{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01298809,0.002050559,0.001809408,0.007725387,0.001708733,0.007212918,0.002929052,0.002764262,0.004556823],"category_scores_gemma":[0.05063513,0.001105725,0.002886527,0.007610737,0.003184787,0.00826533,0.003793679,0.007798373,0.003587587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002188182,"about_ca_system_score_gemma":0.004175059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002631404,"about_ca_topic_score_gemma":0.003562692,"domain_scores_codex":[0.9905668,0.003776253,0.001047565,0.00201537,0.002394436,0.0001996147],"domain_scores_gemma":[0.9391934,0.04507465,0.002441321,0.008361886,0.004377475,0.0005513321],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001499452,0.0004603404,0.01034807,0.002016347,0.0005378412,0.0003764911,0.0008244036,0.03024132,0.002939662,0.1438358,0.05083431,0.7574355],"study_design_scores_gemma":[0.00006602275,0.0001374726,0.003242609,0.0009019134,0.0001228198,0.0003608632,0.0007278128,0.25066,0.007534917,0.6401557,0.09593771,0.0001522214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005353387,0.007096689,0.9591964,0.01496788,0.0004785267,0.0004880384,0.002619603,0.004176865,0.005622599],"genre_scores_gemma":[0.0566064,0.00576604,0.9273175,0.001842042,0.0005957079,0.0008419527,0.0038682,0.0004088372,0.002753294],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01298809,"threshold_uncertainty_score":0.06868839,"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."}}