{"id":"W3090095336","doi":"10.1109/access.2020.3028544","title":"A Prototype System for Real-Time Monitoring of Arctic Char in Indoor Aquaculture Operations: Possibilities &amp; Challenges","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Aquaculture; Data collection; Trajectory; Scale (ratio); Work (physics); Software; Environmental science; Real-time computing; Marine engineering; Fish <Actinopterygii>; Simulation; Fishery; Statistics; Engineering; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002105691,0.0004458427,0.0004525535,0.0005248137,0.0005275312,0.0008506979,0.001388238,0.0006500129,0.003454884],"category_scores_gemma":[0.002594558,0.0002120606,0.0002924884,0.0004434583,0.0004128991,0.001072977,0.0005293326,0.0004696887,0.0007946607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006450336,"about_ca_system_score_gemma":0.001301418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005790155,"about_ca_topic_score_gemma":0.008641922,"domain_scores_codex":[0.9992148,0.0002142016,0.00003929105,0.0001957791,0.0002702996,0.00006568701],"domain_scores_gemma":[0.9978295,0.0005997102,0.0001104248,0.0003102077,0.0009691499,0.0001809746],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001409847,0.001222402,0.05251979,0.001409972,0.0001352735,0.001055724,0.002590875,0.01425679,0.3878801,0.001996964,0.01281123,0.522711],"study_design_scores_gemma":[0.0005759681,0.01040374,0.167473,0.0006979098,0.000495941,0.002293868,0.006371474,0.4028573,0.3024121,0.003204105,0.1027571,0.0004574473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3686571,0.0003169356,0.6043289,0.0009611203,0.0003854083,0.002254627,0.001454606,0.01398389,0.007657347],"genre_scores_gemma":[0.6246486,0.0001641923,0.3675874,0.0003424497,0.00006415231,0.001026791,0.0009794227,0.0002350868,0.00495192],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005790155,"threshold_uncertainty_score":0.01155776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1086903470801198,"score_gpt":0.3285442894095977,"score_spread":0.2198539423294779,"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."}}