{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003584341,0.0005347951,0.0004451637,0.0009366762,0.0005018399,0.000669293,0.000848713,0.0005511679,0.00484759],"category_scores_gemma":[0.000458888,0.0002543516,0.0002927422,0.0005913461,0.000154662,0.001036693,0.0008706171,0.0003296307,0.001425801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003509508,"about_ca_system_score_gemma":0.0004376184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001762553,"about_ca_topic_score_gemma":0.001609262,"domain_scores_codex":[0.9994879,0.000038876,0.00005929782,0.0001552059,0.0002068466,0.00005188311],"domain_scores_gemma":[0.9996537,0.0000481603,0.00005129988,0.000061481,0.0001473763,0.00003803553],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001546976,0.0008466373,0.06298447,0.0009070592,0.0002920687,0.002655925,0.00115,0.01703357,0.3212496,0.004565678,0.06293573,0.5238322],"study_design_scores_gemma":[0.0004216551,0.001966559,0.09871618,0.0003260094,0.0005442139,0.003422196,0.001124821,0.5123655,0.2085254,0.004750995,0.167358,0.0004784535],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3113869,0.001157611,0.5599117,0.00119372,0.0008645189,0.001235814,0.004143016,0.0462762,0.07383053],"genre_scores_gemma":[0.9177577,0.0005328694,0.05713269,0.000546049,0.0001294332,0.0006167679,0.002531552,0.0002235211,0.02052934],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00484759,"threshold_uncertainty_score":0.01621681,"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."}}