{"id":"W4256308008","doi":"10.1515/iupac.88.1301","title":"Rostral","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Glossary; Terminology; Relation (database); Computer science; Linguistics; Philosophy; 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":[0.001141786,0.0009021712,0.001195489,0.0003839592,0.0004364114,0.0004419519,0.002011259,0.0008078874,0.0074444],"category_scores_gemma":[0.002050709,0.0008455079,0.0003939101,0.0001510241,0.0005046911,0.0002542776,0.000509617,0.001486867,0.0003385967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009865655,"about_ca_system_score_gemma":0.00230244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007159635,"about_ca_topic_score_gemma":0.007663013,"domain_scores_codex":[0.9947631,0.0001292579,0.0006435569,0.0009520871,0.002537546,0.0009744451],"domain_scores_gemma":[0.9941202,0.00006016769,0.0009093655,0.003684134,0.00081267,0.0004134509],"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":[0.0002958176,0.0003121873,0.00001293905,0.0001374591,0.0001932391,0.0004573376,0.00000580595,0.000002581923,0.00001305863,0.000002695874,0.9978961,0.0006707556],"study_design_scores_gemma":[0.001341601,0.0002175353,0.0001811818,0.0004005657,0.0003402821,0.00006009558,0.000009396815,0.000004878194,0.00001466136,0.00009105021,0.9964379,0.0009008607],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009875724,0.000682237,0.000005049716,0.0001803492,0.001729229,0.0004498651,0.9964331,0.0002570706,0.0001643273],"genre_scores_gemma":[0.00001514936,0.0002019879,0.00007341804,0.0001163637,0.002495212,0.00001828643,0.9956924,0.0002294282,0.00115777],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.007105803,"threshold_uncertainty_score":0.9993995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02566975484346257,"score_gpt":0.4717222152897979,"score_spread":0.4460524604463353,"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."}}