{"id":"W4391266490","doi":"10.1109/icscan58655.2023.10395013","title":"Fish Farm Monitoring System Using IoT","year":2023,"lang":"en","type":"article","venue":"","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Aquaculture; Fish farming; Fish <Actinopterygii>; Productivity; Environmental science; Profitability index; Computer science; Schedule; Agriculture; Internet of Things; Quality (philosophy); Water quality; Agricultural engineering; Business; Fishery; Engineering; Embedded system; Ecology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002486918,0.00009692281,0.00009596637,0.00005003725,0.0001333293,0.00004221417,0.0003134618,0.00007159413,0.00004127375],"category_scores_gemma":[0.00003154357,0.00008616966,0.00003144745,0.0004578237,0.00007039781,0.00006100387,0.0005517617,0.00008624813,0.001194198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003265622,"about_ca_system_score_gemma":0.000001967882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009725635,"about_ca_topic_score_gemma":0.000009367905,"domain_scores_codex":[0.9990225,0.00002290854,0.0001471747,0.0002378959,0.0002605377,0.0003090282],"domain_scores_gemma":[0.9995714,0.00003074563,0.00003175052,0.0003232936,0.000003004424,0.00003974381],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000004918214,0.0000250526,0.793874,0.00006887979,0.000020249,0.00007980876,0.0006029662,0.009991939,0.1794067,0.0004248834,0.001828596,0.01367204],"study_design_scores_gemma":[0.0002838849,0.00004517827,0.2813994,0.0001284063,0.00001811206,0.00002320073,0.006034762,0.01191465,0.6915833,0.0005169685,0.007463149,0.0005890146],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931362,0.00000196713,0.0001839194,0.0001623605,0.0006389929,0.00008400373,0.000001567977,0.002497247,0.003293801],"genre_scores_gemma":[0.9935362,0.00000184152,0.005176622,0.000006820751,0.00009542644,0.000008980314,5.237796e-7,0.00001459336,0.001159028],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5124745,"threshold_uncertainty_score":0.9995835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08023780456830183,"score_gpt":0.2920104218522843,"score_spread":0.2117726172839825,"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."}}