{"id":"W4233561799","doi":"10.1515/iupac.88.0178","title":"Exhaustive 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":"Computer science; Extraction (chemistry); Sample (material); Scale (ratio); Process engineering; Chromatography; Engineering; Chemistry; 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":[0.003261631,0.002663956,0.002296618,0.005808857,0.001278461,0.003121524,0.003403961,0.002575963,0.07242929],"category_scores_gemma":[0.01815337,0.0008342496,0.002269238,0.00826533,0.0005955833,0.002614606,0.002979178,0.002582017,0.09624895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001833654,"about_ca_system_score_gemma":0.005601363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01152689,"about_ca_topic_score_gemma":0.02522841,"domain_scores_codex":[0.9960503,0.0008151327,0.0006670387,0.001267793,0.0008320406,0.0003677726],"domain_scores_gemma":[0.9934599,0.002279784,0.0008085746,0.001396271,0.001787369,0.0002681277],"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":[0.0003676244,0.00004366383,0.002006937,0.008474297,0.0002049485,0.00006356419,0.00005316845,0.0005077007,0.0007488561,0.00200378,0.9670391,0.01848637],"study_design_scores_gemma":[0.0002144334,0.00002326899,0.002129318,0.001270238,0.0000888987,0.00006246072,0.00005273738,0.0002082513,0.0006034346,0.002002507,0.9933138,0.00003059331],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001732704,0.0004607135,0.0004875258,0.00009962185,0.00004511539,0.00006530208,0.9967661,0.0005559145,0.001346452],"genre_scores_gemma":[0.0004051845,0.0004137592,0.001399734,0.0001450073,0.00001192017,0.0003369991,0.9962464,0.0001330384,0.0009079129],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07242929,"threshold_uncertainty_score":0.2423002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03438694911216963,"score_gpt":0.4756832651523015,"score_spread":0.4412963160401319,"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."}}