Dark-adapted leaf conductance, but not minimum leaf conductance, predicts water use efficiency of soybean (Glycine max L. Merr.)
Bibliographic record
Abstract
Walden-Coleman, A. E., Rajcan, I. and Earl, H. J. 2013. Dark-adapted leaf conductance, but not minimum leaf conductance, predicts water use efficiency of soybean (Glycine max L. Merr.). Can. J. Plant Sci. 93: 13-22. The conductance to water vapor of dark-adapted leaves (gdark) has been shown to be negatively correlated with whole-plant water use efficiency (WUE) in soybean, but the physiological basis of this relationship is unknown. It is also not clear how gdark compares with the minimum leaf conductance of wilted leaves (gmin), a trait that has been studied extensively across a broad range of species. We compared gdark to gmin of soybean leaves and found that gdark values were consistently much higher than gmin values measured on the same leaves. Also, across seven soybean varieties known to differ for WUE, gdark but not gmin was correlated with WUE. Thus, gdark and gmin should be considered distinct traits. We measured gdark at two different leaf positions, and found that gdark measured at the lower leaf position (two main stem nodes below the youngest fully expanded leaf) was best correlated with WUE. We then used this method to screen a selection of current commercial soybean varieties adapted to Ontario, Canada, for variation in gdark. The range in gdark among the commercial varieties was as broad as that measured previously among more diverse genotypes, suggesting that Ontario soybean varieties might also vary widely for 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) | 0.000 | 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".