{"id":"W4385963506","doi":"10.1080/10618600.2023.2233581","title":"Functional Nonlinear Learning","year":2023,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Functional principal component analysis; Functional data analysis; Principal component analysis; Computer science; Multivariate statistics; Generalization; Nonlinear system; Artificial intelligence; Representation (politics); Pattern recognition (psychology); Data mining; Machine learning; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0002042566,0.00006217304,0.0001124357,0.00009743939,0.00009408715,0.00001660301,0.00003717469,0.00003571009,0.00001324473],"category_scores_gemma":[0.000196597,0.00005083888,0.00004499182,0.0001518408,0.00006683906,0.000002454714,0.00003917727,0.000115642,0.000003405762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002220455,"about_ca_system_score_gemma":0.00003263396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.977433e-7,"about_ca_topic_score_gemma":8.835448e-7,"domain_scores_codex":[0.9994127,0.00002842493,0.0002061716,0.00007909597,0.0001831989,0.00009041879],"domain_scores_gemma":[0.9994922,0.00009129127,0.0001110816,0.00002300224,0.0002198161,0.0000626005],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.001482767,0.0005791701,0.1330018,0.0002049639,0.002377454,0.000261419,0.0002509147,0.1638674,0.05821094,0.3161339,0.2459101,0.07771918],"study_design_scores_gemma":[0.001850759,0.001496439,0.6660939,0.00001739434,0.00008704775,0.0002758816,0.0001383005,0.01637243,0.0004706318,0.177028,0.13585,0.0003192569],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8051842,0.0005347203,0.1930962,0.0007280292,0.0002573706,0.00003256424,0.00006020705,0.000006482975,0.0001002037],"genre_scores_gemma":[0.9646617,0.001142306,0.03314974,0.0001631383,0.0004283217,8.990888e-7,0.0001481278,0.000009190337,0.0002965649],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5330921,"threshold_uncertainty_score":0.207315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01122446183980309,"score_gpt":0.2531441275216021,"score_spread":0.2419196656817991,"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."}}