{"id":"W4385999578","doi":"10.1002/cjs.11794","title":"Contrast tests for groups of functional data","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Aeronautics and Space Administration; National Oceanic and Atmospheric Administration; National Science Foundation","keywords":"Contrast (vision); Functional data analysis; Test statistic; Functional principal component analysis; Analysis of variance; Mathematics; Statistics; Covariance; Computer science; Statistical hypothesis testing; 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":[0.02415805,0.001028817,0.001498805,0.004189819,0.001129698,0.001987293,0.002865302,0.001675221,0.01242913],"category_scores_gemma":[0.2201024,0.0003913612,0.002330904,0.002523537,0.003598096,0.004534289,0.002813296,0.003097384,0.000791225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001384659,"about_ca_system_score_gemma":0.001131117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001108987,"about_ca_topic_score_gemma":0.0007305062,"domain_scores_codex":[0.9738207,0.01656281,0.001326774,0.004089304,0.003603635,0.0005968635],"domain_scores_gemma":[0.6952741,0.2784101,0.007522208,0.01040211,0.006865401,0.001526088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.01262858,0.00122982,0.1658087,0.002421461,0.005452262,0.00270691,0.002391206,0.09502905,0.01991359,0.2840384,0.01481368,0.3935664],"study_design_scores_gemma":[0.001016574,0.005193665,0.08337454,0.000390754,0.0008657553,0.0016252,0.001324719,0.4969916,0.01525481,0.3794445,0.01422451,0.0002932956],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2229313,0.0007851336,0.7662585,0.0006013683,0.0004849317,0.0005907387,0.001607641,0.001331794,0.005408553],"genre_scores_gemma":[0.8000641,0.0001173453,0.1953342,0.0002186297,0.0001968494,0.001207868,0.001447496,0.000315398,0.001098192],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02415805,"threshold_uncertainty_score":0.1277615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.393885353112752,"score_gpt":0.386793569681691,"score_spread":0.007091783431060983,"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."}}