{"id":"W2552083027","doi":"10.3390/s16111926","title":"Methods and Research for Multi-Component Cutting Force Sensing Devices and Approaches in Machining","year":2016,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Changsha Science and Technology Project; State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body; Natural Science Foundation of Hunan Province; National Natural Science Foundation of China","keywords":"Machinability; Machining; Component (thermodynamics); Tool wear; Machine tool; Cutting tool; Mechanical engineering; Numerical control; Field (mathematics); Engineering; Computer science","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":[0.001391071,0.0002915863,0.0007520008,0.000341515,0.0001445221,0.00007350172,0.00007771175,0.0002008053,5.640025e-7],"category_scores_gemma":[0.0003007121,0.0002318046,0.00006001575,0.0002024805,0.00005577188,0.00009904544,0.00008223762,0.0003991207,6.741581e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006966184,"about_ca_system_score_gemma":0.0000191171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007551192,"about_ca_topic_score_gemma":0.000009003414,"domain_scores_codex":[0.9984823,0.0002214716,0.0003975671,0.0004041324,0.0000986669,0.0003958503],"domain_scores_gemma":[0.9983588,0.001305154,0.00009275984,0.0001399672,0.00003524105,0.00006806528],"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":[0.000002206728,0.000002486453,0.000002745435,0.01305799,0.00002708609,0.000001676379,0.0003428637,0.005290183,0.000001999878,0.00007735975,8.684394e-7,0.9811925],"study_design_scores_gemma":[0.0003368636,0.00001797938,0.000004104791,0.01360234,0.0000740303,0.00003899397,0.0002703449,0.7675049,0.00001537484,0.0001451783,0.2175432,0.0004467297],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00008797513,0.8136827,0.1853746,0.000007155862,0.00007438781,0.0005296282,0.000009063335,0.00008436107,0.000150082],"genre_scores_gemma":[0.0001033821,0.6296268,0.3700556,0.000001648958,0.00005519353,0.00002056018,0.00001263334,0.00007663218,0.00004766806],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9807458,"threshold_uncertainty_score":0.9452717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2700676315064661,"score_gpt":0.4710140795094021,"score_spread":0.2009464480029359,"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."}}