Different responses of two Mosla species to potassium limitation in relation to acid rain deposition
Bibliographic record
Abstract
The increasingly serious problem of acid rain is leading to increased potassium (K) loss from soils, and in our field investigation, we found that even congenerically relative Mosla species show different tolerance to K-deficiency. A hydroponic study was conducted on the growth of two Mosla species and their morphological, physiological and stoichiometric traits in response to limited (0.35 mmol K/L), normal (3.25 mmol K/L) and excessive (6.50 mmol K/L) K concentrations. Mosla hangchowensis is an endangered plant, whereas Mosla dianthera a widespread weed. In the case of M. hangchowensis, in comparison with normal K concentration, K-limitation induced a significant reduction in net photosynthetic rate (P(n)), soluble protein content, and superoxide dismutase (SOD) activity, but an increase in malondialdehyde (MDA) concentration. However, leaf mass ratio (LMR) and root mass ratio (RMR) were changed little by K-limitation. In contrast, for M. dianthera, K-limitation had little effect on P(n), soluble protein content, SOD activity, and MDA concentration, but increased LMR and RMR. Critical values of N (nitrogen):K and K:P (phosphorus) ratios in the shoots indicated that limitation in acquiring K occurred under K-limited conditions for M. hangchowensis but not for M. dianthera. We found that low K content in natural habitats was a restrictive factor in the growth and distribution of M. hangchowensis, and soil K-deficiency caused by acid rain worsened the situation of M. hangchowensis, while M. dianthera could well acclimate to the increasing K-deficiency. We suggest that controlling the acid rain and applying K fertilizers may be an effective way to rescue the endangered M. hangchowensis.
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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 | 0.001 | 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) | 0.001 | 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".