{"id":"W4398190802","doi":"10.2139/ssrn.4836407","title":"Multivariate Kernel Regression in Vector and Product Metric Spaces","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Multivariate statistics; Mathematics; Kernel (algebra); Metric (unit); Product (mathematics); Product metric; Inner product space; Regression; Metric space; Pure mathematics; Statistics; Business; Geometry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002125343,0.0008268429,0.001229589,0.001089178,0.0002880297,0.001821376,0.001040376,0.001027059,0.002973708],"category_scores_gemma":[0.009659262,0.0004766665,0.000674481,0.002010745,0.00105961,0.003705934,0.001646879,0.001511306,0.0009407587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005973584,"about_ca_system_score_gemma":0.0007252061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002882189,"about_ca_topic_score_gemma":0.001647597,"domain_scores_codex":[0.99856,0.0006957432,0.00007605384,0.0002157895,0.0003738899,0.00007855745],"domain_scores_gemma":[0.9961877,0.001944825,0.0004885101,0.0005189833,0.0007045637,0.0001553032],"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":[0.0002255628,0.0001165139,0.001788495,0.000324371,0.0001708622,0.0001454072,0.0001965734,0.1637961,0.005673245,0.609718,0.006672124,0.2111727],"study_design_scores_gemma":[0.000009575986,0.00003599164,0.0007857649,0.00001256264,0.00001792961,0.00006458612,0.00002531135,0.8358449,0.0007653647,0.1594935,0.00292338,0.0000211049],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01838854,0.001114932,0.9784204,0.0002918963,0.00008210494,0.00001496812,0.00009181193,0.0002080529,0.001387198],"genre_scores_gemma":[0.6036913,0.004719249,0.3646132,0.0001910475,0.0006330984,0.0001365845,0.0007008937,0.0005412028,0.0247735],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002973708,"threshold_uncertainty_score":0.01124001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01402657941338281,"score_gpt":0.2727096635120494,"score_spread":0.2586830840986666,"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."}}