{"id":"W4236214015","doi":"10.21203/rs.3.rs-15717/v2","title":"Variation in breeding practices and geographic isolation drive subpopulation differentiation, contributing to the loss of genetic diversity within dog breed lineages","year":2020,"lang":"en","type":"preprint","venue":"Research Square","topic":"Human-Animal Interaction Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Breed; Isolation (microbiology); Diversity (politics); Biology; Genetic diversity; Variation (astronomy); Evolutionary biology; Genetic variation; Reproductive isolation; Zoology; Genetics; Sociology; Demography; Gene; Anthropology; Population; Bioinformatics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006045472,0.0001474264,0.0002479931,0.000560088,0.000388048,0.0005895037,0.0003183645,0.0002889597,0.001403946],"category_scores_gemma":[0.001031517,0.0001426711,0.0002499765,0.0005353969,0.0003952954,0.0002798121,0.0004628526,0.0003389815,0.0001597778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002371821,"about_ca_system_score_gemma":0.0002451099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001519998,"about_ca_topic_score_gemma":0.002940082,"domain_scores_codex":[0.9995157,0.0001550729,0.00003410866,0.0001706839,0.00007542875,0.00004911581],"domain_scores_gemma":[0.9991259,0.0002778039,0.0003146605,0.0001077888,0.00008711878,0.0000867386],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000314067,0.00008441833,0.8955659,0.00007852567,0.000366891,0.0003026806,0.001351751,0.0006389166,0.08589254,0.0007329616,0.0002910807,0.01438014],"study_design_scores_gemma":[0.000004713514,0.00004210346,0.9965022,0.00001157435,0.00005605201,0.0001375533,0.0002466204,0.00086945,0.001168946,0.0003063234,0.000647997,0.000006466629],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981523,0.0001772446,0.00113886,0.00002972715,0.000003404656,0.000004423024,0.0001044557,0.00001029413,0.0003791721],"genre_scores_gemma":[0.9989994,0.0000474771,0.000601526,0.00002440358,0.000004135748,0.000005837208,0.0001396701,0.000005852459,0.0001716015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001519998,"threshold_uncertainty_score":0.004696727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06721189320890218,"score_gpt":0.4107984415401591,"score_spread":0.3435865483312569,"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."}}