{"id":"W2139544932","doi":"10.4155/bio.12.250","title":"<i>In Vivo</i> Solid-Phase Microextraction For Tissue Bioanalysis","year":2012,"lang":"en","type":"review","venue":"Bioanalysis","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bioanalysis; In vivo; Solid-phase microextraction; Nanotechnology; Chemistry; Sample preparation; Biochemical engineering; Chromatography; Biocompatible material; Biomedical engineering; Materials science; Biotechnology; Mass spectrometry; Biology; Medicine; Gas chromatography–mass spectrometry","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001063406,0.001111019,0.004251258,0.0007148306,0.0001397541,0.0001120341,0.0008323338,0.001196918,0.009307447],"category_scores_gemma":[0.0003570391,0.001002331,0.002558308,0.001813423,0.0001351227,0.0001857964,0.0001694256,0.0006357412,0.0002386074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001231072,"about_ca_system_score_gemma":0.0003159717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007159427,"about_ca_topic_score_gemma":0.00003360139,"domain_scores_codex":[0.9947072,0.0001334056,0.002205943,0.00134409,0.0005469571,0.001062393],"domain_scores_gemma":[0.9960194,0.0008279929,0.001208965,0.001313486,0.00017605,0.0004541272],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001789944,0.0005481357,0.000004259978,0.01801045,0.00369526,0.00003221484,0.00002081256,8.531147e-7,0.001385203,0.00001624961,0.0004143162,0.9758543],"study_design_scores_gemma":[0.0004315984,0.000008480234,2.754157e-8,0.001152923,0.02376063,0.00002354315,0.00003177547,0.00008679627,0.009348628,0.00002933694,0.9641013,0.001024959],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[6.814861e-7,0.9835047,0.01230201,0.00006162667,0.0001435846,0.0002943729,0.0005655762,0.00008399704,0.003043417],"genre_scores_gemma":[0.00001628153,0.9546223,0.0188726,0.00004093766,0.0008392668,0.0005200892,0.000828484,0.0001602586,0.02409982],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9748294,"threshold_uncertainty_score":0.9992427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08074335308277965,"score_gpt":0.4412389807519767,"score_spread":0.3604956276691971,"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."}}