Change in Particle Density and Other Hydrophysical Properties With Depth in a Northern Boreal Bog Peatland
Bibliographic record
Abstract
Abstract Advances in peatland ecohydrological modelling highlight the need for higher resolution depth profiles of important soil physical properties. Peatland soil profiles exist as a continuum from Sphagnum-dominated surface cover to highly decomposed peat at depth. We determined the bulk and particle density, porosity, saturated hydraulic conductivity (Ksat), and von Post in a northern bog to a depth of ~ 200 cm in 5-cm intervals along a transect extending from a moderately treed to a more open area. The bulk and particle densities and von Post scores increased, and porosity decreased with depth. The particle density had a relatively abrupt shift near ~ 75 cm changing from ~ 0.8 g cm− 3 to a relatively consistent ~ 1.4 g cm− 3. The variability measured was small in the upper ~ 25 cm, larger at depths of ~ 25–125 cm, and became more moderate at depths > ~ 125 cm (but not particle density). The variability of bulk density at the deeper depths results in the observed variability of porosity as well. The larger variability in physical properties roughly coincides with the abrupt shift in the magnitude of measured properties suggesting that contemporary processes and/or past events (e.g., wildfire) could be responsible for this pattern. Bulk and particle density and porosity exhibited a relationship with the von Post score with an abrupt shift in values between von Post scores of 3 and 4. Detailed examination of peatland soil properties will improve the robustness and reliability of models and may reveal additional information on the history and processes of formation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".