{"id":"W2970955126","doi":"10.1016/j.jmarsys.2019.103229","title":"Variability of Pacific herring (Clupea pallasii) spawn abundance under climate change off the West Coast of Canada over the past six decades","year":2019,"lang":"en","type":"article","venue":"Journal of Marine Systems","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Fisheries and Oceans Canada","funders":"Fisheries and Oceans Canada","keywords":"Spawn (biology); Herring; Pacific herring; Oceanography; Upwelling; Clupea; Fishery; Ocean gyre; Pacific decadal oscillation; Environmental science; Geography; Sea surface temperature; Geology; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[],"category_scores_codex":[0.002409114,0.0001499187,0.0003857542,0.00002982504,0.0001110155,0.00004586078,0.0006630381,0.00005382949,0.001774102],"category_scores_gemma":[0.00007706559,0.00008119999,0.000105212,0.0002623588,0.0002131685,0.0001726893,0.0006307319,0.0003560727,0.000006434483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002493639,"about_ca_system_score_gemma":0.00009779128,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2054421,"about_ca_topic_score_gemma":0.184602,"domain_scores_codex":[0.9974643,0.0003205109,0.0007183903,0.0001626673,0.0009843145,0.0003497897],"domain_scores_gemma":[0.9982566,0.0003816046,0.0006907132,0.0004953148,0.00007495919,0.0001008506],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007703889,0.00004892646,0.9955744,0.0001778405,0.00004215047,0.000005830505,0.0002229244,0.0007696824,0.000278461,0.0002676138,0.0006653995,0.001869756],"study_design_scores_gemma":[0.0003633768,0.0001373046,0.9630682,0.00006489213,0.0000172091,0.00006539365,0.001686608,0.00104722,0.00002929412,0.00004195959,0.03337325,0.0001052223],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9398652,0.00004577629,0.00002085197,0.0006547311,0.0005284391,0.0003933249,0.00001531497,0.000002815838,0.05847349],"genre_scores_gemma":[0.9988077,0.000141505,0.00001645873,0.00004189743,0.0001958228,0.000006545909,9.468401e-7,0.00001462274,0.0007744567],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05894249,"threshold_uncertainty_score":0.9991384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01431914256660138,"score_gpt":0.2264000380492244,"score_spread":0.212080895482623,"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."}}