Investigating hydrological recovery in regenerating coniferous stands in snow‐dominated watersheds using <scp>simultaneous localization and mapping</scp>‐enabled mobile terrestrial <scp>LiDAR</scp>
Bibliographic record
Abstract
Abstract The return of snow accumulation and ablation processes in regenerating forests to pre‐disturbance conditions, collectively referred to as hydrological recovery, has been investigated in past decades through manual snow surveys in adjacent open, juvenile, and mature stands. The outcomes of such studies provide a general understanding of hydrological recovery but lack transferability to areas where stand structure and terrain conditions differ from the reference sites. The application of mobile terrestrial LiDAR to investigate peak snow water equivalent (SWE) and ablation rates beneath regenerating trees in a space‐for‐time substitution study design provides new insights on the process of hydrological recovery in snowmelt forests of British Columbia, Canada. Outcomes of this study better quantify the influence of tree growth on peak SWE and ablation rate at both the tree and stand level for north aspect mixed conifer stands. Recovery of these two processes differ with recovery of Peak SWE beginning when the trees in a stand reach 3 m in height and recovery of ablation rates beginning once trees reach 5 m in height. Additionally, the process of negative ablation recovery in early juvenile stands reported in previous studies is herein clearly observed, providing an improved understanding of forest canopy effects on hydrological recovery in juvenile stands. The methods used in this study, which are internationally applicable, increase transferability of outcomes to stands where canopy characteristics (i.e., height, crown cover, and heterogeneity) are not represented in reference sites.
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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".