{"id":"W4233266715","doi":"10.1515/iupac.88.0500","title":"Arachnoid Membrane","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Glossary; Terminology; Relation (database); Computer science; Linguistics; Philosophy; Data mining","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":[0.0008207541,0.001328873,0.00145535,0.004345457,0.0007471929,0.002678365,0.001773519,0.001229487,0.08990179],"category_scores_gemma":[0.007727636,0.0005377844,0.001953019,0.005030132,0.0004561754,0.002247739,0.002111848,0.001579124,0.0586892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001132846,"about_ca_system_score_gemma":0.003013432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01503444,"about_ca_topic_score_gemma":0.02988644,"domain_scores_codex":[0.999007,0.0001615045,0.0003176285,0.0002371877,0.0001827527,0.00009391623],"domain_scores_gemma":[0.9969044,0.001107689,0.0006495587,0.0005803168,0.0005875532,0.0001703625],"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":[0.000374325,0.00002343673,0.004293968,0.009524486,0.0001552837,0.0001959906,0.00009846459,0.0004066013,0.0004727599,0.001903366,0.9494696,0.0330818],"study_design_scores_gemma":[0.0001438883,0.00002207921,0.009232075,0.003629292,0.0001015672,0.0004694962,0.0001312633,0.0002026205,0.0002891037,0.002407813,0.9833335,0.00003723638],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004646535,0.001312503,0.0003657685,0.0001498572,0.00008626772,0.00006746698,0.9934925,0.0003834506,0.003677484],"genre_scores_gemma":[0.001956189,0.001812871,0.001892405,0.0002923798,0.0000376018,0.0002720218,0.9917761,0.0001424899,0.001817906],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08990179,"threshold_uncertainty_score":0.3007514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0167244891487121,"score_gpt":0.3994232064072187,"score_spread":0.3826987172585066,"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."}}