{"id":"W4232258419","doi":"10.1515/iupac.79.0894","title":"Benefit","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; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009911589,0.0009457204,0.001157783,0.0005853759,0.0001621691,0.0001198919,0.001233946,0.0007782148,0.02536906],"category_scores_gemma":[0.00100158,0.0007120121,0.0003641195,0.0004277643,0.0002856327,0.000179814,0.000464083,0.0008362508,0.0004397512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00163182,"about_ca_system_score_gemma":0.001267054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002366844,"about_ca_topic_score_gemma":0.003751849,"domain_scores_codex":[0.9942711,0.00009595992,0.0007896661,0.001040769,0.002798795,0.001003699],"domain_scores_gemma":[0.9953291,0.00014623,0.0005845223,0.00241914,0.001099306,0.0004216856],"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":[0.0003098638,0.0002779973,0.000004735805,0.0001095389,0.0002192994,0.0001647485,0.000004139143,9.614117e-7,0.00003008305,0.00002568284,0.9964387,0.002414215],"study_design_scores_gemma":[0.001563647,0.0002059947,0.00002421341,0.000701525,0.0002673306,0.00003839204,0.000006684033,0.000001103783,0.00003102874,0.0004989316,0.995711,0.000950172],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004677209,0.00107976,0.00001773716,0.0003661763,0.001258919,0.0004545877,0.9962544,0.0003553033,0.000166343],"genre_scores_gemma":[0.000002463421,0.0005587852,0.00005166623,0.0002651159,0.002550396,0.000028672,0.9945738,0.0002882571,0.001680815],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02492931,"threshold_uncertainty_score":0.9995331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01613022108435774,"score_gpt":0.4123548776291683,"score_spread":0.3962246565448106,"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."}}