{"id":"W2930808060","doi":"10.1038/s41597-019-0024-7","title":"Geo-referenced population-specific microsatellite data across American continents, the MacroPopGen Database","year":2019,"lang":"en","type":"article","venue":"Scientific Data","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg; Golder Associates (Canada); BGC Engineering (Canada); Concordia University","funders":"Concordia University; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Biology; Fish migration; Microsatellite; Biodiversity; Genetic diversity; Population; Ecology; Intraspecific competition; Zoology; Allele; Demography; Genetics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002032132,0.0003399698,0.0004514673,0.005949183,0.0003707579,0.00128089,0.0007889381,0.000235434,0.008403929],"category_scores_gemma":[0.00536596,0.0003558913,0.0001793121,0.01129196,0.0002029335,0.0007337938,0.001216386,0.000456887,0.002557059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002876848,"about_ca_system_score_gemma":0.001356501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009253222,"about_ca_topic_score_gemma":0.01230032,"domain_scores_codex":[0.9992579,0.0001610276,0.0001459276,0.0002292281,0.0001553268,0.00005062572],"domain_scores_gemma":[0.9959417,0.0008759247,0.001183636,0.0007668749,0.0007331286,0.0004986945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001575162,0.0001593539,0.5897497,0.002037471,0.0009829666,0.0005104566,0.001134396,0.002990585,0.006186806,0.005477066,0.2288156,0.1603805],"study_design_scores_gemma":[0.0002317623,0.00007906641,0.7306705,0.0004913357,0.0003633633,0.0005644775,0.0008757018,0.004802689,0.002440508,0.003083048,0.2563467,0.00005085669],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1178499,0.0006104119,0.007451069,0.0001813756,0.00002471501,0.0001331344,0.8665323,0.001032549,0.006184598],"genre_scores_gemma":[0.1133019,0.0004698618,0.01757813,0.0001031281,0.00002389164,0.0006593907,0.8664314,0.0002655158,0.0011668],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.009253222,"threshold_uncertainty_score":0.02811396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05393613729136078,"score_gpt":0.3173182112775451,"score_spread":0.2633820739861844,"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."}}