{"id":"W2407202096","doi":"10.1002/9781118445112.stat06392","title":"Functional data analysis ‐ An Introduction","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Functional data analysis; Flexibility (engineering); Computer science; Data mining; Mathematics; Statistics; Machine learning","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009465017,0.0007947586,0.001498182,0.0008047898,0.0001401511,0.0001499204,0.001216219,0.0005404816,0.02638327],"category_scores_gemma":[0.004171626,0.0007080947,0.00008745343,0.0008363762,0.0003562645,0.0001201088,0.0004215888,0.0009047563,0.0003323294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009969472,"about_ca_system_score_gemma":0.000270834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003447146,"about_ca_topic_score_gemma":0.003658991,"domain_scores_codex":[0.9947083,0.0006305807,0.001083687,0.001821863,0.001086989,0.0006685993],"domain_scores_gemma":[0.9932438,0.001307672,0.0009755515,0.0036276,0.0004083932,0.0004369528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000301868,0.0003466623,0.00003882964,0.0001716862,0.0006752139,0.000008785623,0.00001269047,0.00001013153,0.00000779868,0.3531777,0.6223724,0.02314785],"study_design_scores_gemma":[0.0005259952,0.0003424718,0.000489955,0.0001310707,0.003484407,0.000008789107,0.00005748157,0.03096066,0.000002139329,0.2902069,0.6726182,0.001172011],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000006232253,0.0001132699,0.8828464,0.0001648482,0.0007619539,0.0003254586,0.1054423,0.0003779485,0.009961596],"genre_scores_gemma":[0.00003760459,0.0003859937,0.7931891,0.0001107511,0.002247557,0.00002253643,0.09375478,0.0004667279,0.1097849],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.09982332,"threshold_uncertainty_score":0.999537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2126113253773755,"score_gpt":0.4236762838336573,"score_spread":0.2110649584562818,"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."}}