{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001007539,0.0008709515,0.0008378353,0.000854033,0.0007329974,0.001812771,0.001494756,0.001197967,0.01098671],"category_scores_gemma":[0.0031212,0.0005425243,0.001243436,0.0009323104,0.0006287043,0.001510375,0.000882376,0.001696202,0.005042657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008993976,"about_ca_system_score_gemma":0.001683579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01197185,"about_ca_topic_score_gemma":0.01117283,"domain_scores_codex":[0.9993569,0.0001633261,0.00003966623,0.00009614148,0.0002871979,0.00005675659],"domain_scores_gemma":[0.9984381,0.0006966395,0.00008584938,0.000332226,0.0004170585,0.0000302245],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003151443,0.0001297761,0.002983099,0.0005645499,0.0001505364,0.0004258273,0.0007743558,0.3866792,0.03517691,0.09086651,0.007916755,0.4740173],"study_design_scores_gemma":[0.00001199026,0.0000305496,0.0004324496,0.00003877146,0.0000128647,0.0001054328,0.00005974754,0.9555721,0.01808876,0.006908129,0.01871138,0.00002775002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01443056,0.0003306673,0.974928,0.0001859402,0.0001661645,0.00004107134,0.0001545137,0.002042263,0.007720814],"genre_scores_gemma":[0.1835311,0.0007640246,0.7870429,0.0002000994,0.00007158346,0.0001741657,0.0005705507,0.001588781,0.02605682],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01197185,"threshold_uncertainty_score":0.03675425,"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."}}