{"id":"W3148639695","doi":"10.21979/n9/lphcus","title":"Replication Data for: GenSlice: Generalized Semantic History Slicing","year":2020,"lang":"en","type":"dataset","venue":"DR-NTU (Data)","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of British Columbia","funders":"","keywords":"Slicing; Replication (statistics); Computer science; Programming language; World Wide Web; Biology; Virology","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":[0.002296099,0.002505232,0.001260919,0.001733646,0.001206267,0.001751401,0.003874162,0.002219195,0.07758382],"category_scores_gemma":[0.009498294,0.0007601155,0.00210274,0.002411847,0.0007499537,0.001693519,0.002054361,0.002245759,0.08802404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001364003,"about_ca_system_score_gemma":0.002514455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01391321,"about_ca_topic_score_gemma":0.02962354,"domain_scores_codex":[0.9983032,0.0003423624,0.00015045,0.0005439465,0.0004701843,0.0001898592],"domain_scores_gemma":[0.9943996,0.001174537,0.0002801853,0.002741971,0.001095288,0.0003082799],"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":[0.000134176,0.00005169331,0.0008941233,0.0003688252,0.00003310259,0.00001725943,0.00002260184,0.0004862396,0.0003969075,0.0006083716,0.9933578,0.003628836],"study_design_scores_gemma":[0.0006671682,0.00008159389,0.006892007,0.0002014574,0.00008299222,0.0001425824,0.0001536175,0.002914284,0.004065844,0.007381661,0.9773114,0.0001053331],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0008246186,0.0001270442,0.0009576687,0.0001627545,0.0001327658,0.00006329978,0.9917986,0.004021391,0.001911908],"genre_scores_gemma":[0.001775462,0.00004278176,0.001983395,0.0001052132,0.00001706373,0.000241805,0.9941542,0.0005145657,0.001165497],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07758382,"threshold_uncertainty_score":0.2595437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1381343507278149,"score_gpt":0.3619414059775036,"score_spread":0.2238070552496888,"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."}}