{"id":"W4232171044","doi":"10.1515/iupac.88.0214","title":"Dynamic 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); Sample preparation; Process engineering; 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":[0.002672788,0.002305419,0.001767503,0.004094975,0.001078715,0.002824651,0.003080421,0.001668323,0.059687],"category_scores_gemma":[0.01044541,0.0005476424,0.001960691,0.006038989,0.0004349186,0.00175399,0.002532246,0.001826642,0.06335234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00141566,"about_ca_system_score_gemma":0.004055306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01125211,"about_ca_topic_score_gemma":0.02368259,"domain_scores_codex":[0.9971111,0.0005133575,0.000401669,0.001047757,0.0006621714,0.000263975],"domain_scores_gemma":[0.9951815,0.001489225,0.0006864427,0.001009193,0.001436617,0.0001970096],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006508548,0.00007796777,0.006831428,0.009750779,0.0004873385,0.00008507408,0.00006913723,0.0009177965,0.001541549,0.002261738,0.9473159,0.0300106],"study_design_scores_gemma":[0.000237333,0.00004675133,0.007134145,0.001145028,0.0001692283,0.00008776717,0.0000712378,0.0003626625,0.001489523,0.002636555,0.9865747,0.00004505037],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002626957,0.0002804907,0.0003960692,0.00005554683,0.00002898423,0.00004573452,0.9975601,0.0003864966,0.0009838806],"genre_scores_gemma":[0.000800903,0.0003088039,0.001393376,0.00009312107,0.00001188526,0.000268175,0.9961171,0.00009878723,0.0009078337],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.059687,"threshold_uncertainty_score":0.1996729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01980448090766247,"score_gpt":0.4525473488979192,"score_spread":0.4327428679902567,"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."}}