Areal differentiation of snow accumulation and melt between peatland types in the James Bay Lowland
Bibliographic record
Abstract
Abstract Snow accumulation and melt between various peatland types in the James Bay Lowlands is poorly understood despite being a significant source of fresh water to the saline James Bay. Many topographical factors that control snow accumulation and melt (e.g. slope, aspect) are not relevant in the James Bay Lowlands because of the extremely low relief. Thus, vegetation characteristics (e.g. winter leaf area index, tree density), which are strongly linked to peatland type, may dictate spatial patterns of snow accumulation and melt across the landscape. A 1.5‐km long transect that bisected five peatland types representative of the local area was used to determine average snow depth, density and water equivalence for each of the landscape units. The peatland types were classified, in part, because of the density of treed vegetation and were named open bog, open shrub fen, low‐density treed fen, medium density treed bog and high‐density treed fen. Those with medium or high‐density treed vegetation accumulated significantly more snow than those with low or open densities. Snow density, however, showed no correlation with landscape unit, and snowmelt proceeded at similar rates between all landscape units because of the relatively open canopy typical of this environment. A randomization test showed that the areally weighted basin average snow depth estimates varied by less than 10 cm as a result of the small but statistically significant differences in snow accumulation among landscape units. These differences are therefore relatively unimportant for accurately quantifying basin‐wide snow depth in this landscape. Copyright © 2012 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".