{"id":"W2125772989","doi":"10.1002/jcc.21171","title":"A kernel‐based method to determine optimal sampling times for the simultaneous estimation of the parameters of rival mathematical models","year":2009,"lang":"en","type":"article","venue":"Journal of Computational Chemistry","topic":"Control Systems and Identification","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Canada Research Chairs","keywords":"Sampling (signal processing); Kernel (algebra); Estimation; Mathematics; Applied mathematics; Computer science; Biological system; Mathematical optimization; Biology; Combinatorics; Economics","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":[0.0003391196,0.00007437154,0.000190546,0.00002464144,0.00002973712,0.00001825059,0.0001644318,0.00003520157,0.000004925128],"category_scores_gemma":[0.0002079565,0.00004921923,0.0001489406,0.00007531259,0.00001474439,0.00005198126,0.000006236814,0.0000742731,2.162068e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003922973,"about_ca_system_score_gemma":0.00004158523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.71824e-7,"about_ca_topic_score_gemma":4.311967e-8,"domain_scores_codex":[0.9990855,0.00001365483,0.0005237307,0.00005123864,0.0002570753,0.0000688414],"domain_scores_gemma":[0.9982697,0.001113125,0.0002491278,0.00009337272,0.0002431272,0.00003155102],"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":[0.0000402393,0.00002296579,8.963644e-7,0.00009599343,0.00003677107,2.286857e-7,0.0001098679,0.9639665,0.0269703,0.0000434094,0.00003348239,0.008679322],"study_design_scores_gemma":[0.0003090249,0.0000277522,0.00007532311,0.0001097708,0.00005277612,0.00002852321,0.00002328015,0.9554104,0.03386943,0.01003902,0.000009784027,0.00004495186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2691337,0.00004704334,0.7304732,0.0001698633,0.00003022333,0.0001113585,0.000007743675,0.000004984945,0.00002187954],"genre_scores_gemma":[0.7777324,3.051588e-7,0.2221959,0.00001207601,0.00003829869,0.000002736858,0.000001737503,0.000005748145,0.00001082269],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5085987,"threshold_uncertainty_score":0.2007102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02397733520268379,"score_gpt":0.2854659222083885,"score_spread":0.2614885870057047,"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."}}