Reciprocal interactions between plants and soil in an upland grassland
Bibliographic record
Abstract
Abstract Through the production of litter, plants with different life history strategies are predicted to both affect and be affected by the properties of soil. Competitive species are expected to increase the fertility of, and have a positive growth feedback with, soil, whereas stress‐tolerant species should decrease fertility but show no growth feedback. We maintained monocultures of competitive ( Lolium perenne and Agrostis capillaris ) and stress‐tolerant ( Festuca ovina and Nardus stricta ) grasses on an unproductive grassland for six years. The Nardus soil developed significantly greater inorganic nitrogen than the Agrostis and Festuca soil, and significantly greater soil moisture content than the Festuca soil. However, there were no differences in organic matter content, phosphate or bulk density between the soil types. In a greenhouse assay, each species was grown in soil cores from the different monocultures as well as natural turf. There were significant differences in growth between plant species and soil types. As expected, L. perenne produced the greatest amount of biomass. However, plants grown on Nardus soil were twice as large and had a 21% lower root allocation than plants grown on any of the other soil types. Lolium perenne, A. capillaris and F. ovina had significant negative growth feedbacks with their own soil (−0.460, −0.821 and −0.792, respectively) and N. stricta had a significant positive feedback (0.560). This study highlights the difficulties of predicting how plant traits will affect soil properties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".