{"id":"W3113374883","doi":"","title":"Interpretable Bayesian Functional Linear Regression","year":2015,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Bayesian probability; Linear regression; Bayesian multivariate linear regression; Artificial intelligence; Bayesian linear regression; Regression; Machine learning; Computer science; Regression analysis; Mathematics; Statistics; Pattern recognition (psychology); Bayesian inference","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.01618697,0.0008091765,0.0008555598,0.0003008438,0.0006766854,0.001042741,0.003190376,0.0008401307,0.0006733563],"category_scores_gemma":[0.002426194,0.0007977997,0.0005064499,0.000848092,0.0005026144,0.0007612037,0.004410931,0.001726314,0.0003003373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003667484,"about_ca_system_score_gemma":0.001465143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001117953,"about_ca_topic_score_gemma":0.0005140962,"domain_scores_codex":[0.981057,0.01395473,0.001093394,0.001965614,0.001057854,0.0008714283],"domain_scores_gemma":[0.986366,0.001735813,0.0009547056,0.004467411,0.005671619,0.0008044748],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002914076,0.0006339725,0.0002403776,0.0001378649,0.000103839,0.00002194374,0.009278036,0.0002094357,0.001378883,0.74533,0.01322615,0.2294104],"study_design_scores_gemma":[0.0009139777,0.000001645705,0.0008771444,0.005428135,0.0000981235,0.0001154921,0.00005045425,0.6618315,0.01255704,0.1515218,0.1655041,0.00110057],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001009374,0.003947937,0.8465667,0.02641248,0.001957792,0.0004779273,0.00005145865,0.0003189016,0.1192574],"genre_scores_gemma":[0.06660939,0.0006559843,0.8032041,0.0004015503,0.0001928959,0.00008077906,0.0003020034,0.0000834184,0.1284699],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.661622,"threshold_uncertainty_score":0.9999943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03312253628124889,"score_gpt":0.2652861271825114,"score_spread":0.2321635909012625,"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."}}