{"id":"W2918373935","doi":"10.1007/s10980-019-00789-9","title":"Considering landscape connectivity and gene flow in the Anthropocene using complementary landscape genetics and habitat modelling approaches","year":2019,"lang":"en","type":"article","venue":"Landscape Ecology","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":44,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Vedecká Grantová Agentúra MŠVVaŠ SR a SAV","keywords":"Landscape connectivity; Metapopulation; Landscape ecology; Ecology; Biodiversity; Population; Landscape epidemiology; Geography; Endangered species; Habitat fragmentation; Gene flow; Habitat; Resistance (ecology); Umbrella species; Biology; Genetic diversity; Biological dispersal","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005319871,0.0004337099,0.0006312263,0.0009303293,0.0006968463,0.001344748,0.0009439245,0.001339948,0.002119768],"category_scores_gemma":[0.002699893,0.0004270865,0.0008980471,0.0009168145,0.0007183726,0.001644318,0.0008981387,0.0006758448,0.00009507496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009926632,"about_ca_system_score_gemma":0.001010373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03138626,"about_ca_topic_score_gemma":0.0333798,"domain_scores_codex":[0.9998569,0.00007956909,0.000004758695,0.00002647369,0.000008451555,0.00002376726],"domain_scores_gemma":[0.9991527,0.0006175633,0.00009486581,0.00002805293,0.00003403688,0.00007284791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00001926805,0.00003064657,0.008919642,0.00002673556,0.0001076846,0.0002245473,0.0001148745,0.9656408,0.0003764073,0.01968063,0.0001916626,0.004667107],"study_design_scores_gemma":[0.000009787494,0.00001475319,0.002351157,0.000007957283,0.00002986081,0.00005078492,0.00009843954,0.9833027,0.00004298005,0.01374998,0.0003334704,0.000008192433],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7986008,0.0005636765,0.1934814,0.001476311,0.00004831559,0.00002444152,0.0001405279,0.00006850639,0.005595911],"genre_scores_gemma":[0.9826363,0.0003105366,0.01531346,0.0001125602,0.00005355232,0.0000312697,0.00004585862,0.00003240489,0.001464145],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03138626,"threshold_uncertainty_score":0.06240714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03818858119848564,"score_gpt":0.2383030922721514,"score_spread":0.2001145110736657,"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."}}