{"id":"W2951671519","doi":"10.1002/chin.200718278","title":"Bioanalytical Applications of Solid‐Phase Microextraction","year":2007,"lang":"en","type":"article","venue":"ChemInform","topic":"Advanced Materials Characterization Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Chemistry; Solid-phase microextraction; Bioanalysis; Nanotechnology; Phase (matter); Chromatography; World Wide Web; Organic chemistry; Computer science; 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":[],"consensus_categories":[],"category_scores_codex":[0.00006219486,0.00006305943,0.00009147445,0.00006546063,0.0000121362,0.000004164611,0.00005937263,0.00005803678,0.00008929964],"category_scores_gemma":[0.00000659228,0.00006757987,0.00002332134,0.0001160894,0.00002775588,0.0001304323,0.000009467033,0.00004179622,0.00001692142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003963343,"about_ca_system_score_gemma":0.000004362259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.789984e-7,"about_ca_topic_score_gemma":2.410146e-7,"domain_scores_codex":[0.9995506,4.590223e-8,0.0002422711,0.00004880313,0.00005309092,0.0001051495],"domain_scores_gemma":[0.9997404,0.00001434511,0.00004361683,0.0001253815,0.00003852544,0.00003776404],"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":[0.000005808148,0.0000169394,0.000001621241,0.00004210588,0.000009144367,3.371461e-7,0.00003024253,0.000001524547,0.8808128,0.0002465945,0.0002644755,0.1185685],"study_design_scores_gemma":[0.0001073363,0.000007986509,0.00003566535,0.00000575993,0.000008672479,0.000004773093,0.00001577785,0.00008005987,0.9739015,0.0001522707,0.0256156,0.00006465606],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02852736,0.00003900998,0.8625721,0.00001525076,0.0001392883,0.0003962446,0.0001035829,0.001042402,0.1071648],"genre_scores_gemma":[0.9958193,0.00004970057,0.003520097,0.00001431207,0.00007074422,0.00002470971,0.0000805692,0.00001709286,0.000403449],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.967292,"threshold_uncertainty_score":0.2755827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0085352163079383,"score_gpt":0.2998002048998584,"score_spread":0.2912649885919201,"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."}}