{"id":"W4235148873","doi":"10.1515/iupac.78.0258","title":"Ecdysone Agonist","year":2016,"lang":"it","type":"dataset","venue":"IUPAC Standards Online","topic":"Neurology and Historical Studies","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Glossary; Relation (database); Computer science; Pesticide; Ecology; Biology; 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":[0.0007409895,0.001513154,0.001382793,0.002402614,0.0006066915,0.001950962,0.001959472,0.001666228,0.1008725],"category_scores_gemma":[0.005561461,0.0003736282,0.001111068,0.004350868,0.0003035305,0.001090281,0.001181299,0.001437578,0.08071204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001385389,"about_ca_system_score_gemma":0.001900756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01320214,"about_ca_topic_score_gemma":0.03372844,"domain_scores_codex":[0.9992141,0.0001260148,0.0001641212,0.0002714379,0.0001458374,0.00007856843],"domain_scores_gemma":[0.9980313,0.0007372124,0.0003613346,0.0003362783,0.0003836177,0.0001503171],"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":[0.0003255166,0.00003722431,0.001968754,0.002815649,0.00007646978,0.00006325357,0.00002730926,0.0002155405,0.0002108483,0.0005732554,0.9834914,0.01019486],"study_design_scores_gemma":[0.0003563179,0.00004658968,0.009491112,0.0009952976,0.00009488247,0.0002220025,0.00005879398,0.0001705209,0.0003641772,0.001141786,0.9870245,0.00003392722],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002070271,0.00037919,0.00005933128,0.00007846756,0.00001826294,0.0000209737,0.9977598,0.0001032275,0.001373682],"genre_scores_gemma":[0.00096391,0.0004639104,0.0003197211,0.0001904085,0.0000152617,0.0002432741,0.9962057,0.00003726192,0.001560536],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1008725,"threshold_uncertainty_score":0.3374522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02668365401116174,"score_gpt":0.3901846378848609,"score_spread":0.3635009838736992,"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."}}