{"id":"W4234440451","doi":"10.32920/ryerson.14645091","title":"Drilling Burr Formation With Tool Wear","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Drilling; Drill; Machining; Shearing (physics); Mechanical engineering; Enhanced Data Rates for GSM Evolution; Engineering; Thrust; Cutting tool; Geotechnical engineering","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":[0.0006358054,0.0008258731,0.0006969962,0.0005307604,0.0002583791,0.001242198,0.001345343,0.001433506,0.001068077],"category_scores_gemma":[0.001891227,0.0006162837,0.001317091,0.0003142515,0.0009237715,0.001497067,0.0009200848,0.0007661537,0.0002075702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005450165,"about_ca_system_score_gemma":0.0006029914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00483179,"about_ca_topic_score_gemma":0.002731932,"domain_scores_codex":[0.9995505,0.00007881512,0.000027481,0.0001233444,0.0001583235,0.00006144441],"domain_scores_gemma":[0.9989179,0.000410581,0.0003228977,0.0001539984,0.0001572868,0.00003723091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003882274,0.00004020225,0.001400716,0.0001238165,0.00001667401,0.0002198772,0.00013661,0.9750019,0.009192179,0.006280674,0.0001853999,0.00736308],"study_design_scores_gemma":[0.00001529507,0.00007349694,0.0006869448,0.00001093161,0.00001341461,0.0001415162,0.00002458029,0.9945371,0.001754285,0.001864962,0.0008628055,0.00001468882],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2876345,0.001910502,0.6906027,0.0003490925,0.0001463186,0.0002645612,0.0002609162,0.0005596658,0.01827176],"genre_scores_gemma":[0.9784137,0.0005415826,0.01489187,0.00003490384,0.0000146468,0.00007501255,0.00007441504,0.00003483773,0.005918993],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00483179,"threshold_uncertainty_score":0.009607315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006439948117775516,"score_gpt":0.1979511221722905,"score_spread":0.191511174054515,"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."}}