{"id":"W4255401600","doi":"10.1515/iupac.87.0061","title":"Apraxia","year":2016,"lang":"it","type":"dataset","venue":"IUPAC Standards Online","topic":"Syntax, Semantics, Linguistic Variation","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Relation (database); Psychology; Computer science; Chemistry; Linguistics; Philosophy; Data mining; Organic chemistry","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":[0.001286928,0.001095325,0.001383494,0.0004882521,0.000858132,0.0008183579,0.0009352996,0.000666866,0.06402804],"category_scores_gemma":[0.00398835,0.000893414,0.0004405224,0.0000981374,0.0006585582,0.000221832,0.0003741552,0.0008412017,0.0001661043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009112637,"about_ca_system_score_gemma":0.001303684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002085889,"about_ca_topic_score_gemma":0.007364703,"domain_scores_codex":[0.9938667,0.000249372,0.001495167,0.001138736,0.002204138,0.001045909],"domain_scores_gemma":[0.9940686,0.0004936507,0.001115003,0.001666738,0.002320941,0.0003350606],"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":[0.0002449862,0.000522792,0.00001624278,0.0004601896,0.0005104147,0.0001335907,0.001983766,9.766969e-7,0.000004051114,0.05644979,0.9369498,0.002723367],"study_design_scores_gemma":[0.001675504,0.0004372052,0.00003632534,0.00151935,0.0007534336,0.0000156899,0.00114401,0.00004372028,0.000009078293,0.02437391,0.9688301,0.001161664],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002078457,0.0004885055,0.0005640704,0.0196451,0.01159971,0.0006368939,0.9650043,0.000176775,0.001676773],"genre_scores_gemma":[0.001927285,0.001012398,0.0001010875,0.0005765061,0.02637823,0.00002221643,0.9600732,0.0001795488,0.009729513],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06386194,"threshold_uncertainty_score":0.9993517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02329317334355051,"score_gpt":0.3524316902869467,"score_spread":0.3291385169433961,"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."}}