Interactive Effects of Elevated [CO<sub>2</sub>] and Soil Water Stress on Leaf Morphological and Anatomical Characteristic of Paper Birch Populations
Bibliographic record
Abstract
The leaf morphological and stomatal characteristics of four paper birch (Betula papyrifera Marsh) populations, grown at four treatment conditions of carbon dioxide [CO2] and soil water levels were investigated to determine whether future increases in atmospheric [CO2] and water deficit affected the leaf characteristics. The populations from Cussion Lake, Little Oliver, Skimikin and Wayerton were grown for 12 weeks under ambient (360 ppm) and elevated (720 ppm) [CO2] at both high and low water levels. The populations significantly differed in leaf area and stomatal characteristics due to the interaction effects of [CO2], water levels and population differences. Most leaf morphological characteristics and stomatal density varied due to the effects of [CO2] and/or populations, but not due to the effect of water levels. Although elevated [CO2] alone barely affected stomatal area of the birch populations, simultaneous elevated [CO2] at both water levels had stimulated stomatal characteristics within and among the populations. Overall, elevated [CO2] reduced leaf area and increased stomatal density; and low water level resulted in smaller stomatal area, pore area and guard cell width. However, the populations responded differently to an increase in [CO2] and water levels. All populations showed plastic responses with respect to [CO2] and water levels either by decreasing stomatal area under low water level or by increasing stomatal density under elevated [CO2]. Hence, integration between and within leaf characteristics had helped paper birch populations maintain balance between [CO2] gain and water loss.
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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 | 0.001 |
| 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".