{"id":"W3005998138","doi":"10.1109/iotm.0001.1900033","title":"Precision Aquaculture","year":2019,"lang":"en","type":"article","venue":"IEEE Internet of Things Magazine","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":101,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Aquaculture; Standardization; Interoperability; Internet of Things; Work (physics); Computer science; Cloud computing; Business; Environmental resource management; Data science; Fish <Actinopterygii>; Engineering; Environmental science; Fishery; World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000244111,0.0001252045,0.000174291,0.00003639839,0.000009929188,0.00002096413,0.0006484902,0.0001101636,0.001145693],"category_scores_gemma":[0.00006783694,0.0000974719,0.00008134001,0.0001180998,0.0001081121,0.0003309192,0.0003705019,0.0001793615,0.005678104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008076924,"about_ca_system_score_gemma":0.000001836976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000259195,"about_ca_topic_score_gemma":0.000003456902,"domain_scores_codex":[0.9989579,0.0000235182,0.0002420875,0.0002685657,0.000328399,0.0001795348],"domain_scores_gemma":[0.9993864,0.00003876686,0.0001374184,0.0003954904,0.00001068103,0.00003125672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007261504,0.0001257597,0.1441495,0.00004934183,0.00003297554,0.000005663613,0.002026594,0.0001957792,0.7691921,0.000214219,0.07283039,0.01110513],"study_design_scores_gemma":[0.000465903,0.000377123,0.0540598,0.0001703147,0.00001571689,0.00001043383,0.00007396792,0.001046496,0.876324,0.004687772,0.06243499,0.0003334678],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9827622,0.00001499029,0.000439415,0.0002932897,0.0006163831,0.0001498753,0.000001332634,0.0001733674,0.0155491],"genre_scores_gemma":[0.9714901,0.000004528971,0.005296807,0.00007655622,0.00001921286,0.000003572561,0.000001733716,0.00001243414,0.02309505],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1071319,"threshold_uncertainty_score":0.9997674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01483367711597306,"score_gpt":0.2480073747284655,"score_spread":0.2331736976124925,"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."}}