{"id":"W4386001407","doi":"10.1002/cjs.11796","title":"Robust joint modelling of sparsely observed paired functional data","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Functional principal component analysis; Overfitting; Principal component analysis; Functional data analysis; Computer science; Multivariate statistics; Computation; Rank (graph theory); Mathematics; Algorithm; Artificial intelligence; Machine learning; Artificial neural network","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":{"n_in":1,"stratum":"venue_new","weight":2684.24666666667,"opus":{"tier":"T1","genre":"empirical","about_ca":false,"confidence":"low","reason":"Develops and compares a robust statistical estimator for paired functional data, studying its properties via simulation against an existing method; borderline between statistical-methods research and pure domain statistics."},"gpt":{"tier":"OUT","genre":"empirical","about_ca":false,"confidence":"high","reason":"This develops a statistical model for functional data, not a study of how research is conducted."},"grok":{"tier":"OUT","genre":"empirical","about_ca":false,"confidence":"high","reason":"Develops a robust statistical model for paired functional data (supernova light curves); domain methods development, not study of research."}},"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01505026,0.001610137,0.003099113,0.001542186,0.0005928294,0.002278867,0.004957044,0.002514917,0.003360923],"category_scores_gemma":[0.03283983,0.001599329,0.004259147,0.001663256,0.002588384,0.00256471,0.002711388,0.003209853,0.0007899495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001230212,"about_ca_system_score_gemma":0.001680773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006357272,"about_ca_topic_score_gemma":0.005300846,"domain_scores_codex":[0.9933904,0.003830701,0.0002647553,0.001399744,0.0007794307,0.0003348576],"domain_scores_gemma":[0.985431,0.009601097,0.001780934,0.001883614,0.001052226,0.0002512868],"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":[0.0002734234,0.0001005939,0.002661827,0.0002057922,0.0005221313,0.0002465861,0.0002355244,0.9073473,0.002535669,0.04717884,0.0008563902,0.03783591],"study_design_scores_gemma":[0.00001787536,0.00004510618,0.0008202738,0.00001381854,0.00003831019,0.00003425422,0.00001421651,0.9780873,0.0004858277,0.01993362,0.0004787838,0.00003060264],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01024305,0.0001121262,0.9888177,0.00009616469,0.00002361413,0.00004802439,0.0002033096,0.0002210057,0.0002350838],"genre_scores_gemma":[0.5365757,0.0003763641,0.4543651,0.0002582264,0.0001105438,0.0009184955,0.001764824,0.0002954545,0.005335222],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01505026,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8860408287042677,"score_gpt":0.3379450795875754,"score_spread":0.5480957491166922,"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."}}