Relationship between plant growth and organic acid exudates from ectomycorrhizal and non-ectomycorrhizal<i>Pinus patula</i>
Bibliographic record
Abstract
Plant–mycorrhizal interaction is an important association in the ecosystem with significant impacts on the physical, biological and chemical properties of the soil. In the present study, potential relationships that exist between organic acid production by ectomycorrhizal pine seedlings and plant parameters in the absence of any significant environmental stress were investigated. The aim of the study was to investigate the contribution of organic acid production to plant growth. Four different ectomycorrhizal fungi were used in a mycorrhizal synthesis experiment to colonise roots of Pinus patula. Ectomycorrhizal and non-ectomycorrhizal plants were used in a pot trial experiment that lasted for 24 weeks. After harvesting, plant materials as well as soil samples underwent different analyses, which included the determination of pH, organic acids, plant biomass, and foliar and root phosphorus and potassium. The results indicated a significant interaction (P<0.0001) between fungal type and organic acid production. This reflects the influence of fungal type on organic acid production. However, it was observed that organic acids secreted into the soil are not directly linked with the quantity of nutrients detected in either the root or shoot, but seemed to positively influence plant growth as reflected in the result from root and shoot biomass.
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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) | 0.001 | 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".