{"id":"W4379285102","doi":"10.1101/2023.06.01.543254","title":"QTL Mapping of Seed Fe Concentration in an Interspecific RIL Population Derived from <i>Lens culinaris</i> × <i>Lens ervoides</i>","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetic and Environmental Crop Studies","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Grand Challenges Canada; Natural Sciences and Engineering Research Council of Canada; Saskatchewan Pulse Growers; Ministry of Agriculture - Saskatchewan","keywords":"Quantitative trait locus; Biology; Population; Biofortification; Best linear unbiased prediction; Interspecific competition; Transgressive segregation; Inbred strain; Genetics; Selection (genetic algorithm); Botany; Gene; Chemistry","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":[0.0004749009,0.0005124618,0.0003808021,0.000833799,0.0003122319,0.0003000403,0.0005073799,0.0002225971,0.0009338257],"category_scores_gemma":[0.0002448897,0.0002886843,0.0005066185,0.0004576352,0.0002259599,0.0001090279,0.0003703481,0.0006081545,0.0002166585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008161993,"about_ca_system_score_gemma":0.0003045105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007510048,"about_ca_topic_score_gemma":0.01298607,"domain_scores_codex":[0.9996321,0.00007170503,0.00003205453,0.0001522487,0.00007178426,0.00004008047],"domain_scores_gemma":[0.9996222,0.0001028064,0.0001176583,0.00004108729,0.00004714213,0.00006905859],"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":[0.0001475261,0.00006918822,0.002462903,0.0000180481,0.00004506029,0.00007467999,0.00006418965,0.0001700368,0.9952943,0.00005157818,0.00003012224,0.001572424],"study_design_scores_gemma":[0.0002060877,0.0009695851,0.5241606,0.0000293522,0.0004443076,0.001001678,0.0002964659,0.005593335,0.4617985,0.00009414781,0.005335059,0.00007081678],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961141,0.0001025877,0.002463421,0.0000184348,0.000004755253,0.00003616111,0.0006990841,0.00004903042,0.0005123227],"genre_scores_gemma":[0.9869469,0.0001434269,0.006706056,0.00006299032,0.000005905872,0.00007134642,0.003973955,0.0001249939,0.001964462],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007510048,"threshold_uncertainty_score":0.01493263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03569477555731275,"score_gpt":0.207864500287201,"score_spread":0.1721697247298883,"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."}}