Runoff and phosphorus export patterns in large forested watersheds on the western Canadian Boreal Plain before and for 4 years after wildfire
Bibliographic record
Abstract
We examined water and phosphorus export patterns in four large (130 to 247 km 2 ) Boreal Plain watersheds (two burned and two reference) before and for 4 years after wildfire and the influence of precipitation intensity and timing on these patterns. Time series analysis of the one burned and one reference watershed monitored before and after fire demonstrated that relative to changes in the reference watershed over the same time period, runoff and dissolved and particulate phosphorus exports were higher in the burned watershed during the four post-fire years than before the fire (P = 0.001). Comparison of post-fire means in all four watersheds monitored for the post-fire years demonstrated that mean water and particulate phosphorus exports were 1.6 (P = 0.01) and 3.7 (P = 0.03) times higher in burned than reference watersheds, respectively. A similar pattern existed for dissolved phosphorus exports, but differences were not significant (P = 0.13). Thus, the pre- vs. post-fire comparison was consistent with, and more powerful than, the post-fire treatment vs. reference comparison. As of year 4 post-fire, burned watersheds continued to export more water and particulate phosphorus per unit area than reference watersheds, particularly during peak flow periods. Key words: watershed disturbance, boreal forest, stream, water quality, fire, phosphorus, runoff.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".