{"id":"W2594881023","doi":"10.1139/cjfas-2016-0256","title":"Modeling the drift of European (<i>Anguilla anguilla</i>) and American (<i>Anguilla rostrata</i>) eel larvae during the year of spawning","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of Naval Research; U.S. Navy; Bundesministerium für Ernährung und Landwirtschaft; Deutsche Forschungsgemeinschaft","keywords":"Anguilla rostrata; Sargasso sea; Anguillidae; Fishery; Oceanography; Larva; Subtropics; Current (fluid); Biology; Geography; Ecology; Geology; Fish <Actinopterygii>","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002736703,0.0003402368,0.0002173338,0.0002841241,0.0003185114,0.0004748878,0.0003201069,0.0004518942,0.0005155585],"category_scores_gemma":[0.0004610577,0.0002685194,0.0006550087,0.0002720716,0.0001484142,0.0002510755,0.0001945453,0.0002443833,0.00008591696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008506824,"about_ca_system_score_gemma":0.001032226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1773891,"about_ca_topic_score_gemma":0.1391732,"domain_scores_codex":[0.999949,0.00001195368,0.000004914476,0.00001532538,0.000005637291,0.0000132036],"domain_scores_gemma":[0.9998626,0.00006072835,0.00002852805,0.000006296975,0.0000237673,0.00001814318],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005730662,0.00002947741,0.03731172,0.000009385915,0.00005003156,0.00003506162,0.00003588901,0.9584059,0.001417553,0.000208404,0.00008089078,0.002358439],"study_design_scores_gemma":[0.00001149675,0.00003311051,0.01043148,0.00000205513,0.00001933971,0.00001170904,0.00002398653,0.9890547,0.0002601357,0.00006013812,0.00008507485,0.000006772956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976732,0.00002489973,0.001754974,0.00002346802,0.000003645569,0.000004729984,0.0001468481,0.00001980428,0.0003483596],"genre_scores_gemma":[0.996429,0.00004852549,0.002523459,0.000007061868,0.000002995023,0.0000119026,0.0003319197,0.000008137134,0.0006370206],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1773891,"threshold_uncertainty_score":0.3527132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01817203930102858,"score_gpt":0.2304929320558168,"score_spread":0.2123208927547882,"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."}}