{"id":"W2793138710","doi":"10.21535/ijrm.v4i1.972","title":"Numerical and Experimental Investigation of Thermal Damage in Drilling of CFRP Composites","year":2017,"lang":"en","type":"article","venue":"International Journal of Robotics and Mechatronics","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Materials science; Composite material; Drilling; Machining; Thermal; Carbon fiber reinforced polymer; Finite element method; Anisotropy; Aerospace; Thermal conduction; Fibre-reinforced plastic; Structural engineering; Composite number; Engineering; Metallurgy","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":[],"consensus_categories":[],"category_scores_codex":[0.00007995248,0.00004738682,0.0001088836,0.00005916081,0.00001906924,0.00002185571,0.0001017742,0.00002504157,0.000001637203],"category_scores_gemma":[0.00002128002,0.00004443886,0.00001725249,0.00001190291,0.00003796993,0.0001968042,0.00002677615,0.00007514508,2.609513e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001719963,"about_ca_system_score_gemma":0.00001245514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004084838,"about_ca_topic_score_gemma":0.000001396658,"domain_scores_codex":[0.9995921,0.000004887317,0.0002161068,0.00003494699,0.0001086026,0.00004338151],"domain_scores_gemma":[0.9996244,0.00002310837,0.000204252,0.00003855794,0.00008561602,0.00002409007],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002089504,0.00001151179,0.006921721,0.00001943475,0.0000268748,0.000001908519,0.0003923928,0.9562508,0.03241483,0.003041926,9.803625e-7,0.0008967597],"study_design_scores_gemma":[0.0009410632,0.0001286514,0.006600605,0.0002286025,0.00001499195,0.00001953784,0.0001815046,0.8846236,0.1058111,0.001344519,0.00001625004,0.00008957442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9206565,0.0009877234,0.07800775,0.00008064919,0.0001889045,0.00002069697,0.000002082489,0.000002472443,0.00005323532],"genre_scores_gemma":[0.9672691,0.0004027456,0.03228451,0.000004833088,0.00002962233,9.268492e-8,0.00000165016,0.000006094696,0.000001408267],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0733963,"threshold_uncertainty_score":0.1812164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009810811255651244,"score_gpt":0.2602952837609856,"score_spread":0.2504844725053343,"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."}}