{"id":"W2031490939","doi":"10.1063/1.2363187","title":"Kernel representations for flux and concentration in ion channel models with time-varying concentrations","year":2006,"lang":"en","type":"article","venue":"The Journal of Chemical Physics","topic":"stochastic dynamics and bifurcation","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"National Science Foundation","keywords":"Kernel (algebra); Representation (politics); Probabilistic logic; Flux (metallurgy); Interpretation (philosophy); Variable kernel density estimation; Singularity; Boundary (topology); Heat kernel; Applied mathematics; Kernel method; Mathematics; Statistical physics; Computer science; Physics; Mathematical analysis; Chemistry; Artificial intelligence; Statistics; Combinatorics; Support vector machine","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":[0.00009146233,0.00006498246,0.0001031044,0.000009480896,0.00004838672,0.00002456841,0.00005741136,0.00001511668,0.000002488894],"category_scores_gemma":[0.000003222238,0.00004486947,0.00003036635,0.00007797381,0.00004884231,0.000181377,0.00000799359,0.00008566096,3.719389e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001903387,"about_ca_system_score_gemma":0.00004796975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003381575,"about_ca_topic_score_gemma":4.85042e-7,"domain_scores_codex":[0.9995298,0.0000133151,0.0002032008,0.00005872466,0.0001001844,0.00009472368],"domain_scores_gemma":[0.9994817,0.0001295223,0.0001858167,0.00005842695,0.0001213969,0.00002315682],"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":[0.0003512313,0.0004518308,0.001225699,0.00002020245,0.00009756941,3.984794e-7,0.00156236,0.2695166,0.511053,0.2122912,0.001052334,0.002377533],"study_design_scores_gemma":[0.00156037,0.00005602415,0.0001954315,0.00005967793,0.00008827599,0.000004184154,0.000169058,0.5943148,0.05832984,0.3451,0.000009418221,0.0001129427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.49411,0.00002288909,0.5041816,0.0002663779,0.00002422772,0.0001796905,0.00001400794,0.000002816689,0.001198429],"genre_scores_gemma":[0.9986746,0.000001106811,0.0007527129,0.00001625718,0.0004634056,0.000006166862,0.00004780222,0.000006689892,0.00003122038],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5045646,"threshold_uncertainty_score":0.1829724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0113421656522809,"score_gpt":0.2399269431978748,"score_spread":0.2285847775455939,"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."}}