{"id":"W2497812208","doi":"10.1094/phyto-05-16-0204-r","title":"Development and Validation of High-Resolution Melting Markers Derived from <i>Ry</i><sub><i>sto</i></sub> STS Markers for High-Throughput Marker-Assisted Selection of Potato Carrying <i>Ry</i><sub><i>sto</i></sub>","year":2016,"lang":"en","type":"article","venue":"Phytopathology","topic":"Plant Virus Research Studies","field":"Agricultural and Biological Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"123 Certification (Canada); Government of New Brunswick; University of Victoria","funders":"Division of Materials Research; Agriculture and Agri-Food Canada","keywords":"High Resolution Melt; Biology; Primer (cosmetics); Genetics; Marker-assisted selection; Molecular biology; Selection (genetic algorithm); Genetic marker; Gene; Genotype; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000596857,0.0005463372,0.0005575127,0.0005205195,0.0002071139,0.000465732,0.0004009316,0.0006211281,0.0005736057],"category_scores_gemma":[0.001135637,0.0003807251,0.0005876502,0.0005367221,0.0002561466,0.0002251416,0.0003387189,0.0007210029,0.0009036832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001758453,"about_ca_system_score_gemma":0.0002279909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005311293,"about_ca_topic_score_gemma":0.001221272,"domain_scores_codex":[0.9994568,0.0001043302,0.00006069803,0.0001803972,0.0001438597,0.00005403375],"domain_scores_gemma":[0.9992447,0.0001786347,0.0002572446,0.00009265097,0.000142383,0.00008439975],"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":[0.00002443345,0.000009836727,0.0003264433,0.00001820008,0.000003878456,0.00001397975,0.00002084571,0.00005924181,0.9984134,0.00001122747,0.000009571362,0.00108894],"study_design_scores_gemma":[0.000009578042,0.0002000368,0.01519456,0.00001069648,0.00005057226,0.0002777959,0.0000494188,0.001429931,0.9813709,0.00002225469,0.001371485,0.00001284857],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9230893,0.0005912079,0.07236415,0.00008618861,0.00002336371,0.0002297099,0.001959014,0.0004069734,0.001250083],"genre_scores_gemma":[0.8257449,0.0006593795,0.1611129,0.0001055354,0.00001430281,0.0003579907,0.008883335,0.0002866665,0.002835007],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006211281,"threshold_uncertainty_score":0.003156483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02549862760937354,"score_gpt":0.2380469832372354,"score_spread":0.2125483556278619,"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."}}