{"id":"W4229557287","doi":"10.1515/iupac.88.0169","title":"Calibrant in Extraction Phase","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); Computer science; Sample (material); Scale (ratio); Process engineering; Sample preparation; Data extraction; 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":[0.003914632,0.002795619,0.002025193,0.00346343,0.001079945,0.002993512,0.002781039,0.002448912,0.04936741],"category_scores_gemma":[0.01742702,0.0008223143,0.002371216,0.004950297,0.0007003493,0.002440808,0.002381493,0.002697359,0.06860165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001708725,"about_ca_system_score_gemma":0.003060925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008929305,"about_ca_topic_score_gemma":0.01483276,"domain_scores_codex":[0.9949301,0.0009711305,0.0008213716,0.001968758,0.0009278305,0.000380762],"domain_scores_gemma":[0.9911199,0.003080558,0.001307109,0.002170724,0.002131433,0.0001902971],"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":[0.001408662,0.0001477167,0.01515674,0.01033554,0.0005423012,0.0001119757,0.00008759154,0.001858477,0.002167654,0.002919636,0.9219856,0.04327811],"study_design_scores_gemma":[0.0003596334,0.00008403108,0.009363801,0.001108155,0.0002140485,0.0001236482,0.00007458244,0.0005705678,0.002467824,0.002711866,0.9828711,0.00005075735],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007051036,0.0006300548,0.0006817818,0.0001133035,0.00009646175,0.00007689779,0.9956813,0.0004807848,0.001534398],"genre_scores_gemma":[0.002069508,0.0003447256,0.001760978,0.0002006585,0.00002532987,0.0002932853,0.993888,0.0001239774,0.001293568],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04936741,"threshold_uncertainty_score":0.1651505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03725035317860893,"score_gpt":0.495497884519132,"score_spread":0.458247531340523,"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."}}