{"id":"W4416682517","doi":"10.3897/biss.9.180293","title":"The Canadian Genomic Adaptation and Resilience to Climate Change (GenARCC) Project","year":2025,"lang":"","type":"article","venue":"Biodiversity Information Science and Standards","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada; Agriculture and Agri-Food Canada","funders":"Government of Canada","keywords":"Climate change; Biodiversity; Resilience (materials science); Adaptation (eye); Climate resilience; Psychological resilience; Ecosystem services; Leverage (statistics); Ecosystem","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.005748736,0.001304278,0.0007768521,0.005092894,0.005502079,0.005225901,0.002660252,0.0008855817,0.01395193],"category_scores_gemma":[0.01362642,0.0005351951,0.001328029,0.01282439,0.001494904,0.001792849,0.005461714,0.002353072,0.003634616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04994274,"about_ca_system_score_gemma":0.1410919,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9834388,"about_ca_topic_score_gemma":0.9877211,"domain_scores_codex":[0.9953164,0.0004712258,0.0001462985,0.0008891238,0.002336739,0.0008402535],"domain_scores_gemma":[0.9860643,0.001186261,0.0005938077,0.001637156,0.008109967,0.002408401],"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":[0.0003367755,0.00006653294,0.04202007,0.0006341892,0.0001971065,0.0001457635,0.001548809,0.004697482,0.00163559,0.02830857,0.8269033,0.09350592],"study_design_scores_gemma":[0.00006989768,0.00002904347,0.06025738,0.000421914,0.0001092701,0.00005750805,0.00144994,0.004818124,0.00177948,0.006225897,0.9246386,0.000142933],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.01556903,0.001048761,0.01501018,0.007421885,0.0003692847,0.0006193633,0.9110311,0.003878961,0.04505135],"genre_scores_gemma":[0.05596024,0.002283236,0.07080931,0.002237214,0.00009711947,0.001121816,0.8437904,0.001596563,0.02210421],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.04994274,"threshold_uncertainty_score":0.3623616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02483432817027196,"score_gpt":0.2471483838941274,"score_spread":0.2223140557238554,"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."}}