{"id":"W4400366100","doi":"10.5376/amb.2024.14.0014","title":"Analyzing the Impact of Marker-Assisted Selection on Livestock Productivity and Genetic Diversity","year":2024,"lang":"en","type":"article","venue":"Animal Molecular Breeding","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Livestock; Selection (genetic algorithm); Genetic diversity; Genomic selection; Productivity; Biotechnology; Identification (biology); Marker-assisted selection; Diversity (politics); Biology; Genotyping; Genetic marker; Evolutionary biology; Ecology; Computer science; Genetics; Genotype; Population; Artificial intelligence; Medicine; Political science; Economic growth; Economics","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":[0.0001391592,0.0001613073,0.0001207681,0.00005972116,0.0001723172,0.00003974779,0.0001386826,0.00008868867,0.000007886356],"category_scores_gemma":[0.00005790256,0.0001215096,0.0001347871,0.0001916644,0.0001116735,0.00000531756,0.0002241624,0.0001348762,0.000001729184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002028728,"about_ca_system_score_gemma":0.00005584801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001115948,"about_ca_topic_score_gemma":0.000007387936,"domain_scores_codex":[0.9990999,0.00007461159,0.0001320812,0.0003868294,0.0001173398,0.0001892868],"domain_scores_gemma":[0.9996541,0.00002080679,0.00004882434,0.0001702729,0.00005377661,0.00005226584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001483748,0.00004654033,0.03625065,0.00003785967,0.0002363497,0.000003199119,0.0001079818,0.0005517997,0.9543047,0.0008524835,0.0002557177,0.007204341],"study_design_scores_gemma":[0.0001373338,0.00211734,0.9433143,0.00004459449,0.00008136903,0.00007917108,0.00002428514,0.0003946741,0.0530733,0.000518047,0.00005127651,0.0001643358],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916856,0.00132829,0.006075285,0.00008397086,0.0000686524,0.0001849377,0.000007906498,0.00001786592,0.0005475511],"genre_scores_gemma":[0.9978877,0.00002350488,0.001852647,0.00001144102,0.0001439639,0.000005749492,0.000005247784,0.00001919514,0.00005055854],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9070636,"threshold_uncertainty_score":0.4955019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01479070009228784,"score_gpt":0.2552211219615022,"score_spread":0.2404304218692144,"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."}}