{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007680301,0.002222491,0.001004097,0.003370421,0.000989115,0.002761445,0.002239278,0.001369963,0.1299073],"category_scores_gemma":[0.009180706,0.000404489,0.00131455,0.004129765,0.0005287568,0.002211847,0.00267825,0.001584387,0.1832797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001139982,"about_ca_system_score_gemma":0.001544457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009734523,"about_ca_topic_score_gemma":0.02195572,"domain_scores_codex":[0.998696,0.0001974572,0.0002666648,0.0003955356,0.00029033,0.0001539129],"domain_scores_gemma":[0.9973547,0.0006473452,0.0002750524,0.0008116125,0.0007206501,0.0001907494],"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":[0.0001717391,0.00003183691,0.002299173,0.0008223033,0.00002035034,0.00007735768,0.00005608202,0.000176297,0.0001165376,0.0007914253,0.9791117,0.01632529],"study_design_scores_gemma":[0.0001203473,0.00004167824,0.008897695,0.0005286484,0.00002318569,0.0004195447,0.0002325355,0.0005766671,0.0003888819,0.002971615,0.9857582,0.00004088213],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00116262,0.000316706,0.0004099594,0.000200381,0.0001480744,0.00008091849,0.9867998,0.00143219,0.009449293],"genre_scores_gemma":[0.002320705,0.0002143941,0.0008192647,0.0001914357,0.00002692983,0.0002841951,0.9911315,0.0001488654,0.004862722],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1299073,"threshold_uncertainty_score":0,"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."}}