{"id":"W4399039132","doi":"10.1109/access.2024.3406262","title":"Unlocking Dual Utility: 1D-CNN for Milling Tool Health Assessment and Experimental Optimization","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"National Research Council Canada","keywords":"Bottleneck; Computer science; Robustness (evolution); Dual (grammatical number); Convolutional neural network; Artificial intelligence; Data mining; Machine learning; Precision and recall; Raw data; Embedded system","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001252957,0.0001285948,0.0001326657,0.00007196205,0.0001321488,0.0002890761,0.00006787539,0.0000432414,0.0000180082],"category_scores_gemma":[0.000006303227,0.0001321511,0.00002563981,0.0001417835,0.00001291919,0.0006209465,0.00002434391,0.0000907592,4.177161e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009191134,"about_ca_system_score_gemma":0.00004186977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009232464,"about_ca_topic_score_gemma":0.000002528668,"domain_scores_codex":[0.9992769,0.000008331084,0.0002044136,0.0002232191,0.00009407751,0.0001929961],"domain_scores_gemma":[0.9997606,0.00005716683,0.00002596828,0.000084583,0.00002602876,0.00004564037],"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":[0.000005279177,0.000009115697,0.00004531012,0.0003547097,0.00001754082,0.000001144175,0.0002409386,0.9772093,0.000155591,0.0002352708,0.0001962465,0.02152957],"study_design_scores_gemma":[0.0002002778,0.00004164014,0.00002295724,0.0001105292,0.000008934887,0.000003542055,0.00007153469,0.9936632,0.00429109,0.0001539625,0.001286756,0.0001456005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01680017,0.003395488,0.977865,0.00008814549,0.0008837245,0.0002963869,0.00001663674,0.0003988925,0.0002554891],"genre_scores_gemma":[0.9393297,0.000363439,0.0598801,0.00008170137,0.0001609287,0.000072349,0.00005012234,0.00004479628,0.00001687384],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9225295,"threshold_uncertainty_score":0.5388966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03453877935168125,"score_gpt":0.3733047644817774,"score_spread":0.3387659851300961,"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."}}