{"id":"W3017300021","doi":"10.1038/s41597-020-0465-z","title":"FiCli, the Fish and Climate Change Database, informs climate adaptation and management for freshwater fishes","year":2020,"lang":"en","type":"article","venue":"Scientific Data","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Natural Resources and Forestry","funders":"","keywords":"Climate change; Adaptation (eye); Environmental resource management; Geography; Climate change adaptation; Fish <Actinopterygii>; Effects of global warming; Database; Freshwater fish; Ecology; Global warming; Fishery; Environmental science; Biology; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.0007799104,0.0001026674,0.00008898325,0.00002076372,0.0008046275,0.000214209,0.0005059384,0.00002033284,0.0002140365],"category_scores_gemma":[0.0000432352,0.00006932089,0.00001064054,0.0001441649,0.0004419627,0.0009852882,0.004087463,0.00004536465,0.00008373364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008037446,"about_ca_system_score_gemma":9.91449e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002012235,"about_ca_topic_score_gemma":0.007297258,"domain_scores_codex":[0.9988231,0.00001622425,0.0001455832,0.0005574941,0.0001550728,0.0003025001],"domain_scores_gemma":[0.9993121,0.00004331119,0.00005948414,0.0005252444,0.000004827267,0.000055043],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000345068,0.0000200714,0.02059738,0.0001908931,0.0000260584,0.00000315093,0.001392121,0.000007771709,0.00001258618,0.0005662006,0.9620616,0.0150877],"study_design_scores_gemma":[0.000392078,0.0000406485,0.1088556,0.0000155101,0.00006711105,8.174536e-7,0.001336561,0.02136801,0.00001244765,0.0001614085,0.8676053,0.0001445295],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5231405,0.0006011868,0.004518999,0.3131646,0.005700861,0.01761064,0.09312569,0.000731731,0.04140576],"genre_scores_gemma":[0.7706281,0.01750897,0.04410005,0.1182458,0.0006558733,0.002250395,0.04221755,0.0001098352,0.004283403],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2474875,"threshold_uncertainty_score":0.6188626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0835278061003806,"score_gpt":0.2554216991084943,"score_spread":0.1718938930081137,"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."}}