{"id":"W4403482142","doi":"10.1101/2024.10.15.618412","title":"Parameter Dependence in Identifiability Applied to FP-Fisher-KPP Reaction-Diffusion Equations Parameterized for Tauopathy Network Modeling","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Identifiability; Parameterized complexity; Tauopathy; Diffusion; Applied mathematics; Mathematics; Physics; Statistics; Combinatorics; Thermodynamics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001555648,0.0006962338,0.0007729791,0.0003052065,0.0001874904,0.0002936001,0.0006620461,0.0009200011,0.00001009186],"category_scores_gemma":[0.0005417207,0.0007960538,0.000444138,0.0007732569,0.00006742933,0.00001169733,0.001164702,0.0006273853,0.00003629482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002857872,"about_ca_system_score_gemma":0.0004986866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000732551,"about_ca_topic_score_gemma":0.0001020491,"domain_scores_codex":[0.9954017,0.0001986587,0.00101169,0.002160428,0.0003989316,0.0008285242],"domain_scores_gemma":[0.9968309,0.0001072471,0.0003180832,0.002028139,0.0004031503,0.0003124126],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001505298,0.0001090356,0.001109569,0.0002516545,0.0002211172,0.000005189347,0.000008431095,0.1547776,0.843062,0.00009043373,0.0001988114,0.00001564166],"study_design_scores_gemma":[0.00207159,0.0002905921,0.01195444,0.001097074,0.001495892,9.206293e-8,0.00002558348,0.6733065,0.2999218,0.0007960339,0.004671564,0.004368752],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8721734,0.0008487795,0.1237582,0.0001175728,0.001083145,0.001783486,0.0001095125,0.0001206591,0.00000521171],"genre_scores_gemma":[0.9771747,0.0001130284,0.01954978,0.0001807894,0.000898359,0.001881474,0.00001108845,0.0001789477,0.00001181953],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5431402,"threshold_uncertainty_score":0.999449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01716048348860537,"score_gpt":0.2406874693407372,"score_spread":0.2235269858521318,"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."}}