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Record W6996021854

Quantitative trait loci associated with soybean seed weight and composition under different phosphorus levels

2018· article· en· W6996021854 on OpenAlexaboutno aff

Bibliographic record

VenueSabanci University · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsQuantitative trait locusTransgressive segregationHeritabilityCultivarPhosphorusPoint of deliveryTraitInbred strainComposition (language)
DOInot available

Abstract

fetched live from OpenAlex

Seed size and composition are important traits in food crops and can be affected by nutrient availability in the soil. Phosphorus (P) is a non‐renewable, essential macronutrient, and P deficiency limits soybean (Glycine max) yield and quality. To investigate the associations of seed traits in low‐ and high‐P environments, soybean recombinant inbred lines (RILs) from a cross of cultivars Fiskeby III and Mandarin (Ottawa) were grown under contrasting P availability environments. Traits including individual seed weight, seed number, and intact mature pod weight were significantly affected by soil P levels and showed transgressive segregation among the RILs. Surprisingly, P treatments did not affect seed composition or weight, suggesting that soybean maintains sufficient P in seeds even in low‐P soil. Quantitative trait loci (QTLs) were detected for seed weight, intact pods, seed volume, and seed protein, with five significant QTLs identified in low‐P environments and one significant QTL found in the optimal‐P environment. Broad‐sense heritability estimates were 0.78 (individual seed weight), 0.90 (seed protein), 0.34 (seed oil), and 0.98 (seed number). The QTLs identified under low P point to genetic regions that may be useful to improve soybean performance under limiting P conditions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.926
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.207
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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