Effects of simulated acid rain on the photosynthetic physiology of <i>Acer ginnala</i> seedlings
Bibliographic record
Abstract
Extensive areas in China have been receiving high levels of acid rain. Acid rain affects plant growth by reducing the chlorophyll content, destroying leaf structure, and hindering photosynthesis. Acer ginnala Maxim. has a high economic, ornamental, and medicinal value. To clarify the acid rain resistance mechanism of maple trees, the chlorophyll content, gas exchange parameters, and chlorophyll fluorescence parameters were measured in the leaves of 2-year-old Acer ginnala seedlings 0, 15, and 30 days after simulated acid rain at pH 5.6, 5.0, 4.5, 4.0, 3.5, and 2.5. The results showed that the relative content of chlorophyll gradually decreased with the increasing acidity. The net photosynthetic rate (Pn) decreased with the increasing acidity, and the difference among the treatments was significant except for between pH 5.0 and the control. The stomatal conductance at pH < 5.0 was significantly lower than that of the control, and the intercellular carbon dioxide (CO2) concentration of the leaves with the pH < 4.5 treatments was significantly lower than that of the control. As the acidity increased, both Fv/Fm and Fv/F0 (Fv, variable fluorescence; Fm, maximum fluorescence; F0, initial fluorescence) decreased. The difference in the photosynthetic performance index based on the absorbed light energy (PIabs) between the pH 5.0 treatment and the control was not significant during the three periods after the simulated acid rain stress, whereas in the other treatments, this index was significantly lower than that of the control. Our results indicated that under mild acid rain (pH > 4.0) stress, the chlorophyll content, gas exchange parameters, and chlorophyll fluorescence parameters of Acer ginnala changed little, whereas in the other treatments, especially the severe acid rain (pH < 3.0) treatment, these indices showed significant changes that had a severe impact on plant growth and development.
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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".