{"id":"W4313594739","doi":"10.1016/j.biocon.2022.109883","title":"The application gap: Genomics for biodiversity and ecosystem service management","year":2023,"lang":"en","type":"article","venue":"Biological Conservation","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Horizon 2020; Fonds de recherche du Québec – Nature et technologies; Fundação para a Ciência e a Tecnologia; H2020 Marie Skłodowska-Curie Actions; Centro Singular de Investigación de Galicia; Xunta de Galicia; European Regional Development Fund; European Commission; Gutenberg Forschungskolleg; Javna Agencija za Raziskovalno Dejavnost RS; Generalitat Valenciana; Ministerio de Ciencia e Innovación; European Cooperation in Science and Technology","keywords":"Biodiversity; Ecosystem services; Environmental resource management; Business; Genomics; Environmental planning; Ecosystem; Ecology; Biology; Geography; Genome; Economics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003228014,0.00007043476,0.0000579936,0.000009388684,0.0007176012,0.00001979597,0.0001415074,0.00004649823,0.00001102205],"category_scores_gemma":[0.00001988007,0.00004932722,0.00002051991,0.0001396308,0.00008835278,0.000046427,0.0003823612,0.00003030466,0.0005900879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001157004,"about_ca_system_score_gemma":5.200175e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005472032,"about_ca_topic_score_gemma":0.0001383343,"domain_scores_codex":[0.9994391,0.00003515236,0.0000903593,0.0002201407,0.00007633133,0.000138871],"domain_scores_gemma":[0.9996223,0.0001850627,0.00005152216,0.0001092128,0.000004910645,0.00002703517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005456039,0.00001889023,0.9695837,0.00002090735,0.00002630116,7.766772e-7,0.0001257123,0.00005953165,0.003877866,0.0005506604,0.01347897,0.01220214],"study_design_scores_gemma":[0.0001559437,0.00002998646,0.7893665,0.000001105321,0.000009309142,3.616132e-7,0.0004919757,0.001323516,0.0001066511,0.0004864483,0.2079569,0.00007134918],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944305,0.00001242219,0.0003934542,0.003947328,0.00004576483,0.0006264173,0.0000561836,0.00006979714,0.0004180618],"genre_scores_gemma":[0.9950117,0.00118071,0.001983988,0.001407494,0.00001668067,0.0001357126,0.0001338819,0.000003133007,0.0001267328],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1944779,"threshold_uncertainty_score":0.7584583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05297404862478753,"score_gpt":0.2245489782368269,"score_spread":0.1715749296120394,"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."}}