Causes of degradation and erosion of a blanket mire in the southern Pennines, UK
Bibliographic record
Abstract
This study investigates the causes of erosion and degradation of March Haigh, a blanket mire in the southern Pennines (UK), over a period of 160 years starting in 1840 AD. Peat samples taken from the site were dated using 210 Pb; their humification and magnetic susceptibility were measured; and they were examined for pollen, plant macrofossils and microscopic charcoal. Stratigraphic correlation with a dated ‘master’ sample was achieved using indicators of air pollution (magnetic susceptibility) and climate (peat humification). The data were used in conjunction with documentary records to reconstruct past variations in grazing pressure, climate, moorland fires and air pollution. Three major vegetation changes have occurred on the moorland since 1840, namely: 1. 1) the disappearance of Sphagnum spp. in the mid 19th century; 2. 2) the replacement of Calluna vulgaris by Poaceae as the dominant vegetation type ca. 1918; and 3. 3) a reduction in vegetation cover and consequent erosion ca. 1959. The results concur with the findings of other investigations of ecological change in the southern Pennines insofar as degradation of vegetation prior to the mid 20th century appears to have been caused by air pollution, climate change and fire. Following the removal of vegetation by a severe fire during the summer of 1959, unprecedented sheep stocking levels maintained the bare peat surface and thus precipitated extensive erosion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".