{"id":"W4238400024","doi":"10.1504/ijhm.2019.098949","title":"Engine speed reduction for hydraulic machinery using predictive algorithms","year":2019,"lang":"en","type":"article","venue":"International Journal of Hydromechatronics","topic":"Hydraulic and Pneumatic Systems","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Reduction (mathematics); Excavator; Computer science; Truck; Electronic speed control; Automotive engineering; Control theory (sociology); Controller (irrigation); Algorithm; Specific speed; Simulation; Engineering; Mechanical engineering; Mathematics; Control (management); Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000757642,0.0006335612,0.000602647,0.0005944459,0.000342312,0.0007238721,0.0005652705,0.0004924095,0.001354554],"category_scores_gemma":[0.002391194,0.0002811602,0.0003511012,0.0004176849,0.0004015279,0.000500332,0.0003107005,0.0006939972,0.000177905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006104157,"about_ca_system_score_gemma":0.0007831081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005304647,"about_ca_topic_score_gemma":0.004070138,"domain_scores_codex":[0.9997041,0.00005884908,0.00001331839,0.00004605815,0.0001398861,0.00003779507],"domain_scores_gemma":[0.9990766,0.0006532247,0.0001048239,0.00003661717,0.0001145891,0.00001416848],"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":[0.00007583865,0.00003654331,0.0003238121,0.00007304386,0.00001323636,0.00001838529,0.000018984,0.9615988,0.002530405,0.001914477,0.000209993,0.03318651],"study_design_scores_gemma":[0.00001080586,0.00003924774,0.0001776352,0.000005273119,0.000007411807,0.000005385841,0.000004337578,0.9975338,0.001349524,0.0005912576,0.0002712382,0.000004140948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1128257,0.0009114217,0.8738004,0.0002057874,0.00007594669,0.00008786409,0.00004920937,0.0008765825,0.01116721],"genre_scores_gemma":[0.9585701,0.0002349226,0.03943038,0.00003248466,0.00002394312,0.00006323259,0.00004004242,0.00004181257,0.001563051],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005304647,"threshold_uncertainty_score":0.01054752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01164198753353411,"score_gpt":0.2497040481721678,"score_spread":0.2380620606386337,"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."}}