{"id":"W1990016126","doi":"10.1021/jf103241y","title":"Interlaboratory Evaluation of a Real-Time Multiplex Polymerase Chain Reaction Method for Identification of Salmon and Trout Species in Commercial Products","year":2011,"lang":"en","type":"article","venue":"Journal of Agricultural and Food Chemistry","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"U.S. Food and Drug Administration; Oregon State University; California Department of Fish and Wildlife; Massachusetts Department of Fish and Game","keywords":"Trout; Polymerase chain reaction; Species identification; Multiplex polymerase chain reaction; Fishery; Identification (biology); Multiplex; Biology; Real-time polymerase chain reaction; Fish <Actinopterygii>; Zoology; Ecology; Bioinformatics; Genetics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.02876359,0.001709741,0.0007601576,0.002115355,0.001225796,0.001738331,0.001411241,0.001680178,0.0009918011],"category_scores_gemma":[0.03348175,0.000990179,0.0007281045,0.001436708,0.001971379,0.0008676966,0.001626146,0.0008797324,0.0008372308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000973642,"about_ca_system_score_gemma":0.001664287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001761984,"about_ca_topic_score_gemma":0.003557352,"domain_scores_codex":[0.9461908,0.02167835,0.003656559,0.01024266,0.01728058,0.0009509663],"domain_scores_gemma":[0.9734591,0.01021961,0.003673547,0.003510393,0.008400901,0.000736505],"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":[0.003819767,0.001426971,0.1509004,0.0005862524,0.000556385,0.000293077,0.004711169,0.002115663,0.7755072,0.0005187515,0.0006508731,0.05891363],"study_design_scores_gemma":[0.0008271527,0.02382967,0.2915258,0.0003454042,0.001270958,0.002200269,0.002653338,0.01994859,0.641599,0.0009999007,0.01438994,0.0004099591],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8736374,0.001730294,0.117636,0.0002431067,0.0003132649,0.001774251,0.0008222852,0.0006121402,0.003231281],"genre_scores_gemma":[0.8407536,0.0004565727,0.1510437,0.0004385912,0.0001309743,0.00253486,0.00268579,0.0001765967,0.001779301],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02876359,"threshold_uncertainty_score":0.1521182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03805484994076669,"score_gpt":0.2835343147384567,"score_spread":0.2454794647976901,"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."}}