Elevated CO<sub>2</sub> concentrations affect the growth patterns of dominant C<sub>3</sub> and C<sub>4</sub> shrub species differently in the Mu Us Sandy Land of Inner Mongolia
Bibliographic record
Abstract
Elevated CO2 levels can improve growth and water use efficiency (WUE). However, the influence of atmospheric CO2 concentrations higher than 800 ppm has been less of a concern and has received little attention. In this study, experiments were conducted to explore the responses of four species to elevated CO2 levels. Seedlings of the four species were grown in growth chambers under four different CO2 concentrations. The results showed that elevated CO2 levels resulted in increased net assimilation rates (NARs) (12%–90%) and WUEs (1%–258%) as well as decreased leaf area ratios (LARs) (11%–72%) for the four species. For the two Artemisia species (Artemisia sphaerocephala Krasch. and Artemisia ordosica Krasch.), elevated CO2 significantly increased the relative growth rate (RGRs) (4%–8%) and total biomass increment (19%–44%), while elevated CO2 concentrations resulted in decreased RGRs (2%–20%) and transpiration rates (49%–61%) for Hedysarum laeve Maxim., and increased the height increment (7%–96%) of Caragana korshinskii Kom. The differences among the two Artemisia species (C4 species), and C. korshinskii and H. laeve (C3 species) might be associated with their different photosynthetic pathways. These findings suggest that the two Artemisia species had a stronger ability to adapt to CO2 concentration elevation. Considering its high WUE, C. korshinskii should be applied for vegetation restoration in water-limited areas affected by a warming climate.
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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".