{"id":"W4253441087","doi":"10.1515/iupac.88.0224","title":"Static Headspace Analysis","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":"Computer science; Extraction (chemistry); Sample (material); Process engineering; Sample preparation; Scale (ratio); Chromatography; Chemistry; Engineering; Physics","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":[0.002712705,0.002286681,0.001850243,0.004329943,0.00108178,0.002758176,0.003061125,0.001761695,0.06429636],"category_scores_gemma":[0.01162987,0.0005461848,0.001995266,0.006259939,0.0004802459,0.00178614,0.002440695,0.001793941,0.07199797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001500344,"about_ca_system_score_gemma":0.004585252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01145386,"about_ca_topic_score_gemma":0.02417799,"domain_scores_codex":[0.9970654,0.0004991649,0.000445581,0.001033316,0.0006762665,0.000280281],"domain_scores_gemma":[0.9945704,0.001633287,0.0007774802,0.001115836,0.00167863,0.0002243056],"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":[0.0006800788,0.00007760169,0.006230936,0.01064811,0.0004384841,0.00008255742,0.00006381089,0.0007758693,0.001446481,0.002050405,0.9501144,0.02739144],"study_design_scores_gemma":[0.0002537656,0.00004932629,0.007171997,0.001308148,0.0001702821,0.00008800351,0.00007185789,0.0002984608,0.001379274,0.00238109,0.9867834,0.00004433711],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002568702,0.0002871156,0.0003337499,0.0000520522,0.0000292314,0.00005064475,0.9975841,0.0003483077,0.001057923],"genre_scores_gemma":[0.000783017,0.0003165141,0.001225469,0.00009470013,0.00001229097,0.0003001601,0.996188,0.0000977724,0.0009821299],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06429636,"threshold_uncertainty_score":0.2150928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02835428355679271,"score_gpt":0.4652480672285514,"score_spread":0.4368937836717587,"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."}}