{"id":"W2568995163","doi":"10.18174/318534","title":"Towards marker assisted breeding in garden roses: from marker development to QTL detection","year":2014,"lang":"en","type":"dissertation","venue":"","topic":"Powdery Mildew Fungal Diseases","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Introgression; Cultivar; Microsatellite; Genetic diversity; Biology; Selection (genetic algorithm); Marker-assisted selection; Molecular marker; Plant breeding; Horticulture; Allele; Biotechnology; Genetic marker; Genetics; Population; Computer science; Demography; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002295886,0.0004537799,0.0006713751,0.0006108892,0.0002006794,0.001161169,0.0008679289,0.0005759408,0.001062276],"category_scores_gemma":[0.0009512343,0.000489082,0.000547378,0.000854075,0.0005904507,0.0006566087,0.000777429,0.00143852,0.0006455727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003692623,"about_ca_system_score_gemma":0.0005374551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001172453,"about_ca_topic_score_gemma":0.001605218,"domain_scores_codex":[0.9994619,0.0001178458,0.00002805117,0.0002021209,0.0001461284,0.00004405639],"domain_scores_gemma":[0.9995229,0.0001571443,0.00009916441,0.00007592555,0.000104506,0.00004037744],"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":[0.0002384357,0.00008368153,0.01158355,0.0008536709,0.0001226624,0.0003662006,0.001051838,0.00690296,0.5573383,0.01048908,0.001040417,0.4099292],"study_design_scores_gemma":[0.0002997728,0.003390498,0.1434172,0.001381458,0.0008077881,0.003414954,0.001605313,0.05236904,0.4840752,0.04315623,0.2657103,0.0003722633],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3003096,0.0341923,0.655779,0.001986437,0.00019174,0.0002653023,0.0007008419,0.001166076,0.005408781],"genre_scores_gemma":[0.2546155,0.02944242,0.7066238,0.0006848956,0.0001550606,0.0001879276,0.00138657,0.0002652743,0.006638492],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002295886,"threshold_uncertainty_score":0.01214194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01926585136729292,"score_gpt":0.2351495040135559,"score_spread":0.215883652646263,"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."}}