{"id":"W1518834645","doi":"10.1155/2015/509143","title":"Triaxial Accelerometer Error Coefficients Identification with a Novel Artificial Fish Swarm Algorithm","year":2015,"lang":"en","type":"article","venue":"Journal of Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"China Scholarship Council; Ministry of Science and Technology of the People's Republic of China; Queen's University; National Natural Science Foundation of China","keywords":"Accelerometer; Swarm behaviour; Identification (biology); Algorithm; Computer science; Artificial neural network; Fish <Actinopterygii>; Control theory (sociology); Engineering; Artificial intelligence; Ecology","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":[0.0003506072,0.000132571,0.0002190887,0.0001981668,0.0000402561,0.00007383877,0.0001189841,0.00008273652,0.00001296048],"category_scores_gemma":[0.00008176127,0.0001066293,0.00007713252,0.0003018038,0.00003075408,0.0002754643,0.000007360037,0.0002160372,0.00002152859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001585987,"about_ca_system_score_gemma":0.00004682744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001069063,"about_ca_topic_score_gemma":0.000007225368,"domain_scores_codex":[0.9986851,0.00002475926,0.0005260954,0.0000942603,0.0004801448,0.0001896398],"domain_scores_gemma":[0.999131,0.00003481468,0.0002028327,0.0001080068,0.0003701085,0.0001532451],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001221542,0.000479359,0.0002367232,0.00006886639,0.0003970058,0.0001577898,0.00421902,0.7039446,0.2396362,0.00004747178,0.009780481,0.03981098],"study_design_scores_gemma":[0.009975293,0.001561141,0.008402358,0.0002938185,0.0004720151,0.001195017,0.002286449,0.4575535,0.503123,0.0002029274,0.01373953,0.001194953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9540899,0.00001668495,0.04425858,0.0001098091,0.001188337,0.0001088373,0.00002121928,0.00004519068,0.0001614056],"genre_scores_gemma":[0.9964046,0.000005567874,0.002678221,0.00002781594,0.0007533056,0.000001265468,0.000009171215,0.00003090548,0.00008908864],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2634868,"threshold_uncertainty_score":0.4348216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04102097867946244,"score_gpt":0.2616644239935751,"score_spread":0.2206434453141126,"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."}}