{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003151779,0.000915165,0.0007246583,0.0007492008,0.0004389546,0.001838906,0.001146207,0.00106547,0.002303527],"category_scores_gemma":[0.0114399,0.0006892078,0.0009561267,0.0007403895,0.0004041344,0.002685326,0.001014326,0.00187024,0.0006326031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006584342,"about_ca_system_score_gemma":0.001182495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02336456,"about_ca_topic_score_gemma":0.02780744,"domain_scores_codex":[0.9996482,0.0001299556,0.00004247783,0.0001060179,0.00004545638,0.0000279305],"domain_scores_gemma":[0.9962436,0.001832751,0.0005229489,0.0006943517,0.000532545,0.0001738888],"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":[0.00007719387,0.00005058492,0.02783906,0.00007680293,0.0001826978,0.00004185423,0.00008180323,0.9391678,0.00182816,0.002655628,0.001267451,0.02673098],"study_design_scores_gemma":[0.00002651815,0.00004130516,0.007410621,0.00002842677,0.00004236497,0.00002079195,0.00005516325,0.9844537,0.0007328267,0.005181503,0.001969455,0.00003724326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4477677,0.0007866311,0.5289529,0.002264011,0.0004881572,0.0001668315,0.004237832,0.003501539,0.01183446],"genre_scores_gemma":[0.900028,0.0004976817,0.0947329,0.0003696332,0.0001583438,0.00009143381,0.001933128,0.0004060835,0.001782651],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02336456,"threshold_uncertainty_score":0.04645717,"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."}}