{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":["open_science","insufficient_payload"],"category_scores_codex":[0.005796572,0.0006643989,0.001032542,0.0001965488,0.0003937833,0.0004757449,0.02030437,0.0004068826,0.00188004],"category_scores_gemma":[0.005829763,0.0006563135,0.00006787865,0.0002725885,0.0003183228,0.0012712,0.01063692,0.0005317131,0.002844089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003008513,"about_ca_system_score_gemma":0.0006622708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002566745,"about_ca_topic_score_gemma":0.0004203037,"domain_scores_codex":[0.9916028,0.0005867959,0.001166836,0.004694544,0.001116867,0.0008322013],"domain_scores_gemma":[0.9696686,0.0003883073,0.001342436,0.02816317,0.000157608,0.0002798292],"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":[0.00005302936,0.00004243454,0.000003322114,0.0006549279,0.00002066093,0.00001672449,0.00003605697,0.0000692748,0.05236078,0.00003312781,0.9465559,0.0001537186],"study_design_scores_gemma":[0.000410344,0.00005622469,0.00001800191,0.0001125585,0.0002687529,0.00003135115,0.000009204879,0.03387868,0.0003459671,0.00004773647,0.9641052,0.0007159334],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002463396,0.000661191,0.002479706,0.001385402,0.005958613,0.001074377,0.9877939,0.0003497126,0.00005080388],"genre_scores_gemma":[0.00002041067,0.0003435488,0.01860595,0.001774489,0.002206021,0.00007721515,0.9763727,0.00009227505,0.0005073947],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05201481,"threshold_uncertainty_score":0.9995888,"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."}}