{"id":"W6929678610","doi":"10.5061/dryad.5757d42","title":"Data from: Evidence for contemporary and historical gene flow between guppy populations in different watersheds, with a test for associations with adaptive traits","year":2019,"lang":"en","type":"dataset","venue":"Data Archiving and Networked Services (DANS)","topic":"Bacterial Infections and Vaccines","field":"Immunology and Microbiology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; University of British Columbia; McGill University","funders":"","keywords":"Gene flow; Biological dispersal; Gene; Flooding (psychology); Event (particle physics); Selection (genetic algorithm); Guppy; Negative selection; Variation (astronomy)","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":[0.001542008,0.001202785,0.001078258,0.002970928,0.0008611107,0.00179315,0.002129447,0.00135377,0.0259622],"category_scores_gemma":[0.006813876,0.0005827909,0.0006723089,0.005388819,0.0004386502,0.000890524,0.002023726,0.001648755,0.01939476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001964232,"about_ca_system_score_gemma":0.003016607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0762656,"about_ca_topic_score_gemma":0.118042,"domain_scores_codex":[0.999006,0.0001714405,0.0001422546,0.0003152976,0.0002073022,0.0001576599],"domain_scores_gemma":[0.9976133,0.0007432802,0.0004200076,0.0004697053,0.0005155177,0.0002381922],"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":[0.0002646865,0.00005003158,0.01606685,0.002287271,0.0001607568,0.0001203997,0.0002104653,0.000810036,0.000602869,0.0009986239,0.9727547,0.005673423],"study_design_scores_gemma":[0.0004347946,0.00002152644,0.04666934,0.0005393422,0.00007261543,0.00009860481,0.0002608939,0.0007333361,0.0005948861,0.001108787,0.9494199,0.00004607937],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007544857,0.00007195189,0.00006845091,0.00007395387,0.00001060847,0.000009600953,0.9983609,0.0001426569,0.0005074293],"genre_scores_gemma":[0.001538787,0.00004619582,0.0002466809,0.00003110979,0.000003257076,0.00006390497,0.9976273,0.00004993283,0.0003929436],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0762656,"threshold_uncertainty_score":0.1516434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2111353160023717,"score_gpt":0.3221002091255978,"score_spread":0.1109648931232261,"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."}}