{"id":"W3046203440","doi":"10.3390/molecules25153448","title":"Systematic Evaluation of Different Coating Chemistries Used in Thin-Film Microextraction","year":2020,"lang":"en","type":"article","venue":"Molecules","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council; U.S. Department of Veterans Affairs","keywords":"Solid-phase microextraction; Extraction (chemistry); Graphene; Materials science; Coating; Solid phase extraction; Polyacrylonitrile; Oxide; Carbon nanotube; Divinylbenzene; Chemical engineering; Polystyrene; Chromatography; Nanotechnology; Chemistry; Gas chromatography–mass spectrometry; Mass spectrometry; Polymer; Styrene; Copolymer","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004592672,0.0001625627,0.0003719898,0.00001973482,0.00002429667,0.00002067493,0.0001556974,0.0001014323,0.0003187667],"category_scores_gemma":[0.002004316,0.0001499387,0.00007476778,0.0001276093,0.00004008626,0.00003787801,0.00004231474,0.0001689898,0.000005946508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001849036,"about_ca_system_score_gemma":0.00007198862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006409499,"about_ca_topic_score_gemma":0.000002132175,"domain_scores_codex":[0.9983207,0.00008722616,0.000591115,0.0002581291,0.0005845917,0.0001582483],"domain_scores_gemma":[0.9991285,0.0002262379,0.0002614027,0.0001818435,0.0001268445,0.00007519111],"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":[0.00001491435,0.00004844884,0.001399763,0.009325913,0.00004331969,0.000002519388,0.0005496494,0.0001949487,0.9882774,0.0000323169,0.000009354932,0.0001014868],"study_design_scores_gemma":[0.0004268668,0.000004633594,0.0002612034,0.001160145,0.0001081491,0.000002175444,0.0008936321,0.03340074,0.9634543,0.0001471206,0.000004584289,0.0001364745],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9864826,0.0002851371,0.008834863,0.0002305622,0.0000152001,0.0001533244,0.000006167387,0.00004390798,0.003948275],"genre_scores_gemma":[0.9974001,0.000004812257,0.002393085,0.00002521991,0.00002641715,0.00004550061,0.00002594334,0.00001586963,0.00006306971],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03320578,"threshold_uncertainty_score":0.6114323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05371002283115631,"score_gpt":0.307775904186628,"score_spread":0.2540658813554717,"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."}}