Mycorrhizal colonisation in roots of <i>Holcus lanatus</i> (Yorkshire Fog) in a permanent pasture under conditions of reduced precipitation
Bibliographic record
Abstract
The UK climate is projected to become warmer, with more frequent hotter, drier summers. Many governments and international organisations are concerned about how climate change will affect food production and security. Mycorrhizal fungi are an essential part of agricultural systems and yet little is known about how climate change will affect mycorrhizal fungi. We investigated the effect of reduced precipitation on levels of arbuscular mycorrhizal (AM) colonisation in the top 10 cm of soil in the grass Holcus lanatus L. (Yorkshire Fog) in a permanent pasture in South Gloucestershire, UK. Incident rainfall was reduced, by approximately 50%, using clear gutters supported on steel frames. Over three growing seasons we observed little difference in levels of AM colonisation and numbers of intra-root fungal structures between the roots of H. lanatus grown with reduced or full incident rainfall. Time of year when water stress occurred had a stronger effect on levels of colonisation than the absolute amount of precipitation received. In H. lanatus, growing in a permanent pasture, levels of AM colonisation were around 40%–50%, across a range of precipitation, from 18% above to 36% below the long-term average. The results highlight the complex relationships between mycorrhizal fungi, host plant, and abiotic stress.
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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 | 0.001 | 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".