{"id":"W4391215695","doi":"10.1101/2024.01.22.576731","title":"Genetic network analysis uncovers spatial variation in diversity and connectivity of a species presenting a continuous distribution","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Government of Manitoba; Government of Saskatchewan; Government of Northwest Territories; Alberta Environment and Protected Areas; Government of Alberta; Trent University; Environment and Climate Change Canada","funders":"Environment and Climate Change Canada; Trent University","keywords":"Diversity (politics); Variation (astronomy); Geography; Evolutionary biology; Genetic diversity; Distribution (mathematics); Ecology; Economic geography; Sociology; Biology; Anthropology; Mathematics; Demography; Astronomy; Physics","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":[0.0004748724,0.0001728156,0.000244041,0.003204962,0.0004349258,0.0006191125,0.0002569577,0.0002725522,0.000709235],"category_scores_gemma":[0.002454,0.0001058896,0.0002795974,0.002315665,0.0004042774,0.0005203657,0.000501702,0.0002998195,0.00009803058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005246456,"about_ca_system_score_gemma":0.0003423754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0162044,"about_ca_topic_score_gemma":0.02650226,"domain_scores_codex":[0.999703,0.00008052534,0.0000150393,0.0001265931,0.00003652813,0.00003830394],"domain_scores_gemma":[0.9985813,0.0006581032,0.0003684,0.00009789623,0.0001780257,0.0001162181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001711107,0.00006155124,0.9301463,0.000119556,0.0003401621,0.000277915,0.0007575117,0.02477016,0.01053557,0.00227269,0.0007992514,0.02974824],"study_design_scores_gemma":[0.00001472072,0.00007704087,0.840377,0.00003390498,0.0001497991,0.0003919729,0.001029907,0.1498924,0.001122271,0.005427217,0.001456149,0.00002762548],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9927139,0.0001194616,0.006192048,0.00005019834,0.000003168303,0.000005914795,0.0004239015,0.00003298118,0.0004584898],"genre_scores_gemma":[0.9960919,0.00005427507,0.003230024,0.000007176834,0.000003157194,0.000005874002,0.0005130902,0.00000450301,0.00008991441],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0162044,"threshold_uncertainty_score":0.03222013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008221577587324067,"score_gpt":0.1957707469379406,"score_spread":0.1875491693506166,"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."}}