{"id":"W2128968645","doi":"10.1016/j.aca.2012.01.034","title":"Semi-automated in vivo solid-phase microextraction sampling and the diffusion-based interface calibration model to determine the pharmacokinetics of methoxyfenoterol and fenoterol in rats","year":2012,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Analytical Chemistry and Chromatography","field":"Chemistry","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Chemistry; Pharmacokinetics; Solid-phase microextraction; Chromatography; In vivo; Diffusion; Sampling (signal processing); Analytical Chemistry (journal); Pharmacology; Mass spectrometry; Gas chromatography–mass spectrometry; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000709739,0.0004122342,0.0005477137,0.0002919117,0.0003044162,0.000441278,0.0005421398,0.0005343273,0.0004449053],"category_scores_gemma":[0.0007806542,0.0003196583,0.0003844986,0.0002908264,0.0003016154,0.0004522413,0.0002769296,0.0007998949,0.0003186368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006100569,"about_ca_system_score_gemma":0.00107157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003537143,"about_ca_topic_score_gemma":0.005595722,"domain_scores_codex":[0.9994685,0.0001190224,0.00002000779,0.0001322123,0.0002241932,0.00003606396],"domain_scores_gemma":[0.9996152,0.0001059212,0.00009630922,0.00005249187,0.0001080303,0.00002197788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001653944,0.000307766,0.002054067,0.0001327526,0.00006822361,0.00003254694,0.00009958717,0.006297198,0.9399216,0.0005142244,0.0003450047,0.04857313],"study_design_scores_gemma":[0.0001057653,0.0008473715,0.005042155,0.000007647504,0.00008999871,0.0001547051,0.00003256716,0.08839808,0.9028258,0.000227268,0.002224694,0.00004380978],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6736385,0.001174539,0.3219755,0.0002015187,0.00007628839,0.0002058985,0.0006151311,0.001125417,0.0009873135],"genre_scores_gemma":[0.8571693,0.0008640012,0.1375309,0.0001210604,0.00002638294,0.0003000243,0.0003386733,0.0001349051,0.00351481],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003537143,"threshold_uncertainty_score":0.00703311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02540764933910742,"score_gpt":0.3356062219147383,"score_spread":0.3101985725756309,"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."}}