{"id":"W3151203259","doi":"","title":"Learning from Fish： Kinematics and Experimental Hydrodynamics for Roboticists","year":2006,"lang":"en","type":"article","venue":"国际自动化与计算杂志","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Kinematics; Biomechanics; Fish <Actinopterygii>; Engineering; Anatomy; Medicine; Biology; Physics; Fishery","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009617639,0.0003820904,0.0002557098,0.0002983613,0.0003853984,0.0007802931,0.0002930036,0.0005927936,0.00354693],"category_scores_gemma":[0.007380427,0.0003519013,0.0005192126,0.0002333745,0.001579571,0.001256993,0.0007350617,0.00065733,0.0004281439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001259349,"about_ca_system_score_gemma":0.001235543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008039894,"about_ca_topic_score_gemma":0.00971792,"domain_scores_codex":[0.9997178,0.0001036054,0.00001748818,0.00008029196,0.00005552821,0.00002522579],"domain_scores_gemma":[0.998736,0.0007802787,0.0001828875,0.0001137951,0.000123582,0.00006352275],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0009681269,0.0003668155,0.1556209,0.0005643739,0.0002833486,0.0002644881,0.005118259,0.3750024,0.03553471,0.0692765,0.004540149,0.35246],"study_design_scores_gemma":[0.0001660697,0.001115883,0.2492014,0.000264483,0.0001261455,0.0002615322,0.003387694,0.5144827,0.01827705,0.1991787,0.01324474,0.0002936979],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7819107,0.0006718744,0.1866133,0.002553505,0.0001216774,0.0001345498,0.0002397523,0.0004165017,0.02733812],"genre_scores_gemma":[0.972297,0.0003670362,0.02185829,0.0001217319,0.00002190611,0.00007709052,0.0001074438,0.00003926306,0.005110263],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008039894,"threshold_uncertainty_score":0.0159862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01085986726559297,"score_gpt":0.2076214568112468,"score_spread":0.1967615895456538,"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."}}