{"id":"W2758907932","doi":"10.1139/cjfas-2017-0140","title":"Seabird diets as bioindicators of Atlantic herring recruitment and stock size: a new tool for ecosystem-based fisheries management","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Pew Charitable Trusts","keywords":"Herring; Clupea; Seabird; Fishery; Stock assessment; Groundfish; Bioindicator; Sterna; Clupeidae; Bycatch; Atlantic herring; Geography; Fisheries management; Ecology; Predation; Fishing; Biology; Fish <Actinopterygii>","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.001556091,0.000457164,0.0003272849,0.0007516659,0.0002519272,0.000969442,0.0004087741,0.0002607009,0.0009906202],"category_scores_gemma":[0.002912527,0.0001993769,0.0002852372,0.0003277483,0.0002739156,0.0006979656,0.0004069927,0.0002809959,0.0001383427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004772468,"about_ca_system_score_gemma":0.000542954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01967178,"about_ca_topic_score_gemma":0.044597,"domain_scores_codex":[0.9996786,0.0001861359,0.00001562506,0.00006411097,0.00003704971,0.00001845389],"domain_scores_gemma":[0.9986401,0.0006314437,0.0003418133,0.0000954495,0.0001449268,0.0001463744],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005064454,0.00003298792,0.9844433,0.00001981201,0.0001609715,0.00002743107,0.0000742702,0.004237316,0.001205127,0.0001301194,0.00009352253,0.009524454],"study_design_scores_gemma":[0.000009121726,0.000156364,0.9409313,0.00002752369,0.0001119118,0.00006897957,0.000252032,0.05671315,0.0007114408,0.0003778931,0.0006215504,0.00001875623],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973583,0.0002470185,0.001835822,0.0001173374,0.000003907109,0.000005153087,0.0001349021,0.00002893733,0.000268648],"genre_scores_gemma":[0.9972766,0.0001269317,0.002289774,0.00001981675,0.000005940984,0.000004510469,0.00009570777,0.000004781014,0.0001758718],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01967178,"threshold_uncertainty_score":0.03911453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04781690221273444,"score_gpt":0.276968253655657,"score_spread":0.2291513514429226,"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."}}