{"id":"W2139256913","doi":"10.1109/ical.2008.4636254","title":"An automated industrial fish cutting machine: Control, fault diagnosis and remote monitoring","year":2008,"lang":"en","type":"article","venue":"","topic":"Industrial Technology and Control Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Automation; Fault (geology); Remote monitoring and control; Factory (object-oriented programming); Remote control; Control system; Fuzzy logic; Hydraulic machinery; Real-time computing; Computer science; Embedded system; Control engineering; Engineering; Control (management); Computer hardware; Artificial intelligence; Mechanical engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000258891,0.0003572367,0.0003489095,0.0003195342,0.0003198076,0.0003483156,0.0006478472,0.0006228999,0.001266388],"category_scores_gemma":[0.0005365394,0.0001545653,0.0001262162,0.0002128835,0.0003531314,0.0003162864,0.0002142278,0.0002910691,0.000263408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003250761,"about_ca_system_score_gemma":0.0004853078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005537346,"about_ca_topic_score_gemma":0.004328141,"domain_scores_codex":[0.9996637,0.00002885523,0.000009093792,0.00006139653,0.0002110861,0.00002592785],"domain_scores_gemma":[0.9997239,0.0000779834,0.00003906448,0.00004042123,0.0001011217,0.00001742838],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005912608,0.0001507788,0.005524387,0.0003738627,0.00002439963,0.0006605557,0.0002894726,0.03536033,0.4758442,0.00141011,0.00145447,0.4783161],"study_design_scores_gemma":[0.0003277194,0.002847169,0.05168789,0.0001054573,0.0001190462,0.003928385,0.0002563671,0.5411667,0.3690283,0.002549365,0.02786254,0.0001211941],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4526367,0.001019955,0.533815,0.0002842226,0.0000788551,0.0003846221,0.0001034447,0.004621109,0.007056165],"genre_scores_gemma":[0.92243,0.0001780185,0.07348668,0.00005043135,0.00002509463,0.00008912795,0.00005482724,0.00002730952,0.003658515],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005537346,"threshold_uncertainty_score":0.01101029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02395098621186866,"score_gpt":0.2454375731111514,"score_spread":0.2214865868992827,"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."}}