{"id":"W4321253252","doi":"10.3390/machines11020297","title":"ConvLSTM-Att: An Attention-Based Composite Deep Neural Network for Tool Wear Prediction","year":2023,"lang":"en","type":"article","venue":"Machines","topic":"Lubricants and Their Additives","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Artificial neural network; Feature (linguistics); Pattern recognition (psychology); Feature extraction; Deep learning; Key (lock); Sequence (biology); Machine learning","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":[0.00008877444,0.0001180268,0.0001207792,0.00005240145,0.0001258347,0.00004636744,0.00008205794,0.00003673927,0.00005204496],"category_scores_gemma":[0.000009226112,0.000104857,0.00006613023,0.0001728013,0.00002071451,0.0001158244,0.00001116605,0.00006298551,0.00002855353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001391681,"about_ca_system_score_gemma":0.000003215489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001633028,"about_ca_topic_score_gemma":0.0000275629,"domain_scores_codex":[0.9994026,0.00002011665,0.0001364788,0.0001345787,0.00007904571,0.0002271956],"domain_scores_gemma":[0.999697,0.00008880684,0.00001874833,0.0001264786,0.00002215977,0.00004681215],"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":[0.00005104002,0.00003615316,0.0610521,0.0001255692,0.00009716944,0.000006971833,0.0001556341,0.9003877,0.005622616,0.0004375592,0.00633123,0.02569626],"study_design_scores_gemma":[0.0003370886,0.00004551609,0.1376316,0.00001354277,0.00001597149,0.000001238618,0.00001079455,0.8595755,0.0001021644,0.0002773608,0.001881644,0.0001076284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9763631,0.0000875049,0.02004882,0.0001331823,0.001153004,0.0003041953,0.0003708365,0.001204732,0.0003346382],"genre_scores_gemma":[0.9979148,0.000006094271,0.0005372377,0.00007373442,0.0006194467,0.00006778115,0.0006196098,0.00003937636,0.0001219301],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07657951,"threshold_uncertainty_score":0.4275942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00939127143012939,"score_gpt":0.2247686080899688,"score_spread":0.2153773366598394,"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."}}