Mycorrhization of Quercus dentata Seedlings with Laccaria bicolor Enhances Salt Tolerance of Plants Only Under Relatively Moderate Soil Salinity Level
Bibliographic record
Abstract
Soil salinization is a growing global concern in many ecosystems. Although ectomycorrhizal fungi have been shown to alleviate the effects of salinity in some tree species, uncertainties persist concerning their effectiveness when plants are exposed to different salinity levels that are commonly present in salt-affected soils. Quercus dentata seedlings either non-inoculated (mycorrhizal control) or inoculated with the ectomycorrhizal fungus Laccaria bicolor were then treated with three NaCl concentrations (0, 0.4%, and 0.8%). The physiological, stoichiometric, and growth characteristics of the plants were examined. NaCl significantly affected seedling growth and physiology. However, the impact of L. bicolor on Q. dentata seedlings could shift in response to varying salt concentrations. Under moderate salinity, inoculation of L. bicolor increased root biomass by 4.55% and leaf chlorophyll concentrations by 46.8%, and decreased leaf Na+ concentrations and the Na+/K+ ratios. Under high salinity, L. bicolor decreased leaf water content and fluorescence parameters, and increased leaf Na+ concentrations. The effect of ectomycorrhizal fungus L. bicolor on Q. dentata seedlings was dependent on NaCl concentration, and our results indicate that the use of L. bicolor in afforestation efforts with Q. dentata would only be effective under relatively low soil salinity levels.
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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".