Seed traits evaluation from long-term selection of kernel oil concentration in a high-oil maize population KYHO
Bibliographic record
Abstract
Wang, H.-W., Hu, H.-X., Song, T.-M. and Chen, S.-J. 2012. Seed traits evaluation from long-term selection of kernel oil concentration in a high-oil maize population KYHO. Can. J. Plant Sci. 92: 857–866. A high-oil maize population, KYHO, was developed over 10 generations by selective breeding for increased kernel oil content (KOC). The objectives of this study were to evaluate kernel oil selection effects, and measure the trait changes and genetic variance in the embryo and endosperm. Oil, protein, and starch content in the embryo and endosperm were estimated by near-infrared reflectance spectroscopy (NIRS). Mass and volume of embryo and endosperm were measured. Selective breeding increased embryo oil content (EMOC) and endosperm oil content (ENOC), especially EMOC, which changed from 315.62 g kg−1C0 to 592.54 g kg−1C10, resulting in an increase in total embryo and endosperm oil content (EEOC) from 43.32 g kg−1C0 to 139.95 g kg−1C10. With selection for increase in EEOC, embryo protein content (EMPC) decreased slightly; however, endosperm protein content (ENPC) and total protein content (EEPC) increased significantly. Embryo and endosperm starch content (EMSC and ENSC) and total starch content (EESC) all decreased substantially with selection. One hundred embryo mass (EMM) was not notably changed with selection, but 100 embryo volume (EMV) increased significantly. Mass and volume of endosperm (ENM and ENV) and total mass and volume of embryo and endosperm (EEM and EEV) all decreased significantly with selection, possibly due to markedly decreased starch content. Linear regression analysis indicated with each 1 g kg−1EEOC increase, EMOC, ENOC, ENPC, EEPC, EMM, and EMV increased 2.74 g kg−1, 0.16 g kg−1, 0.38 g kg−1, 0.36 g kg−1, 0.06 g, and 0.20 mL, respectively, and EMPC, EMSC, ENSC, EESC, EEM, ENM, EEV, and ENV decreased 0.04 g kg−1, 1.48 g kg−1, 0.60 g kg−1, 1.09 g kg−1, 1.26 g, 1.32 g, 0.97 g, and 1.17 mL, rspectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".