The Forest Watershed and Riparian Disturbance study: a multi-discipline initiative to evaluate and manage watershed disturbance on the Boreal Plain of Canada
Bibliographic record
Abstract
The Forest Watershed and Riparian Disturbance (FORWARD) initiative integrates aquatic and soil science, hydrology, and forestry into models that link water quality, water quantity, and disturbance indicators with management of watersheds on the Boreal Plain of western Canada. The impacts of varying patterns and intensities of fire and logging are being evaluated for 16 streams in the Swan Hills, Alberta, with the intention to extend the approach to the eastern portion of the Boreal Plain and into the Boreal Shield. The study uses two comparative approaches: treatment versus reference stream and before versus after disturbance. Models generated will be applied to designated multi-user watersheds in selected forest areas. In addition to yielding transferable technology for forest product industries, the FORWARD study tests hypotheses related to effects of watershed disturbance on soils, hydrology, and water quality on the phosphorus-rich and fire-prone Boreal Forest in western Canada. Key words: watershed disturbance, surface waters, soils, hydrology, modelling, forest management.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".