{"id":"W2120390620","doi":"10.3389/fgene.2015.00275","title":"How spatio-temporal habitat connectivity affects amphibian genetic structure","year":2015,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Wildlife-Road Interactions and Conservation","field":"Environmental Science","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"U.S. Forest Service; Wyoming Space Grant Consortium; U.S. Geological Survey; Colorado State University; National Aeronautics and Space Administration","keywords":"Biological dispersal; Metapopulation; Ecology; Wetland; Abiotic component; Population; Habitat; Biology; Genetic structure; Genetic variation","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":[],"consensus_categories":[],"category_scores_codex":[0.0000863489,0.0001583356,0.0001575871,0.00006754829,0.00006254937,0.00008782213,0.000188744,0.0001064629,0.00007253764],"category_scores_gemma":[0.0000730867,0.0001608399,0.00003797743,0.0002713837,0.0001235479,0.0002041389,0.0000894346,0.0001470598,0.00002211148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003166562,"about_ca_system_score_gemma":0.00003250403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001576331,"about_ca_topic_score_gemma":0.003668089,"domain_scores_codex":[0.9989179,0.00009956783,0.0001361916,0.0003143465,0.0002760183,0.0002559574],"domain_scores_gemma":[0.9994361,0.00001599257,0.00009066091,0.0002989754,0.00001702512,0.0001412767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001846332,0.00002931441,0.9064258,0.00000340817,0.000006318295,0.00000638928,0.0002010907,0.0040321,0.0003366778,0.000003790215,0.07844595,0.01049073],"study_design_scores_gemma":[0.0005912394,0.0001556814,0.9218822,0.00001112906,0.00001451898,0.00001130786,0.0004407408,0.02417024,0.001300726,0.002148368,0.04898284,0.0002909783],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9848315,0.0001933089,0.01185518,0.0008751506,0.001321903,0.000274746,0.00001311543,0.000028111,0.0006070152],"genre_scores_gemma":[0.9517149,0.00001516098,0.04742289,0.0002470493,0.00008781895,0.00001288277,0.00002434693,0.00001832036,0.0004566545],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03556772,"threshold_uncertainty_score":0.6558862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01193456406379473,"score_gpt":0.2111421281245564,"score_spread":0.1992075640607616,"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."}}