Characterizing the influence of forest cover changes on streamflow variability at Fishtrap Creek, British Columbia
Bibliographic record
Abstract
Modelling hydrologic recovery following a forest disturbance can assist forest managers to practice forest management while taking into account the hydrologic response of certain forest activities and disturbances. Using Vegetation Resource Inventory (VRI) data that are available on a province-wide scale, a results-based approach of looking at hydrologic recovery following a major fire was carried out on the Fishtrap Creek Watershed. Stands were modelled using the Chapman Richards growth model, and the data were predicted backwards through time for the period for which stream discharge measurements are available. The stand forest parameters are used to calculate a measure of Equivalent Clearcut Area (ECA) at the watershed scale for each year of data available. Climate variables and equivalent clear cut area were used in a regression model to separate their effects from those of a wildfire on the following streamflow metrics: timing of the onset of freshet, total freshet runoff, and the timing and magnitude of the annual peak flow. The analysis identified the timing of the onset of freshet as the most sensitive metric to forest cover change. The challenges in using currently available forest inventory data for hydrologic applications are also discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".