{"id":"W2962842587","doi":"10.1016/j.csda.2018.06.010","title":"Model-based curve registration via stochastic approximation EM algorithm","year":2018,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Hydrology and Sediment Transport Processes","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Centre National de la Recherche Scientifique","keywords":"Image warping; Dynamic time warping; Smoothing; Inference; Algorithm; Functional data analysis; Curve fitting; Computer science; Image registration; Cluster analysis; Mathematics; Artificial intelligence; Computer vision; Machine learning; Image (mathematics)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003784118,0.0001504546,0.0001904218,0.0001089747,0.0003187124,0.00005340503,0.0003782719,0.00006430354,0.001522696],"category_scores_gemma":[0.00005911624,0.0001553065,0.00003803241,0.0007327312,0.0002945651,0.0003513285,0.0000756541,0.00009054088,0.000266345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005877447,"about_ca_system_score_gemma":0.00005150028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001776967,"about_ca_topic_score_gemma":0.001009404,"domain_scores_codex":[0.9983291,0.00005371747,0.0003589194,0.0005403776,0.0005263617,0.0001914985],"domain_scores_gemma":[0.9990795,0.0001391501,0.0001848986,0.0004263283,0.00008126504,0.00008881843],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001450877,0.00009645351,0.002650944,0.000006066953,0.0001555867,0.000002783408,0.0001020246,0.9791818,0.000009329156,0.0004674764,0.002044725,0.01526835],"study_design_scores_gemma":[0.000184837,0.00004081387,0.01096837,0.000002187651,0.0006491175,8.932612e-7,0.000004614566,0.9662914,0.000006520317,0.02158288,0.0001068421,0.0001614749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004497762,0.000008919593,0.9932811,0.00008571322,0.00004985479,0.0001124717,0.001792711,0.00004356655,0.0001279022],"genre_scores_gemma":[0.5981458,8.898202e-7,0.3825769,0.0001687506,0.0000326483,0.000006991201,0.01900634,0.000006839133,0.000054772],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6107042,"threshold_uncertainty_score":0.9993901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02648778613799462,"score_gpt":0.2813811823188129,"score_spread":0.2548933961808182,"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."}}