{"id":"W2017981243","doi":"10.1016/j.chroma.2012.04.001","title":"Analysis of synthetic canine training aids by comprehensive two-dimensional gas chromatography–time of flight mass spectrometry","year":2012,"lang":"en","type":"article","venue":"Journal of Chromatography A","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University; University of Ontario Institute of Technology","funders":"","keywords":"Chemistry; Chromatography; Mass spectrometry; Gas chromatography; Gas chromatography–mass spectrometry; Time-of-flight mass spectrometry; Organic chemistry; Ion","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"],"consensus_categories":[],"category_scores_codex":[0.0001579193,0.0003512916,0.001383576,0.002275594,0.0000364617,0.000009861754,0.0004043007,0.0001763007,0.0001803149],"category_scores_gemma":[0.00005653869,0.0003115575,0.001047973,0.003862643,0.0003488503,0.0002767515,0.00003947866,0.0004129981,0.000001931322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005701025,"about_ca_system_score_gemma":0.00001071055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005142303,"about_ca_topic_score_gemma":5.078084e-7,"domain_scores_codex":[0.9974605,0.00004525925,0.001139376,0.0001662892,0.0006482573,0.0005403288],"domain_scores_gemma":[0.998156,0.0003275819,0.0007146859,0.0003587781,0.000229123,0.0002138261],"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":[0.00002927398,0.0001211284,0.009119264,0.00009334274,0.004138438,0.0000114622,0.0002004421,0.0112954,0.9742115,0.00007174993,0.0003677601,0.0003402204],"study_design_scores_gemma":[0.0009060773,0.000261742,0.005572342,0.0002378284,0.001791176,0.0001697732,0.000458417,0.002261856,0.9872867,0.0004656997,0.000182556,0.0004057681],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9911456,0.006449958,0.001683239,0.00002602834,0.0001299718,0.00008345956,0.00007571957,0.0001356084,0.0002704611],"genre_scores_gemma":[0.9830983,0.0001696075,0.01660401,0.00001240426,0.00005402852,0.000002382972,0.000009896827,0.00004725439,0.000002138241],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01492077,"threshold_uncertainty_score":0.9999337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008184729486367136,"score_gpt":0.2226486039547805,"score_spread":0.2144638744684133,"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."}}