{"id":"W4412552792","doi":"10.3390/jsan14040077","title":"FO-DEMST: Optimized Multi-Scale Transformer with Dual-Encoder Architecture for Feeding Amount Prediction in Sea Bass Aquaculture","year":2025,"lang":"en","type":"article","venue":"Journal of Sensor and Actuator Networks","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Agriculture","funders":"National Key Research and Development Program of China; Tianjin Agricultural University","keywords":"Computer science; Encoder; Transformer; Aquaculture; Fishery; Sea bass; Bass (fish); Architecture; Fish <Actinopterygii>; Electrical engineering; Biology; Operating system; Engineering","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":[0.0004036851,0.0001804916,0.0003049448,0.00009780745,0.0001185825,0.00006082086,0.0001364688,0.0002051647,0.00001387952],"category_scores_gemma":[0.00003536445,0.0001183305,0.0000799692,0.0002173649,0.0001378991,0.0002330699,0.00002581601,0.0005058019,5.400961e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001383009,"about_ca_system_score_gemma":0.00001043747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004244945,"about_ca_topic_score_gemma":0.00006671958,"domain_scores_codex":[0.9988843,0.00003870537,0.0003785586,0.0002034277,0.0002081334,0.0002868711],"domain_scores_gemma":[0.9995337,0.0001099987,0.0001566247,0.0001041581,0.00002537972,0.0000701223],"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":[0.003340256,0.0005592589,0.2893277,0.0002030744,0.0004317836,0.00007805121,0.005938411,0.5727315,0.07288537,0.00003082342,0.00907861,0.04539522],"study_design_scores_gemma":[0.03440863,0.002823787,0.5218855,0.003046696,0.001069773,0.0007642679,0.01049641,0.3144936,0.07652383,0.002442275,0.02991395,0.002131252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7980676,0.0001453194,0.1994629,0.001547841,0.0002535225,0.0003612168,0.00001213717,0.00004416895,0.0001053351],"genre_scores_gemma":[0.9292256,0.0001018041,0.07020359,0.00008975396,0.0001327797,0.00001267007,0.000002452186,0.00001464962,0.0002167193],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2582379,"threshold_uncertainty_score":0.4825377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01237102772750306,"score_gpt":0.2441152062597441,"score_spread":0.2317441785322411,"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."}}