{"id":"W4236746952","doi":"10.1515/iupac.88.0201","title":"Salting in","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada","funders":"","keywords":"Computer science; Extraction (chemistry); Process engineering; Sample (material); Sample preparation; Throughput; 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":[0.002301289,0.003597551,0.002396906,0.005435498,0.001481274,0.004492942,0.003449623,0.002941082,0.08318327],"category_scores_gemma":[0.01458944,0.0008496204,0.003045642,0.008187748,0.00067306,0.003804964,0.003769851,0.00224315,0.1427081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001645289,"about_ca_system_score_gemma":0.003736604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01819339,"about_ca_topic_score_gemma":0.03849556,"domain_scores_codex":[0.9960967,0.0005953925,0.0006773061,0.001515439,0.0007237635,0.000391447],"domain_scores_gemma":[0.9943949,0.001431039,0.0006159947,0.00177623,0.001499097,0.000282858],"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":[0.0002554826,0.00004437151,0.005093416,0.002686317,0.0001249547,0.00004695701,0.00005196101,0.0004459661,0.0004845045,0.001640361,0.9695643,0.01956148],"study_design_scores_gemma":[0.0001718356,0.000037088,0.004911113,0.0009144684,0.00008545755,0.00009456221,0.0001046818,0.0006037371,0.0008116814,0.003283812,0.9889361,0.00004539733],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004265855,0.000577243,0.00065332,0.00018384,0.0001425321,0.00005752347,0.9943509,0.001197872,0.002409985],"genre_scores_gemma":[0.0008286632,0.0003407645,0.001572723,0.0002384073,0.00002443081,0.0001482267,0.9952826,0.0001957971,0.001368425],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08318327,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02654938081003374,"score_gpt":0.4798667193473733,"score_spread":0.4533173385373396,"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."}}