{"id":"W4249946149","doi":"10.1515/iupac.88.0369","title":"Flow-Through Techniques","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Extraction and Separation Processes","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada","funders":"","keywords":"Sample (material); Computer science; Perspective (graphical); Extraction (chemistry); Scale (ratio); Solvent extraction; Data science; Sample preparation; Data mining; Artificial intelligence; Chromatography; Chemistry; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000175725,0.0003567934,0.0004235836,0.0001128896,0.0001683046,0.0001841833,0.0004120895,0.0004253514,0.002135859],"category_scores_gemma":[0.0002105898,0.0003487653,0.0001035165,0.00007567648,0.00006920115,0.0003223114,0.00004483373,0.0005960496,0.00001190975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000184402,"about_ca_system_score_gemma":0.0002202707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003489248,"about_ca_topic_score_gemma":0.0005663714,"domain_scores_codex":[0.9985816,0.00001592689,0.0003546654,0.0002618583,0.0005342688,0.0002517039],"domain_scores_gemma":[0.9987245,0.00002837677,0.0001248009,0.0007551476,0.0002870888,0.00008008254],"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":[0.00001206952,0.00003940684,2.63883e-7,0.0002829683,0.00005250466,0.00002374101,0.00001283401,0.0001487112,0.00001600133,0.000004004656,0.9958786,0.003528929],"study_design_scores_gemma":[0.0001486274,0.00003993307,0.000001392228,0.000168273,0.00004783198,0.00001650162,0.000009726428,0.0003042555,0.0004951342,0.0001548227,0.9982328,0.0003806986],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000002864413,0.001149085,0.003412302,0.0001210756,0.001027631,0.0001694764,0.9911283,0.0006464729,0.002342786],"genre_scores_gemma":[0.000007217356,0.004821131,0.001163672,0.0001531675,0.0008081814,0.00002883339,0.9918812,0.00004992713,0.001086672],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.003672046,"threshold_uncertainty_score":0.9998964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01964014664195394,"score_gpt":0.4307164230652533,"score_spread":0.4110762764232993,"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."}}