{"id":"W2775076633","doi":"10.1094/pdis-08-17-1222-re","title":"Development and Application of a Multiplex qPCR Method for the Simultaneous Detection and Quantification of <i>Pratylenchus alleni</i> and <i>P. penetrans</i> in Quebec, Canada","year":2017,"lang":"en","type":"article","venue":"Plant Disease","topic":"Nematode management and characterization studies","field":"Agricultural and Biological Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cégep Saint-Jean-sur-Richelieu; Grain Research Centre; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Ministère de l'Agriculture, des Pêcheries et de l'Alimentation","keywords":"Biology; Pratylenchus penetrans; Larva; Nematode; Multiplex polymerase chain reaction; Population density; Veterinary medicine; Agronomy; Population; Zoology; Ecology; Polymerase chain reaction; Genetics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007560026,0.00072136,0.0005150199,0.0009702163,0.001027555,0.0007787903,0.0008863841,0.0005115636,0.001944902],"category_scores_gemma":[0.0005963959,0.0005024033,0.0003931606,0.0009054138,0.0005472716,0.0002773868,0.0004100634,0.0006390492,0.0005282487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005943252,"about_ca_system_score_gemma":0.007689288,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7083746,"about_ca_topic_score_gemma":0.8791733,"domain_scores_codex":[0.9990464,0.00005690526,0.00003418171,0.0002997057,0.0004364116,0.0001264325],"domain_scores_gemma":[0.9992987,0.00006095821,0.00009781495,0.00002158095,0.0004698782,0.00005108792],"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":[0.0001372695,0.00009310395,0.01829714,0.0002578635,0.00004535863,0.000108728,0.0003668984,0.002260521,0.94357,0.0003698796,0.0009879289,0.0335054],"study_design_scores_gemma":[0.00009204377,0.001221346,0.3271705,0.0002252812,0.0002195465,0.0005572978,0.001256677,0.04859263,0.5615551,0.0002668732,0.05858534,0.0002572547],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7650623,0.003695061,0.1872675,0.0007541734,0.0002043339,0.002467775,0.01484668,0.001830575,0.02387173],"genre_scores_gemma":[0.7557307,0.002068397,0.2036494,0.00063009,0.00003273957,0.001989602,0.00742659,0.0001527885,0.02831974],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.2916254,"threshold_uncertainty_score":0.5866857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0189452143430977,"score_gpt":0.2309129572875797,"score_spread":0.211967742944482,"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."}}