{"id":"W4241613844","doi":"10.1515/iupac.79.1451","title":"Immission","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Computer science; Toxicology; Library science; Chemistry; Philosophy; Biology; Linguistics","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":[0.001283931,0.001458732,0.00137339,0.004154884,0.0009369923,0.004555318,0.002101604,0.001485961,0.2533708],"category_scores_gemma":[0.01049246,0.0005906977,0.001496769,0.006985976,0.0003135914,0.002607252,0.002282289,0.001800206,0.3313125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001879447,"about_ca_system_score_gemma":0.003487553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01803661,"about_ca_topic_score_gemma":0.02716018,"domain_scores_codex":[0.9980153,0.0003243273,0.0003058283,0.0005996368,0.0004764439,0.0002784264],"domain_scores_gemma":[0.9954488,0.0009261132,0.0005379379,0.001170532,0.001460699,0.0004560191],"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":[0.00006545134,0.000009229516,0.0008351742,0.000439929,0.0000175905,0.00001148386,0.00001252449,0.00008643138,0.00004036326,0.0008595499,0.9907972,0.006825144],"study_design_scores_gemma":[0.00005438004,0.000006522496,0.001446337,0.0002867933,0.0000124428,0.00002744545,0.00003250524,0.00008877183,0.00006998803,0.0007502896,0.9972154,0.00000908676],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00008959341,0.000136121,0.0000878131,0.0001521452,0.00006250638,0.00001689733,0.9941339,0.0003530589,0.004968006],"genre_scores_gemma":[0.0004695583,0.0002109069,0.0002662855,0.0002209606,0.00003086814,0.00006556416,0.993849,0.0001476022,0.004739306],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7466292,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01578127347592428,"score_gpt":0.4323238645978017,"score_spread":0.4165425911218774,"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."}}