Effects of root medium <scp>pH</scp> on root water transport and apoplastic <scp>pH</scp> in red‐osier dogwood (<i>Cornus sericea</i>) and paper birch (<i>Betula papyrifera</i>) seedlings
Bibliographic record
Abstract
Soil pH is a major factor affecting plant growth. Plant responses to pH conditions widely vary between different species of plants. However, the exact mechanisms of high pH tolerance of plants are largely unknown. In the present study, we compared the pH responses of paper birch (Betula papyrifera) seedlings, a relatively sensitive species to high soil pH, with red-osier dogwood (Cornus sericea), reported to be relatively tolerant of high pH conditions. We examined the hypotheses that tolerance of plants to high root zone pH is linked to effective control of root apoplastic pH to facilitate nutrient and water transport processes In the study, we exposed paper birch and red-osier dogwood seedlings for six weeks to pH 5, 7 and 9 under controlled-environment conditions in hydroponic culture. Then, we measured biomass, gas exchange, root hydraulic conductivity, ferric chelate reductase (FCR) activity, xylem sap pH and the relative abundance of major elements in leaf protoplasts and apoplasts. The study sheds new light on the rarely studied high pH tolerance mechanisms in plants. We found that compared with paper birch, red-osier dogwood showed greater growth, higher gas exchange, and maintained higher root hydraulic conductivity as well as lower xylem sap pH under high pH conditions. The results suggest that the relatively high pH tolerance of dogwood is associated with greater water uptake ability and maintenance of low apoplastic pH. These traits may have a significant impact on the uptake of Fe and Mn by leaf cells.
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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".