{"id":"W4239494471","doi":"10.1515/iupac.78.0438","title":"Necrosis","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Dermatological and COVID-19 studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Glossary; Pesticide; Relation (database); Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001970318,0.0004401237,0.001099765,0.0001402948,0.0001249167,0.00002738203,0.0002276522,0.0004009047,0.004140912],"category_scores_gemma":[0.000891228,0.000249913,0.000301916,0.0001710191,0.00026228,0.00003726926,0.0002495912,0.0004456498,0.00001868002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002540987,"about_ca_system_score_gemma":0.0005127743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006523852,"about_ca_topic_score_gemma":0.0001128505,"domain_scores_codex":[0.9975559,0.00004236177,0.00045427,0.0005065215,0.0009837019,0.0004572546],"domain_scores_gemma":[0.998326,0.0001305648,0.000168269,0.0006991207,0.0004338493,0.0002421651],"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":[0.0003194736,0.0002984422,0.00009343659,0.0003671845,0.0002368632,0.000403138,0.000005260111,2.169615e-8,0.000007473917,0.000006255457,0.9941514,0.004111028],"study_design_scores_gemma":[0.001543493,0.0003719523,0.0002701019,0.0006445518,0.0003858533,0.00009701976,0.00001513894,4.047746e-7,0.0000198085,0.0002491651,0.9960871,0.0003154102],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009802527,0.001798805,0.00003427973,0.009457003,0.000509694,0.0003103786,0.9873259,0.0001199666,0.0003459142],"genre_scores_gemma":[0.00003358797,0.003205191,0.0000344388,0.005296649,0.0007724037,0.00002940933,0.9881216,0.00002596875,0.002480812],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.004160354,"threshold_uncertainty_score":0.9999953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0247447107349806,"score_gpt":0.430276007598243,"score_spread":0.4055312968632624,"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."}}