{"id":"W7055769007","doi":"","title":"Data mining and machine learning for reverse engineering","year":2019,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Thermal properties of materials","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Reverse engineering; Executable; Process (computing); Scalability; Focus (optics); Relevance (law); Binary number","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002083225,0.0007046956,0.0008927186,0.0001943802,0.0006862135,0.0002724949,0.001341942,0.0005522592,0.0007030356],"category_scores_gemma":[0.002473509,0.0007080649,0.0001135789,0.00009698488,0.00003673949,0.001340341,0.0006427077,0.0005571945,0.0003984561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001884536,"about_ca_system_score_gemma":0.00003929391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002017555,"about_ca_topic_score_gemma":0.0001255916,"domain_scores_codex":[0.9963209,0.0002152261,0.0008189079,0.001402441,0.0004878819,0.000754662],"domain_scores_gemma":[0.9974416,0.0003832746,0.0005920705,0.001183973,0.0001866408,0.0002124162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003956763,0.00002813277,0.000006881717,0.001277623,0.00005998799,0.00001177295,0.00001355467,0.0001802592,0.9800847,0.0006964012,0.00001187604,0.01723315],"study_design_scores_gemma":[0.001888407,0.000322708,0.0001566634,0.001611355,0.0004067039,0.00004709541,0.0004318477,0.001278698,0.7507353,0.00017071,0.2408301,0.00212034],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9880227,0.0006991658,7.770125e-7,0.00001107103,0.0028522,0.0009512902,0.004811391,0.0003511689,0.002300243],"genre_scores_gemma":[0.9756428,0.0001786689,0.007889867,0.0001170957,0.0001320642,0.0001236096,0.004128028,0.0004001797,0.01138766],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2408182,"threshold_uncertainty_score":0.9995371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04030899130542254,"score_gpt":0.2482131097731572,"score_spread":0.2079041184677347,"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."}}