Monitoring riparian restoration to ensure recruitment of large woody debris in Haida Gwaii, British Columbia
Bibliographic record
Abstract
Monitoring is a fundamental aspect of restoration, as it determines when the restoration objectives have been met. As restoration objectives are not universal, monitoring needs to be included in the development and design of each restoration project. We assessed the effectiveness and efficiency of a forest stand dynamic monitoring plan, developed for use with riparian restoration occurring on Lyell Island, Haida Gwaii, British Columbia. The restoration objective is to accelerate the development of late-successional forests for the benefit of riparian wildlife species and recruitment of in-stream large woody debris, which specifically provides essential habitat for a variety of fish species. In this study large woody debris (LWD) is referred to as downed wood greater than 7.5 cm in diameter. Prior to the start of riparian restoration, two watersheds were quantified for their stand structure and composition using the forest stand dynamic monitoring plan. An error analysis of these data was used to assess the sampling efficiency of the monitoring plan. The design of the monitoring plan was found to be efficient at monitoring the riparian forest stand dynamics (with seven or eight plots per site sufficient to evaluate stand basal area and stem density to within 10%), but not woody debris volumes (for which deviations >10% were found even with 14 plots per site). Incorporation of additional line transects or adoption of more efficient sampling methods for woody debris (such as diameter or length relascope methods) is suggested as a means of enhancing large woody debris sampling efficiency.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".