{"id":"W2118819001","doi":"10.1007/s00180-007-0044-1","title":"Parameter cascades and profiling in functional data analysis","year":2007,"lang":"en","type":"article","venue":"Computational Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Smoothing; Nuisance parameter; Cascade; Range (aeronautics); Mathematical optimization; Estimation theory; Mathematics; Penalty method; Profiling (computer programming); Function (biology); Algorithm; Computer science; Applied mathematics; Statistics; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0009042666,0.0001184138,0.0002446117,0.0001798097,0.00007340774,0.00002925734,0.0001056638,0.00004800998,0.0000411652],"category_scores_gemma":[0.002123322,0.0001130252,0.0000182864,0.0003348489,0.00009298033,0.00009910455,0.0001113254,0.0001454183,0.000002385316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003617331,"about_ca_system_score_gemma":0.00003488873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001473031,"about_ca_topic_score_gemma":0.0001118436,"domain_scores_codex":[0.9986777,0.00005982128,0.0004157049,0.0003527422,0.0002817932,0.0002122671],"domain_scores_gemma":[0.9917223,0.007781907,0.0000969391,0.0001986738,0.0001101666,0.00009001731],"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":[0.00004408707,0.00007935823,0.005840956,0.00004655058,0.0001345859,0.00004040404,0.00009780289,0.01288192,0.000009584029,0.9513388,0.000329437,0.02915654],"study_design_scores_gemma":[0.0001852046,0.00001207845,0.02525234,0.000004687742,0.0000936098,0.000004902527,0.00003755767,0.3627975,0.000004859855,0.6114695,0.0000469984,0.00009075795],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0105182,0.00005179643,0.988157,0.00002985498,0.0000538571,0.000125593,0.0009097522,0.00002239126,0.0001315586],"genre_scores_gemma":[0.1635171,0.00000398907,0.8357961,0.00006574093,0.00003534926,0.000003197348,0.0005263644,0.00001088922,0.0000413129],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3499156,"threshold_uncertainty_score":0.4609032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2508627214871457,"score_gpt":0.4662053268861944,"score_spread":0.2153426053990487,"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."}}