Fine‐scale influences on thaw depth in a forested peat plateau landscape in the Northwest Territories, Canada: Vegetation trumps microtopography
Bibliographic record
Abstract
Abstract The influence of vegetation and microtopography on fine‐scale variability of thaw depth is largely unknown but potentially important for improving modeling of ecosystem–permafrost interactions. To elucidate their influence, we measured tree density, shrub cover and cryptogam presence (lichen and bryophyte) on forested permafrost peat plateaus in the discontinuous permafrost zone in the southern Northwest Territories, Canada. Greater tree density was associated with shallower thaw depth (approximately one quarter of the variance), whereas shrub cover had a negligible influence on thaw depth. Cryptogam species influenced thaw depth, with greater thaw depth associated with Sphagnum than with Cladonia (a difference on the order of 10%). Greater thaw depth occurred beneath hummocks than beneath hollows (a difference also on the order of 10%). Together, canopy cover, cryptogam species and microforms contribute to a variation of roughly half the variance in thaw depth in the peat plateau landscape.
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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 | 0.001 | 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".