Dark-adapted leaf conductance, but not minimum leaf conductance, predicts water use efficiency of soybean (<i>Glycine max</i> 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 (g dark ) 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 g dark compares with the minimum leaf conductance of wilted leaves (g min ), a trait that has been studied extensively across a broad range of species. We compared g dark to g min of soybean leaves and found that g dark values were consistently much higher than g min values measured on the same leaves. Also, across seven soybean varieties known to differ for WUE, g dark but not g min was correlated with WUE. Thus, g dark and g min should be considered distinct traits. We measured g dark at two different leaf positions, and found that g dark 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 g dark . The range in g dark 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".