{"id":"W4286210642","doi":"10.1111/ecog.06189","title":"Climate‐informed models benefit hindcasting but present challenges when forecasting species–habitat associations","year":2022,"lang":"en","type":"article","venue":"Ecography","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"National Oceanic and Atmospheric Administration; Joint Institute for the Study of the Atmosphere and Ocean; University of Washington","keywords":"Hindcast; Environmental science; Groundfish; Climatology; Climate change; Computer science; Ecology; Econometrics; Fisheries management; Biology; Machine learning; Mathematics; Fishing","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005147818,0.000156301,0.000173659,0.0001354069,0.0009471964,0.00008233056,0.0004309319,0.00004362386,0.0104553],"category_scores_gemma":[0.00006069271,0.0001641968,0.0001621689,0.0004190643,0.00009207933,0.0004552311,0.001481587,0.000337084,0.00004260508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000220343,"about_ca_system_score_gemma":0.00001313976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004884151,"about_ca_topic_score_gemma":0.0008185823,"domain_scores_codex":[0.9978489,0.00005706427,0.000294901,0.0003555076,0.0007742772,0.0006693528],"domain_scores_gemma":[0.9992263,0.0002044574,0.0001329791,0.0002833411,0.00001745302,0.00013541],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004623092,0.0002928007,0.8045443,0.00005340901,0.00009401765,0.00003269553,0.006320085,0.01082551,0.00006451031,0.002529794,0.008428473,0.1667682],"study_design_scores_gemma":[0.001379198,0.0007096424,0.3780016,0.00001739521,0.00005422724,0.00004012272,0.01215012,0.1328985,0.00007177675,0.03199998,0.4414049,0.001272544],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2034006,0.00008103169,0.00005334667,0.001097599,0.0001457816,0.0003369418,0.0001200992,0.00008741763,0.7946772],"genre_scores_gemma":[0.991317,0.0004695108,0.00263429,0.0001880284,0.0001766852,0.000334646,0.0001292609,0.00004597454,0.004704561],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7899727,"threshold_uncertainty_score":0.9904492,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08290488927063043,"score_gpt":0.2464501087868782,"score_spread":0.1635452195162478,"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."}}