{"id":"W7116920991","doi":"10.64898/2025.12.19.695474","title":"The RUMIGEN EpiChip: a versatile, medium density DNA methylation Beadchip for large scale population studies in cattle","year":2025,"lang":"en","type":"article","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre de Recherche en Sciences Animales de Deschambault; Agriculture and Agri-Food Canada","funders":"Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement; Région Occitanie Pyrénées-Méditerranée; Javna Agencija za Raziskovalno Dejavnost RS; European Commission; APIS-GENE","keywords":"DNA methylation; Epigenetics; Methylation; CpG site; Population; Differentially methylated regions; Phenotype; Quantitative trait locus","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":[0.001965135,0.0007008928,0.001084816,0.001362884,0.0004524265,0.0008266257,0.0009320639,0.0007777861,0.00422205],"category_scores_gemma":[0.001388408,0.0005714794,0.0008503562,0.001216797,0.0003408765,0.0002787718,0.001136983,0.0007534529,0.001487743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000425076,"about_ca_system_score_gemma":0.0006793477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002205102,"about_ca_topic_score_gemma":0.005951407,"domain_scores_codex":[0.9978219,0.0005882073,0.00007117679,0.0006671595,0.0006629659,0.0001885268],"domain_scores_gemma":[0.9992383,0.0002489316,0.0001296991,0.000143277,0.0001513536,0.0000884464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001065332,0.0001564721,0.02118724,0.0005801994,0.0005766084,0.0001649981,0.0003244351,0.005691534,0.8141459,0.001919766,0.008764522,0.145423],"study_design_scores_gemma":[0.0004335878,0.002456001,0.3580031,0.0002062413,0.001080074,0.001492335,0.0002382684,0.0588248,0.3960286,0.004766377,0.1760544,0.000416286],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2342192,0.004526866,0.6771004,0.000490859,0.0002274505,0.0009920713,0.06343598,0.009240493,0.009766703],"genre_scores_gemma":[0.2703484,0.001337085,0.6666572,0.0008960058,0.0002210702,0.003811457,0.04182368,0.001034135,0.01387096],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00422205,"threshold_uncertainty_score":0.01412421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0150271860295614,"score_gpt":0.2733115572868332,"score_spread":0.2582843712572718,"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."}}