{"id":"W2060564963","doi":"10.1016/j.tvjl.2014.09.014","title":"Measurement of serum immunoglobulin G in dairy cattle using Fourier-transform infrared spectroscopy: A reagent free approach","year":2014,"lang":"en","type":"article","venue":"The Veterinary Journal","topic":"Animal health and immunology","field":"Veterinary","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Prince Edward Island","funders":"Atlantic Canada Opportunities Agency; Ministère de l'Education Nationale, de l'Enseignement Superieur et de la Recherche; Ministry of Higher Education; Ministry of Higher Education and Scientific Research","keywords":"Fourier transform infrared spectroscopy; Spectroscopy; Analytical Chemistry (journal); Chemistry; Radial immunodiffusion; Partial least squares regression; Calibration; Fourier transform; Mathematics; Chromatography; Antibody; Statistics; Immunology; Biology; Optics; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00236574,0.0002578195,0.0004510769,0.0001965785,0.0003185121,0.00003363186,0.0007816747,0.0001361832,0.0001609372],"category_scores_gemma":[0.00009453208,0.0001920182,0.0001638943,0.0002367096,0.0001762246,0.0002124668,0.0002129537,0.0007774462,0.00001779162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003388338,"about_ca_system_score_gemma":0.0002309011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001745284,"about_ca_topic_score_gemma":0.000006976794,"domain_scores_codex":[0.9971626,0.0006151882,0.0008483076,0.0002331292,0.0004791682,0.0006615695],"domain_scores_gemma":[0.9986991,0.00004930844,0.000348215,0.0006630259,0.0001176087,0.0001228015],"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":[0.06228112,0.003068272,0.01171761,0.001760361,0.0009313248,0.0008539264,0.01446851,0.0004961113,0.8498266,0.001223854,0.003567388,0.04980495],"study_design_scores_gemma":[0.01968049,0.09473671,0.7294216,0.002386955,0.0003823357,0.06204711,0.006558533,0.01871316,0.008670437,0.01475995,0.04028162,0.002361117],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9868532,0.001836453,0.00451676,0.0003527777,0.0004349698,0.0003391778,0.00001597653,0.00002598323,0.005624632],"genre_scores_gemma":[0.9933424,0.0001722589,0.006028475,0.0001262168,0.0002273877,0.0000118123,0.000001718048,0.0000385175,0.00005122993],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8411561,"threshold_uncertainty_score":0.7830276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1032357887654242,"score_gpt":0.3305061655909576,"score_spread":0.2272703768255335,"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."}}