Digital phenotyping of Wheat ( Triticum aestivum L.) canopy architectural and stomatal traits for drought and heat tolerance
Bibliographic record
Abstract
Wheat ( Triticum aestivum L.) is one of the key staple crops worldwide. Even though future demand for wheat is estimated to increase by 6% by 2050, wheat production might drop by 30% due to climate change. The purpose of this research is to identify canopy architecture (light capture) and anatomical (stomatal) traits that significantly increase radiation use efficiency (RUE; dry weight biomass produced per unit radiation intercepted) and improve yield under high temperature and drought stress conditions due to the growing concern over food security. This research was conducted with five contrasting wheat genotypes in a new field-based high tunnel system with 4 treatments and three replications (control, heat stress, drought stress and heat x drought stress with 3 replications = 12 tunnels) in a randomized completed block design with stress applied at the heading stage. Canopy architecture was graded according to the visual scoring scale concerning UPOV and RUE was calculated in three growth stages. New techniques of high throughput imaging of stomatal number and size using a handheld digital microscope will also be presented.
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 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 | 0.000 |
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".