{"id":"W6923004888","doi":"10.1371/journal.pone.0056204.t001","title":"Summary of genetic datasets&lt;sup&gt;1&lt;/sup&gt; used in a study of genetic connectivity measures.","year":2015,"lang":"en","type":"dataset","venue":"Figshare","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Genetic data; Marten; Genetic variation; Genetic distance; Genetic diversity; Genetic variability","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":[0.00404697,0.001549792,0.002048601,0.005237865,0.001829332,0.001981738,0.00286473,0.001209358,0.1333228],"category_scores_gemma":[0.01508852,0.0009420378,0.001066794,0.01357901,0.0006533783,0.001296651,0.001426444,0.002209871,0.05989536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001793537,"about_ca_system_score_gemma":0.004054898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07044733,"about_ca_topic_score_gemma":0.1132948,"domain_scores_codex":[0.9975661,0.0006253668,0.0003228312,0.0009368632,0.0003630742,0.0001857867],"domain_scores_gemma":[0.9924233,0.00252574,0.0006205878,0.001876691,0.001991427,0.0005621264],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004248898,0.0001422865,0.01758553,0.001885935,0.0002961274,0.0001365728,0.000518429,0.003610799,0.002118336,0.003399651,0.9539137,0.0159678],"study_design_scores_gemma":[0.001043924,0.000118944,0.05619804,0.0005592295,0.0003008659,0.0002496196,0.0005876232,0.001987065,0.0009412967,0.004848935,0.9330364,0.0001282268],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001286426,0.000057254,0.001247095,0.0000411036,0.0000248366,0.0001155764,0.9953958,0.0003767782,0.001455042],"genre_scores_gemma":[0.003366715,0.00007365668,0.005150738,0.00008407205,0.00001019124,0.0006696757,0.9890365,0.0004044825,0.001204012],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1333228,"threshold_uncertainty_score":0.4460091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1017656403913541,"score_gpt":0.3096680108292545,"score_spread":0.2079023704379004,"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."}}