{"id":"W4231437535","doi":"10.1515/iupac.88.0315","title":"Downstream","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); Downstream (manufacturing); Process engineering; Throughput; Chromatography; Chemistry; Engineering; Operations management; Telecommunications","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002381813,0.003066118,0.001710756,0.004931422,0.001354683,0.003965465,0.003434014,0.002016226,0.1110622],"category_scores_gemma":[0.01262409,0.0007450808,0.002067805,0.008570181,0.000459535,0.002832099,0.003364772,0.002217696,0.1929128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001992284,"about_ca_system_score_gemma":0.004057576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02579042,"about_ca_topic_score_gemma":0.04698894,"domain_scores_codex":[0.9968773,0.0004253413,0.000357259,0.001272681,0.0006858284,0.00038167],"domain_scores_gemma":[0.9938128,0.001434562,0.0006193909,0.001669602,0.002121541,0.0003421102],"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":[0.0002294844,0.00002466811,0.003157149,0.0018451,0.00008573488,0.00003530165,0.00004911616,0.0003804669,0.0003841334,0.001657283,0.9790289,0.01312261],"study_design_scores_gemma":[0.00009579986,0.00001786611,0.004583453,0.0006312311,0.00005501842,0.00004749688,0.00008856432,0.0003254837,0.0005810589,0.002522498,0.9910216,0.00002993466],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001506351,0.0001483772,0.0003164038,0.00008708793,0.00003582391,0.00002680961,0.9966027,0.0005551625,0.002077058],"genre_scores_gemma":[0.0003382765,0.0001322423,0.0007746745,0.00010449,0.000009850244,0.00009028466,0.9972048,0.0001176654,0.001227598],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8889378,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02104272174063964,"score_gpt":0.4578003716880633,"score_spread":0.4367576499474236,"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."}}