{"id":"W2112579654","doi":"10.5539/jas.v4n11p16","title":"Quantitative Trait Loci Associated with Moisture, Protein, and Oil Content in Soybean [Glycine max (L.) Merr.]","year":2012,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Soybean genetics and cultivation","field":"Agricultural and Biological Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Army Research Laboratory; Army Research Office","keywords":"Quantitative trait locus; Water content; Inbred strain; Genetic linkage; Moisture; Biology; Soybean oil; Cultivar; Linkage (software); Agronomy; Genetics; Horticulture; Food science; Gene; Chemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0001840059,0.0002852189,0.0001524719,0.0004623283,0.0000927884,0.0001663137,0.0001333146,0.000147098,0.0004551504],"category_scores_gemma":[0.000224157,0.000166334,0.0001206158,0.0002702621,0.0001334488,0.00009286577,0.0001332663,0.0001700294,0.00008070539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003141895,"about_ca_system_score_gemma":0.0001395669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002301908,"about_ca_topic_score_gemma":0.004135242,"domain_scores_codex":[0.9998919,0.00001887717,0.000006928488,0.00004448551,0.00002398875,0.00001392444],"domain_scores_gemma":[0.9997742,0.00004789087,0.0001154408,0.000008750261,0.00001548123,0.00003821446],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003211297,0.0000480315,0.01492304,0.00003865982,0.00002657808,0.00005766606,0.00008014414,0.0001656735,0.9817585,0.00004899183,0.00002901751,0.002502573],"study_design_scores_gemma":[0.00004885007,0.0002946646,0.9544609,0.000005769236,0.00005944041,0.0002936575,0.00007166314,0.002086272,0.04212153,0.0001140207,0.0004251608,0.00001808175],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992157,0.00007729451,0.0004340984,0.000009686635,0.000001121009,0.00000329253,0.0001752289,0.00001506258,0.00006866925],"genre_scores_gemma":[0.997961,0.00005327978,0.001083197,0.00001178646,0.00000202888,0.00001011794,0.0005305015,0.00001036035,0.0003377686],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002301908,"threshold_uncertainty_score":0.004577041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.039517775133211,"score_gpt":0.2337815574059491,"score_spread":0.1942637822727381,"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."}}