647066,0.001464369],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008247011,"about_ca_system_score_gemma":0.003352838,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02980801,"about_ca_topic_score_gemma":0.003432187,"domain_scores_codex":[0.9946218,0.0007777257,0.0008570589,0.001440984,0.001298409,0.00100403],"domain_scores_gemma":[0.9973089,0.0002490131,0.0008603146,0.0006855392,0.0004137719,0.0004825001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003812976,0.0002332408,0.004092935,0.001410609,0.0001380776,0.002271276,0.06157638,0.04310784,0.8525891,0.03138595,0.001157775,0.001655479],"study_design_scores_gemma":[0.001389286,0.0002570457,0.06383717,0.00238079,0.001466484,0.004085612,0.03242834,0.5266234,0.3308147,0.005338159,0.02904856,0.002330388],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9543074,0.00796941,0.006272503,0.0009784119,0.007243078,0.0008288999,0.0001262928,0.000627046,0.02164694],"genre_scores_gemma":[0.8860083,0.000297076,0.01144411,0.0001099975,0.0007487768,0.0001223424,0.0008330716,0.0001063022,0.10033],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5217744,"threshold_uncertainty_score":0.9996362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00813232492386144,"score_gpt":0.2062674822358715,"score_spread":0.1981351573120101,"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."}}