{"id":"W4393319458","doi":"10.1002/aro2.58","title":"Developing a liquid capture chip to accelerate the genetic progress of cattle","year":2024,"lang":"en","type":"article","venue":"Animal Research and One Health","topic":"Animal Genetics and Reproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Agricultural Science and Technology Innovation Program; Chinese Academy of Agricultural Sciences","keywords":"Chip; Nanotechnology; Computer science; Business; Biotechnology; Biology; Materials science; Telecommunications","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":[0.0008222422,0.0004450459,0.0004764791,0.000480616,0.0002131573,0.0006712315,0.0006782609,0.0005115604,0.00287798],"category_scores_gemma":[0.0007365138,0.0003910778,0.0003659415,0.0003469152,0.0002571748,0.0002826128,0.0005407664,0.000483548,0.001463795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003883933,"about_ca_system_score_gemma":0.0003620982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001426851,"about_ca_topic_score_gemma":0.003670805,"domain_scores_codex":[0.9992664,0.0001244086,0.00003372596,0.0002118275,0.0002544647,0.0001091788],"domain_scores_gemma":[0.9995912,0.0001778507,0.00004398748,0.00005091748,0.0001012536,0.00003479779],"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":[0.00008689238,0.00003589168,0.002000449,0.0001164964,0.00004422489,0.00002357638,0.00001967379,0.000680757,0.9764499,0.0002474755,0.0009377704,0.0193569],"study_design_scores_gemma":[0.00005546921,0.0005321499,0.03354432,0.00003631756,0.0001499046,0.0002804262,0.00006361487,0.03101494,0.9112229,0.0003859996,0.02263152,0.00008232761],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3693324,0.003550478,0.6128678,0.0005437456,0.0003835985,0.000349044,0.004247251,0.003514241,0.005211474],"genre_scores_gemma":[0.4979614,0.001157195,0.4789529,0.001556855,0.0001083795,0.0009240838,0.006099026,0.0003091103,0.01293106],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00287798,"threshold_uncertainty_score":0.009627819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1071498866843668,"score_gpt":0.4151002576635967,"score_spread":0.3079503709792299,"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."}}