Water‐Use Efficiency Is Negatively Correlated with Leaf Epidermal Conductance in Cotton (<i>Gossypium</i> spp.)
Bibliographic record
Abstract
Water‐use efficiency (WUE) may be a useful trait for improving productivity of cotton (Gossypium spp.) under certain water‐limited conditions, but it is difficult to measure in the field or in large controlled‐environment screening studies. Recently, an easily measured trait, the epidermal conductance of dark‐adapted leaves (gdark), was shown to be predictive of whole‐plant WUE in soybean [Glycine max (L.) Merr.]. Here, a greenhouse experiment was conducted using 22 cotton race stocks, converted lines, and commercial varieties to determine if the relationship between WUE and gdark previously observed in soybean also exists in cotton. A secondary objective was to determine if genotypic differences in WUE and gdark in cotton were constitutive in nature, or if they differed between water replete and drought conditions. There was significant genotypic variation for both WUE and gdark, and in both cases the lack of a treatment × genotype interaction indicated that the trait was constitutive. The relationship between WUE and gdark (r = −0.75, P < 0.0001) was very similar to that reported previously for soybean. Understanding the mechanistic link between gdark and WUE may provide further insight into the physiological basis of genotypic differences in WUE.
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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 | 0.000 |
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".