{"id":"W1552983290","doi":"10.3968/5487","title":"Some Genetic Features of Population Migration","year":2014,"lang":"en","type":"article","venue":"Advances in natural science/Advances in natural sciences","topic":"Agriculture and Biological Studies","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Inbreeding; Population; Panmixia; Selection (genetic algorithm); Biology; Allele frequency; Natural selection; Genetics; Allele; Computer science; Demography; Gene","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001246213,0.0003575998,0.0005166039,0.0001541684,0.0006123995,0.0001080702,0.001352617,0.0001321781,0.00001819125],"category_scores_gemma":[0.0007838226,0.0001275662,0.00012447,0.004398305,0.001775516,0.003807896,0.0002552882,0.0004223066,0.000004524013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001492062,"about_ca_system_score_gemma":0.00002081484,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006554567,"about_ca_topic_score_gemma":0.01836539,"domain_scores_codex":[0.9960978,0.000158656,0.0007046988,0.001026052,0.001099808,0.0009129806],"domain_scores_gemma":[0.9985648,0.0006812671,0.0003990197,0.0001043762,0.0001470817,0.0001034557],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0000658889,0.0001076155,0.2599773,0.00002372722,0.000002210136,0.000002943486,0.00009894906,0.001187235,0.0675817,0.01186509,0.00002107864,0.6590663],"study_design_scores_gemma":[0.0002128136,0.0004131662,0.9582877,0.0001070354,0.000004412386,0.000009042663,0.0003748121,0.0006029208,0.004685052,0.02814104,0.006725645,0.0004363499],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9164426,0.07968193,0.000001837416,0.0009058877,0.001264045,0.0003671338,0.000005836912,0.00005681515,0.001273852],"genre_scores_gemma":[0.9893736,0.008604123,0.001256818,0.0002534641,0.0004151841,0.00002495886,0.00002021812,0.000001139578,0.00005052162],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6983104,"threshold_uncertainty_score":0.9995469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007180829506538582,"score_gpt":0.2597145680372807,"score_spread":0.2525337385307421,"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."}}