Canopy and leaf photosynthetic characteristics and water use efficiency of sweet sorghum under drought stress
Bibliographic record
Abstract
The objective of this paper was to examine the relationship between Water Use Efficiency (WUE) at the canopy and leaf levels, to determine soil moisture conditions, which can optimize yield, and WUE of sweet sorghum (Sorghum bicolor (Linn.) Moench), thus providing some theoretical foundation for using marginal land effectively and developing production of sweet sorghum. Three levels of soil moisture conditions were established, and photosynthetic characteristics and yield were measured. The canopy apparent photo-synthetic rate (CAP) and leaf photosynthetic rate (P N) were reduced gradually with increased drought stress, and the CAP was lower than the P N under every soil moisture conditions. The P N had a midday depression phenomenon, but the CAP did not exhibit this midday depression phenomenon under severe drought stress. The linear regression relationship of CAP and P N was CAP = 1.5945 + 0.1496 P N. The canopy apparent WUEC and leaf WUEL were the highest under moderate drought stress. The first was 5.3 and 5.8 times higher than the WUEL in mid-July and late August, respectively. The stem fresh biomass yield was 77 tons/ha under moderate drought stress and WUE of aboveground biomass yield (WUEB) was also the highest. Our results showed that moderate drought stress did not result in a significant reduction in biomass yield but increased WUE significantly.
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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".