{"id":"W4232991408","doi":"10.1515/iupac.88.0375","title":"Headspace Extraction","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada","funders":"","keywords":"Extraction (chemistry); Sample (material); Computer science; Sample preparation; Perspective (graphical); Data mining; Process engineering; Chromatography; Artificial intelligence; Engineering; Chemistry","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":[0.002813061,0.002122272,0.002156614,0.003987944,0.001094724,0.002812689,0.002654438,0.001678476,0.03426605],"category_scores_gemma":[0.009414235,0.0004802906,0.001627725,0.006145808,0.0006386696,0.001774421,0.002380622,0.001766059,0.0470585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001273893,"about_ca_system_score_gemma":0.003628265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006217823,"about_ca_topic_score_gemma":0.01147605,"domain_scores_codex":[0.9968795,0.0005173355,0.0005146732,0.001089402,0.0007532281,0.0002459294],"domain_scores_gemma":[0.9961952,0.00121388,0.0004957569,0.0008565911,0.001104513,0.0001339631],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001167801,0.0001661583,0.007203537,0.01698846,0.0004811912,0.0002128235,0.0001292937,0.001670997,0.003765896,0.003370693,0.8924434,0.07239981],"study_design_scores_gemma":[0.000187457,0.00005565385,0.005276716,0.000880024,0.000119157,0.0001228681,0.00007998677,0.0003389838,0.003064999,0.00266914,0.9871569,0.00004801988],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0009158236,0.001087254,0.001631616,0.0001121029,0.00007787802,0.0001901781,0.9925542,0.001109559,0.002321308],"genre_scores_gemma":[0.00154616,0.0008513441,0.003604393,0.0001492231,0.00002113965,0.0005712956,0.9919947,0.0001557556,0.001105987],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03426605,"threshold_uncertainty_score":0.1146314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03346271644888123,"score_gpt":0.4776937136533818,"score_spread":0.4442309972045005,"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."}}