{"id":"W2268310503","doi":"10.5539/ijsp.v5n2p35","title":"Transmetric Density Estimation","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Estimator; Mathematics; Kernel density estimation; Kernel (algebra); Mean squared error; Convergence (economics); Metric (unit); Generalization; Variable kernel density estimation; Applied mathematics; Type (biology); Density estimation; Mathematical optimization; Statistics; Kernel method; Discrete mathematics; Computer science; Mathematical analysis; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.00880189,0.001509055,0.001669678,0.00412067,0.00112922,0.003550535,0.003290612,0.003589373,0.01652912],"category_scores_gemma":[0.06196798,0.0009053275,0.001951976,0.003565623,0.002498292,0.007000742,0.006079427,0.005060568,0.005860088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001830752,"about_ca_system_score_gemma":0.001802889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002505003,"about_ca_topic_score_gemma":0.002307312,"domain_scores_codex":[0.9951254,0.002411055,0.0002801778,0.0007836281,0.001194282,0.0002055139],"domain_scores_gemma":[0.9809507,0.008663213,0.001343612,0.004794928,0.00373659,0.0005110124],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001608238,0.00009545356,0.002596074,0.0003125834,0.0001521328,0.0001986115,0.0002333338,0.06908972,0.002086347,0.6951353,0.02032187,0.2096177],"study_design_scores_gemma":[0.00002581173,0.00009534718,0.0008702884,0.0001000975,0.00004686486,0.0005501273,0.00009805425,0.556133,0.002098592,0.419807,0.02011398,0.00006070389],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002684163,0.0004290379,0.9912251,0.0004450219,0.0001360863,0.00005978315,0.0002281354,0.000377797,0.004414924],"genre_scores_gemma":[0.2011484,0.001988205,0.7665244,0.001380098,0.0008395239,0.0004501559,0.002212921,0.001090167,0.02436617],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01652912,"threshold_uncertainty_score":0.05529541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01615057286510684,"score_gpt":0.2710930595122827,"score_spread":0.2549424866471759,"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."}}