{"id":"W2589650277","doi":"10.1016/j.aca.2017.02.014","title":"Inter-laboratory validation of a thin film microextraction technique for determination of pesticides in surface water samples","year":2017,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":67,"is_retracted":false,"has_abstract":false,"ca_institutions":"Maxxam (Canada); University of Waterloo","funders":"","keywords":"Repeatability; Chemistry; Detection limit; Polydimethylsiloxane; Chromatography; Thermal desorption; Analyte; Analytical Chemistry (journal); Extraction (chemistry); Solid-phase microextraction; Membrane; Linear range; Sample preparation; Divinylbenzene; Desorption; Gas chromatography–mass spectrometry; Mass spectrometry; Adsorption; Styrene; Polymer","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":[0.002963326,0.001214579,0.0006898104,0.0008283672,0.001261619,0.0008758599,0.001187625,0.001883899,0.0007126919],"category_scores_gemma":[0.003543798,0.0007392939,0.0009145244,0.0004979738,0.001227253,0.0005589648,0.001124955,0.001166655,0.0009081583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007977981,"about_ca_system_score_gemma":0.001683366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002714069,"about_ca_topic_score_gemma":0.006570506,"domain_scores_codex":[0.9940409,0.001185492,0.0003450696,0.001405802,0.002821161,0.0002015696],"domain_scores_gemma":[0.9972113,0.0007815291,0.0003006585,0.0004525193,0.001142909,0.0001111533],"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":[0.0002872763,0.0001725696,0.001324786,0.00005697036,0.00007967795,0.00002836505,0.0001115653,0.0002860283,0.9916584,0.00006808505,0.00007677819,0.00584945],"study_design_scores_gemma":[0.00005501047,0.001401794,0.006880888,0.00001863473,0.0001437182,0.0003106143,0.00007121959,0.00426747,0.9846215,0.0001145503,0.002084936,0.00002969095],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8114101,0.001418378,0.1819005,0.0004243648,0.0003640967,0.0008250658,0.0007628552,0.001062599,0.001832137],"genre_scores_gemma":[0.8452602,0.001040244,0.14596,0.000759292,0.00007777134,0.0009809013,0.00116157,0.0001718632,0.004588126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002963326,"threshold_uncertainty_score":0.01567173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03404995690260203,"score_gpt":0.3212126193681989,"score_spread":0.2871626624655969,"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."}}