Quantifying bulk density of boreal peat using X-ray computed tomography
Bibliographic record
Abstract
Peatlands play a crucial role in carbon storage and climate regulation. Traditional gravity-based and loss-on-ignition methods have been widely used to acquire bulk density and thus organic carbon estimates in peat sequences. However, these methods are time-consuming, and the measurement resolution frequently ranges from half to a few centimetres, hampering the understanding of carbon accumulation history at finer temporal resolution. Here, we explore the potential of non-destructive X-ray computed tomography (XCT), a method for analyzing 3D material structure and mass density, for obtaining proxy measurements for bulk density parameters using peat cores collected in eastern boreal Quebec, Canada. We find that the Hounsfield Unit (HU) of medical XCT scans is a robust surrogate for the bulk density of wet peat (BD wet ). A universal linear model can be applied to calibrate HU values for a wide range of peat stratigraphy from different microforms: Sphagnum hummock, lichen hummock, lawn, and hollow. Moreover, HU of dry peat is indicative of both dry and organic matter bulk density (BD dry and BD om ). It is possible to develop case-specific logarithmic models to calibrate HU with BD dry . In addition, the precise measurement of the peat sample volumes using XCT suggests that traditional methods can be subject to substantial uncertainties when estimating bulk density and carbon content. Medical XCT can be applied to quantify bulk density in peat soils in a more time-efficient manner, with a resolution up to 0.6 mm, approximately equivalent to the yearly accumulation rate.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".