Ecohydrologically important subsurface structures in peatlands revealed by ground‐penetrating radar and complex conductivity surveys
Bibliographic record
Abstract
The surface pattern of vegetation influences the composition and humification of peat laid down during the development of a bog, producing a subsurface hydrological structure that is expected to affect both the rate and pattern of water flow. Subsurface peat structures are routinely derived from the inspection of peat cores. However, logistical limits on the number of cores that can be collected means that the horizontal extent of these structures must be inferred. We consider whether subsurface patterns in peat physical properties can be mapped in detail over large areas with ground‐penetrating radar (GPR) and complex conductivity by comparing geophysical measurements with peat core data along a 36 m transect through different microhabitats at Caribou Bog, Maine. The geophysical methods show promise. Peat horizons produced radar reflections because of changes in the volumetric moisture content. Although these reflections could not be directly correlated with the peat core data, they were related to the depth‐averaged peat properties which varied markedly between the microhabitats. Well‐decomposed peat below a hollow was characterized by a discontinuous sequence of chaotic wavy reflections, while distinct layering of the peat below an area of hummocks coincided with a pattern of parallel planar reflections. The complex conductivity survey showed spatial variation in the real and imaginary conductivities which resulted from changes in the pore water conductivity; peat structures may also have influenced the spatial pattern in the complex conductivity. The GPR and complex conductivity surveys enabled the developmental history of the different microhabitats along the studied transect to be inferred.
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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 | 0.001 | 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) | 0.000 | 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".