The influence of ground- and lidar-derived forest structure metrics on snow accumulation and ablation in disturbed forests
Bibliographic record
Abstract
The current mountain pine beetle infestation in British Columbia’s lodgepole pine forests has raised concerns about potential impacts on water resources. Changes in forest structure resulting from defoliation, windthrow, and salvage harvesting may increase snow accumulation and ablation (i.e., spring runoff and flooding risk) below the forest canopy because of reduced snow interception and higher levels of radiation reaching the surface. Quantifying these effects requires a better understanding of the link between forest structure and snow processes. Light detection and ranging (lidar) is an innovative technology capable of estimating forest structure metrics in a detailed, three-dimensional approach not easily obtained from manual measurements. While a number of previous studies have shown that increased snow accumulation and ablation occur as forest cover decreases, the potential improvement of these relationships based on lidar metrics has not been quantified. We investigated the correlation between lidar-derived and ground-based traditional canopy metrics with snow accumulation and ablation indicators, demonstrating that a lidar-derived forest cover parameter was the strongest predictor of peak snow accumulation (r2 = 0.70, p < 0.001) and maximum snow ablation rate (r2 = 0.59, p < 0.01). Improving our ability to quantify changes in forest structure in extensive areas will assist in developing more robust models of watershed processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| 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) | 0.000 | 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".