{"id":"W6889280807","doi":"10.25592/dgs.corpus-2.0-type-86455","title":"Tokenliste für das Type TORONTO1^","year":2019,"lang":"en","type":"dataset","venue":"Universität Hamburg","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Akademie der Wissenschaften in Hamburg","keywords":"Type (biology); Term (time); Terminal (telecommunication); Sequence (biology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002325248,0.0007562939,0.0008214267,0.0005849167,0.0002012138,0.0001087104,0.001739601,0.0008152175,0.01519896],"category_scores_gemma":[0.0000875581,0.0008560513,0.0002999356,0.0005705524,0.0001730098,0.001299709,0.0008413249,0.0008436877,0.1289974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001589482,"about_ca_system_score_gemma":0.0005655687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002735995,"about_ca_topic_score_gemma":0.001177954,"domain_scores_codex":[0.9968623,0.0001666961,0.0003206327,0.0009996978,0.0008284168,0.0008222898],"domain_scores_gemma":[0.9963519,0.0001163905,0.0004385706,0.002431818,0.0003271807,0.0003341596],"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":[0.0002425068,0.00009878554,0.0000148908,0.0001151311,0.0003180394,0.0005629295,0.00005792876,0.00003262018,0.00006603899,0.0001131616,0.9983219,0.00005605639],"study_design_scores_gemma":[0.000955652,0.000169213,0.00004597822,0.0001446702,0.0006233804,0.00003020213,0.0002162475,0.0000129474,0.00001352526,0.00001393354,0.9968089,0.0009653566],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009012744,0.0006416869,0.000004071278,0.00004065444,0.002738637,0.0005180974,0.9789066,0.0002131112,0.01684707],"genre_scores_gemma":[0.0000520676,0.0002533477,0.00005532765,0.0002249132,0.0004776768,0.000001008981,0.9584332,0.0001321522,0.04037027],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1137984,"threshold_uncertainty_score":0.9993891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02399164582062321,"score_gpt":0.3059059163691413,"score_spread":0.2819142705485181,"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."}}