A review of ground-penetrating radar studies related to peatland stratigraphy with a case study on the determination of peat thickness in a northern boreal fen in Quebec, Canada
Bibliographic record
Abstract
Ground-penetrating radar (GPR) is a non-intrusive geophysical observation method based on propagation and reflection of high-frequency electromagnetic waves in the shallow subsurface. The vertical cross-sectional images obtained allow the identification of thickness and lithologic horizons of different media, without destruction. Over the last decade, several studies have demonstrated the potential of GPR. This paper presents a review of recent GPR applications to peatlands, particularly to determine peat stratigraphy. An example study of acquisition and comparison of peatland soil thickness of a fen-dominated watershed located in the James Bay region of Quebec, using (1) a meter stick linked to a GPS RTK and (2) a GSSI GPR, is given. A coefficient of determination ( r 2 ) of 56% was obtained between the ordinary krigings performed on data gathered using both techniques. Disparities occurred mainly in the vicinity of ponds which can be explained by the attenuation of GPR signal in open water. Despite these difficulties – the higher time required for analysis and the error margin – it seems more appropriate to use a GPR, instead of a graduated rod linked to a GPS, to measure the peat depths on a site like the one presented in this study. Manual measurements, which are user-dependent in the context of variable mineral substrate densities and with the presence of obstacles in the substrate, may be more subjective.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".