{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003713774,0.000237198,0.0003677848,0.000109755,0.0000780813,0.00005192789,0.0002081455,0.0001348662,0.00008147774],"category_scores_gemma":[0.0001629935,0.0001585315,0.00008109627,0.0003168153,0.0002227213,0.0001350037,0.0001036449,0.0003149166,4.361702e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000350563,"about_ca_system_score_gemma":0.00002768398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003733529,"about_ca_topic_score_gemma":0.00002538737,"domain_scores_codex":[0.9986206,0.00005907309,0.0005299396,0.0002796681,0.000180997,0.0003297446],"domain_scores_gemma":[0.9986857,0.0006627446,0.0001627475,0.0003124001,0.00003491329,0.0001415132],"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":[0.0004411846,0.0002433094,0.001730021,0.0001041322,0.00004398036,8.13744e-7,0.0006440952,0.0006941251,0.9958992,0.00001184693,0.00005755332,0.0001297505],"study_design_scores_gemma":[0.001325494,0.000009830784,0.0001637872,0.00006445975,0.00009482648,0.000004971234,0.000103392,0.5574934,0.4405663,0.00004974284,0.00002212362,0.0001016699],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996281,0.00003946867,0.002005629,0.001247607,0.00001455519,0.0001690182,0.00002988945,0.00004067818,0.0001721341],"genre_scores_gemma":[0.9991341,0.00005763063,0.0003285138,0.0003584322,0.0000435939,0.00002073979,0.00001029415,0.00002158943,0.00002510141],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5567992,"threshold_uncertainty_score":0.6464729,"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."}}