Transverse Sectioning of Mature Rice (<em>Oryza sativa</em> L.) Kernels for Scanning Electron Microscopy Imaging Using Pipette Tips as Immobilization Support
Bibliographic record
Abstract
Starch granules (SGs) exhibit different morphologies depending on the plant species, especially in the endosperm of the Poaceae family. Endosperm phenotyping can be used to classify genotypes based on SG morphotype using scanning electron microscopic (SEM) analysis. SGs can be visualized using SEM by slicing through the kernel (pericarp, aleurone layers, and endosperm) and exposing the organellar contents. Current methods require the rice kernel to be embedded in plastic resin and sectioned using a microtome or embedded in a truncated pipette tip and sectioned by hand using a razor blade. The former method requires specialized equipment and is time-consuming, while the latter introduces a new host of problems depending on rice genotype. Chalky rice varieties, particularly, pose a problem for this type of sectioning due to the friable nature of their endosperm tissue. Presented here is a technique for preparing translucent and chalky rice kernel sections for microscopy, requiring only pipette tips and a scalpel blade. Preparing the sections within the confines of a pipette tip prevents rice kernel endosperm from shattering (for translucent or 'vitreous' phenotypes) and crumbling (for chalky phenotypes). Using this technique, endosperm cell patterning and the structure of intact SGs can be observed.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".