{"id":"W3121458554","doi":"10.1101/2021.01.23.427792","title":"Design and deployment of an affordable and long-lasting deep-water subsurface fish aggregation device","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"Natural Environment Research Council; Sight Research UK","keywords":"Pelagic zone; Software deployment; Exploit; Fish <Actinopterygii>; Fishery; Environmental science; Aggregate (composite); Environmental resource management; Ecology; Computer science; Biology","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":[0.000453114,0.0005061105,0.0002515921,0.0005341674,0.0002518026,0.0004706379,0.001028281,0.0006369162,0.00205309],"category_scores_gemma":[0.0006338212,0.000198154,0.0002434451,0.0001755908,0.0001961666,0.0003963906,0.000799757,0.000279047,0.0006364554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004158547,"about_ca_system_score_gemma":0.0004919701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001602641,"about_ca_topic_score_gemma":0.001805935,"domain_scores_codex":[0.9996539,0.00004389662,0.00003689887,0.00009339611,0.0000962944,0.00007557247],"domain_scores_gemma":[0.9995339,0.00004412579,0.0001068685,0.00006659054,0.0001549105,0.00009368426],"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":[0.0006920415,0.0002815,0.05205244,0.0009426245,0.0001039589,0.002056123,0.0006410922,0.02317741,0.6302758,0.00484256,0.009352176,0.2755822],"study_design_scores_gemma":[0.0004854916,0.0129016,0.1319426,0.0003542224,0.0004891712,0.004457892,0.001866369,0.2212002,0.4229904,0.001997975,0.2010171,0.0002970125],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5639962,0.0005248981,0.419442,0.0006992177,0.0005369748,0.000801756,0.001166397,0.00201236,0.01082023],"genre_scores_gemma":[0.8932756,0.0001369682,0.1018751,0.0001175836,0.00002701352,0.0002767873,0.000309887,0.0000298908,0.003951146],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00205309,"threshold_uncertainty_score":0.006868243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01833315045148126,"score_gpt":0.2199611725605491,"score_spread":0.2016280221090679,"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."}}