{"id":"W4403777594","doi":"10.1111/geb.13917","title":"<scp>FreshLanDiv</scp>: A Global Database of Freshwater Biodiversity Across Different Land Uses","year":2024,"lang":"en","type":"article","venue":"Global Ecology and Biogeography","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières; Carleton University; McGill University; Wilfrid Laurier University","funders":"National Research, Development and Innovation Office; Deutsches Zentrum für integrative Biodiversitätsforschung Halle-Jena-Leipzig; Nemzeti Kutatási Fejlesztési és Innovációs Hivatal; Fundação de Amparo à Pesquisa do Estado do Amazonas; China Scholarship Council; Deutsche Forschungsgemeinschaft; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Biodiversa+","keywords":"Macrophyte; Biodiversity; Abundance (ecology); Geography; Ecology; Taxon; Wetland; Land use; Metadata; Database; Freshwater fish; Fish <Actinopterygii>; Biology; Fishery","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":[0.001021713,0.0009919372,0.0009264824,0.01983471,0.0004804863,0.002179113,0.001243865,0.0006852206,0.03914237],"category_scores_gemma":[0.00589875,0.000500756,0.0003937004,0.04032579,0.0004127798,0.001729167,0.002112035,0.0007060425,0.01260538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008799709,"about_ca_system_score_gemma":0.002961673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01787893,"about_ca_topic_score_gemma":0.01897318,"domain_scores_codex":[0.9991205,0.00009864891,0.0003025043,0.0001754012,0.0002106568,0.00009223817],"domain_scores_gemma":[0.9918585,0.001840683,0.002124529,0.0009641801,0.002051875,0.001160124],"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":[0.0005191973,0.000106433,0.08030934,0.01014277,0.0004576465,0.0004855652,0.0007189244,0.001732292,0.005892637,0.004531249,0.8011014,0.09400256],"study_design_scores_gemma":[0.0001600708,0.00005663136,0.2242474,0.001112925,0.0001472356,0.0003407881,0.0005336918,0.0006482761,0.002289584,0.001521085,0.7688591,0.00008328392],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002620633,0.0001500891,0.0003112541,0.00004742523,0.000006462973,0.00004846472,0.9949373,0.0003591549,0.001519233],"genre_scores_gemma":[0.005725042,0.0002104526,0.00185455,0.00005024003,0.00001230742,0.0001951249,0.9911696,0.0001678473,0.0006148805],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03914237,"threshold_uncertainty_score":0.1309443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007462701074500915,"score_gpt":0.2224312691446385,"score_spread":0.2149685680701376,"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."}}