Snowpack Characteristics following Wildfire on a Simulated Transport Corridor and Adjacent Subarctic Forest, Tulita, N.W.T., Canada
Bibliographic record
Abstract
A simulated transport corridor research site established in a permafrost-affected upland black spruce forest in 1984 was burned by a wildfire in 1995. A data set consisting of four mid-winter snow surveys conducted prior to the fire was supplemented by two postfire surveys (including a new, unburned forest control). Mean daily winter winds increased significantly on the cleared rights-of-way/ simulated pipeline trench (ROW/trench). Increased winds were attributed to postfire reductions in stem density and surface roughness, which offered less resistance to the wind. Maximum winter winds were not significantly greater at 1.5 m on the postfire ROW/trench where the postfire modification of the forest reduced funnelling and acceleration of winds along the cleared ROW/trench. ROW/ trench snowpack depth increased and water equivalence declined after the fire, except in leading-edge forest sites where the reverse occurred. Depth and water equivalence changes resulted from the altered wind regime, where reduction in effective wind erosion on ROW/trench locations left more snow in these areas and reduced the amount available for deposition in leading-edge forest sites. The postfire snowpack was generally less dense on the anthropogenic disturbances, and with greater insulation potential, less heat loss should occur through the snowpack. The postfire forest snowpack was unchanged in comparison to prefire characteristics which suggests that thaw season conditions have the greatest effect on active layer thickening and surface subsidence following wildfire.
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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 | 0.001 |
| 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) | 0.001 | 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".