{"id":"W7000196062","doi":"","title":"Estimation de paramètres en exploitant les aspects calculatoires et numériques","year":2017,"lang":"fr","type":"other","venue":"Open MIND","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Centre de Recherches Mathématiques; Natural Sciences and Engineering Research Council of Canada; Fondation J. Armand Bombardier","keywords":"Estimator; Probability density function; Bayesian probability; Parametric statistics; Random permutation; Permutation (music); Distribution (mathematics); Function (biology); Density estimation; Kernel density estimation","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":[0.002419928,0.001245541,0.001316755,0.001628825,0.0005750522,0.002928051,0.001271523,0.00134773,0.005639876],"category_scores_gemma":[0.01415198,0.0007065632,0.001609189,0.001644828,0.001309998,0.002778218,0.001158644,0.002141602,0.002226328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007028547,"about_ca_system_score_gemma":0.001329817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004453895,"about_ca_topic_score_gemma":0.005028442,"domain_scores_codex":[0.9983252,0.0005843135,0.0001010831,0.0003734193,0.0005368993,0.00007919672],"domain_scores_gemma":[0.9941819,0.004022171,0.0003337412,0.0007866932,0.0006154604,0.00006002786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000128801,0.00008213572,0.006854239,0.0009033288,0.0001950435,0.0003202941,0.0003518715,0.4313415,0.01695406,0.1758666,0.002538865,0.3644634],"study_design_scores_gemma":[0.00001518246,0.0001202109,0.002920869,0.0001854645,0.00008075706,0.000374798,0.0001178382,0.8646587,0.01270255,0.09335202,0.02537972,0.00009194617],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004828909,0.0007790173,0.9918199,0.0001894759,0.00008290947,0.00002210322,0.00007108124,0.000266982,0.001939706],"genre_scores_gemma":[0.1648629,0.004048975,0.8174898,0.0002078415,0.0003733817,0.0001958639,0.0004846835,0.0005613572,0.01177509],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005639876,"threshold_uncertainty_score":0.01886725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0571208781021043,"score_gpt":0.371826711030545,"score_spread":0.3147058329284407,"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."}}