{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001247046,0.0004568055,0.0008231794,0.0001173281,0.0005047436,0.00003798536,0.000316867,0.0002968714,0.00002156014],"category_scores_gemma":[0.0006320146,0.0002435037,0.0001568558,0.0005110705,0.0003969894,0.0003584039,0.0003136196,0.0001998275,0.000007879115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002006236,"about_ca_system_score_gemma":0.00008075304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004750215,"about_ca_topic_score_gemma":0.001609365,"domain_scores_codex":[0.9961031,0.000584908,0.0009481785,0.0009535183,0.0005349749,0.0008752916],"domain_scores_gemma":[0.9970143,0.001482336,0.0007530884,0.0001582822,0.000426966,0.0001649731],"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":[0.001074936,0.0001130328,0.006117053,0.00008385879,0.0001255294,0.000008329986,0.0001597114,0.000006320557,0.899678,0.00003832801,0.0005269154,0.09206802],"study_design_scores_gemma":[0.0009788509,0.0003413538,0.2406355,0.0001787297,0.00006813135,0.00002103158,0.000143346,0.00003192976,0.756547,0.0003133432,0.0003987563,0.0003419615],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996023,0.0003626818,0.0006858392,0.0006573766,0.0003466365,0.0008490934,0.0009114392,0.0001096571,0.00005426392],"genre_scores_gemma":[0.9951563,0.0007742647,0.003148663,0.0000995242,0.0001774861,0.0002360818,0.000381832,0.00001203087,0.00001374167],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2345185,"threshold_uncertainty_score":0.992979,"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."}}