Do peatland microforms move through time? Examining the developmental history of a patterned peatland using ground‐penetrating radar
Bibliographic record
Abstract
Using ground‐penetrating radar (GPR) to map subsurface patterns in peat physical properties, we investigated the developmental history of meso‐scale surface patterning of microforms within a raised bog. Common offset GPR measurements were obtained along a 45‐m transect, at frequencies ranging from 100 to 900 MHz. We found that low‐frequency (central frequency < 240 MHz) GPR could not adequately represent the subsurface structures of the peatland because individual peat layers were too thin. However, more detailed high‐frequency measurements (central frequency ≥ 240 MHz) showed a striking pattern of subsurface reflections that dip consistently in a northerly direction. The angle of these dipping reflectors is calculated using a semblance algorithm and was shown to average 3.9° between a depth of 1.0 and 2.5 m. These dipping reflectors may indicate downslope migration of surface microforms during the development of the peatland. Based on the estimated angle and the rate of peat accumulation, the average rate of downslope propagation of these surface microforms is calculated at 9.8 mm per year. Further survey work is required to establish whether the downslope migration is common across the peatland.
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 | 0.001 | 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".