{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004029294,0.00123898,0.001533177,0.02445785,0.0008564158,0.002683717,0.002419312,0.001477409,0.04919492],"category_scores_gemma":[0.02406373,0.0006329402,0.000975015,0.02441145,0.0004624953,0.004304784,0.003477971,0.0008472137,0.01668433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001983378,"about_ca_system_score_gemma":0.006917042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03259686,"about_ca_topic_score_gemma":0.05557081,"domain_scores_codex":[0.9980197,0.0002636753,0.0007321459,0.0002834197,0.0005567557,0.000144315],"domain_scores_gemma":[0.984047,0.007670799,0.002627697,0.001569363,0.003219377,0.0008657313],"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":[0.0002452635,0.00002997813,0.009167393,0.01546191,0.0002596313,0.000162849,0.0007101632,0.0008591472,0.0007554999,0.006509439,0.8596883,0.1061504],"study_design_scores_gemma":[0.00006602282,0.0000213828,0.01691118,0.002350875,0.0001394585,0.00009985286,0.0001848113,0.0003016278,0.0003233835,0.001925004,0.9775873,0.00008909839],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001722888,0.003235821,0.001561666,0.0006152304,0.00008933639,0.0002295483,0.9762198,0.002034596,0.01429105],"genre_scores_gemma":[0.01111407,0.005780868,0.01688475,0.0008377203,0.0001308629,0.001614679,0.95914,0.0008456972,0.003651413],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04919492,"threshold_uncertainty_score":0.1645734,"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."}}