{"id":"W4393617247","doi":"10.5281/zenodo.6581962","title":"A Probabilistic Framework for Mutation Testing in Deep Neural Networks - Models archive Part 3","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Probabilistic logic; Computer science; Mutation; Artificial neural network; Deep neural networks; Artificial intelligence; Machine learning; Computational biology; Genetics; Biology; Gene","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","sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007443081,0.000276398,0.0002812069,0.0003614174,0.001714723,0.001594795,0.003092379,0.0001391361,0.003826221],"category_scores_gemma":[0.002013586,0.0003024854,0.00006775554,0.001266225,0.0001050241,0.000496756,0.002487672,0.0009201281,0.0001325657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001973623,"about_ca_system_score_gemma":0.00001801262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003356986,"about_ca_topic_score_gemma":0.000003261082,"domain_scores_codex":[0.9971604,0.0004026053,0.0004775813,0.0008969311,0.0004514788,0.0006109858],"domain_scores_gemma":[0.9980032,0.0002909379,0.0003285534,0.000833141,0.0003806955,0.0001634744],"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":[0.00003841607,0.0001672816,1.81846e-7,0.0003619172,0.0000208871,0.00006170115,0.0004383221,0.05864158,0.000001095592,0.02396072,0.8495486,0.06675924],"study_design_scores_gemma":[0.0001928161,0.0002754744,0.000007445078,0.00007471714,0.00001139865,0.00008471841,0.00003331008,0.4533282,3.199948e-7,0.035389,0.5103548,0.0002477376],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.000006713183,0.00007279286,0.7681035,0.0002492809,0.0002238827,0.0009106892,0.229453,0.0003532115,0.0006269072],"genre_scores_gemma":[0.002412739,0.00005218075,0.01784193,0.0002966262,0.0002640311,0.000002822853,0.9783514,0.0007534456,0.00002480304],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.7502615,"threshold_uncertainty_score":0.9999427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04875708313633573,"score_gpt":0.2552288401572365,"score_spread":0.2064717570209008,"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."}}