{"id":"W3045566827","doi":"10.7717/peerj.9518","title":"Mediterranean swordfish ( <i>Xiphias gladius</i> Linnaeus, 1758) population structure revealed by microsatellite DNA: genetic diversity masked by population mixing in shared areas","year":2020,"lang":"en","type":"article","venue":"PeerJ","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"","keywords":"Swordfish; Mediterranean sea; Population; Mediterranean Basin; Mediterranean climate; Genetic structure; Biology; Ecology; Fishery; Geography; Genetic diversity; Zoology; Tuna; Demography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009434109,0.0003072899,0.0003108875,0.00006095349,0.000246428,0.00007360425,0.0003533021,0.0003940904,0.0001546921],"category_scores_gemma":[0.00008890649,0.0003490605,0.0001171592,0.0002592875,0.00003996926,0.00002323419,0.0003156188,0.0002159412,0.000008907302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004591788,"about_ca_system_score_gemma":0.00001739414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007091833,"about_ca_topic_score_gemma":0.0002775316,"domain_scores_codex":[0.9980939,0.000131038,0.0003929765,0.0006761885,0.0003511035,0.0003547985],"domain_scores_gemma":[0.9991736,0.00001138388,0.0002122364,0.0002976264,0.00008450772,0.0002206216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001916913,0.00002453362,0.7001455,0.00006384809,0.0000436363,0.000006863621,0.0006480803,0.0004332059,0.2700839,0.000003073926,0.0265681,0.001787491],"study_design_scores_gemma":[0.001822201,0.0001326298,0.9567869,0.00002701944,0.00007457985,0.00001112084,0.0001090271,0.0004266688,0.01670219,0.000225779,0.02306135,0.0006204926],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960051,0.0008019235,0.0003017055,0.001072141,0.0002877739,0.0003611132,0.001076352,0.00003934089,0.00005453861],"genre_scores_gemma":[0.9824368,0.00008397068,0.001013556,0.00146868,0.0003033075,0.000002927099,0.01441101,0.00002959237,0.0002501898],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2566414,"threshold_uncertainty_score":0.9998962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01194792943687302,"score_gpt":0.2144923071948424,"score_spread":0.2025443777579694,"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."}}