{"id":"W4405580608","doi":"10.1016/j.biocon.2024.110927","title":"Introduction to the special issue: Leveraging genetics in spatial conservation prioritization","year":2024,"lang":"en","type":"article","venue":"Biological Conservation","topic":"Conservation, Biodiversity, and Resource Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Prioritization; Conservation genetics; Geography; Conservation biology; Evolutionary biology; Environmental resource management; Biology; Ecology; Genetics; Management science; Environmental science; Engineering","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005728447,0.0001289598,0.00009801525,0.00007977646,0.0002144777,0.0001301216,0.0001784463,0.00009287115,0.001996493],"category_scores_gemma":[0.0001619097,0.00009460995,0.00003749228,0.0006884201,0.0001035576,0.0001320197,0.0001857509,0.0001106263,0.0006510009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002480041,"about_ca_system_score_gemma":0.00001631463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000930121,"about_ca_topic_score_gemma":0.001645633,"domain_scores_codex":[0.9987164,0.0001464696,0.0002711206,0.0004252831,0.0002451681,0.0001955646],"domain_scores_gemma":[0.999626,0.0000837807,0.00004686315,0.0001747509,0.00002519815,0.00004334672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005590824,0.00004761757,0.7723515,0.00001693275,0.000007839419,0.000006484182,0.001036422,0.001600306,0.002044608,0.0001870558,0.1473174,0.07532791],"study_design_scores_gemma":[0.00007368428,0.00003687283,0.5076454,0.000007190979,0.000005743055,0.000001116144,0.0001129373,0.005814789,0.00007691783,0.0001693797,0.4859746,0.00008131571],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8937524,0.00005142878,0.005397294,0.09763609,0.001035462,0.0006196136,0.000006071161,0.00009351881,0.001408149],"genre_scores_gemma":[0.9838333,0.0001237737,0.0007989221,0.006887185,0.006186062,0.00004669058,0.0001735049,0.00001143282,0.001939116],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3386572,"threshold_uncertainty_score":0.9989158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02613767409890991,"score_gpt":0.2259266531471672,"score_spread":0.1997889790482573,"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."}}