{"id":"W4320893877","doi":"10.1139/cjfas-2022-0091","title":"Using LASSO regularization to project recruitment under CMIP6 climate scenarios in a coastal fishery with spatial oceanographic gradients","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"New York Sea Grant, State University of New York; New York State Department of Environmental Conservation","keywords":"Climate change; Coupled model intercomparison project; Environmental science; Oceanography; Climatology; Spatial ecology; Fisheries management; Climate model; Fishery; Geology; Ecology; Fishing","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":[0.00705061,0.0004960478,0.000312871,0.0004247479,0.0005559426,0.0006385448,0.0006671562,0.0005454453,0.0007128413],"category_scores_gemma":[0.007621819,0.0002287894,0.0005009603,0.0005122166,0.0004581517,0.0005295428,0.0007505628,0.0008398304,0.0001231893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000686455,"about_ca_system_score_gemma":0.001464412,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01857965,"about_ca_topic_score_gemma":0.02547822,"domain_scores_codex":[0.9988207,0.0008192331,0.00004373381,0.0001363545,0.00007385336,0.0001061458],"domain_scores_gemma":[0.9978244,0.001410143,0.0003092391,0.0001415272,0.0002095282,0.0001051924],"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":[0.0004160369,0.0001791002,0.1103195,0.00005277349,0.0002292533,0.0002279277,0.0001957535,0.8230343,0.00226807,0.00406981,0.004032556,0.0549749],"study_design_scores_gemma":[0.00001493711,0.00003423478,0.00975715,0.000005919255,0.000007864326,0.00001516173,0.00006859626,0.987758,0.0003681657,0.0016802,0.0002800355,0.000009788113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8609957,0.0001123015,0.1350248,0.001380127,0.00004045701,0.00005320449,0.0004283073,0.0003933879,0.001571756],"genre_scores_gemma":[0.9511644,0.00004772348,0.04641188,0.0002071618,0.00002144192,0.00008998522,0.0007871105,0.00006304475,0.001207284],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9814203,"threshold_uncertainty_score":0.03728759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07556724990701771,"score_gpt":0.2854329857468246,"score_spread":0.2098657358398069,"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."}}