Selecting Wheat Seeds of Moderate Phytate Using Colorimetric Method
Bibliographic record
Abstract
A simple and effective method of determining the phytate content of a single wheat seed was considered necessary for selecting the materials of moderate phytate during early generations breeding. 23 wheat genotypes (16 winter wheat cultivars and 7 spring wheat cultivars) were firstly used as determining samples of phytate content (PC) and inorganic phosphorus content (IPC) of the whole meal, secondarily they were planted in two locations for two times (2004 and 2005). The all single plants were harvested by a plot from those sites, then 6 individual plants were randomly selected to save their seeds for each plot, and the seeds of other plants were grinded into whole meal to assay their PC and IPC. Subsequently, every seed from per individual plant was cut into two semi-grain seeds: the semi-grain seed without embryo (SGSWOE) and the semi-grain seed with embryo (SGSWE); the SGSWOE was carried out to assay colorimetric scoring values by inorganic phosphorus content (CSVIPC), while according to the CSVIPC index, the satisfied SGSWEs were saved to plant at next generation or the dissatisfied SGSWEs were eliminated. The results showed that there was significantly negative correlation between PC and IPC of the wheat meal from either the first samples or the materials harvested at those sites, and for the latter there was significantly negative correlation between PC and CSVIPC. There was no significant difference of CSVIPC among the SGSWEOs and SGSWE of the spikes and individual plants from the same plot or the same genotype at any sites. In conclusion the CSVIPC of the SGSWOEs can indirectly express PC level of the wheat meal: the higher the former was, the lower the latter was. So the CSVIPC of SGSWOE can be applied to assaying the level of corresponding SGSWE according to their index, and screening the individual plant of moderation phytate. Moreover, this method is characterized by high efficiency during breeding processing.
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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) | 0.001 | 0.001 |
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".