Impact of wildfire on discharge and phosphorus export from the Sakwatamau watershed in the Swan Hills, Alberta, during the first two years
Bibliographic record
Abstract
Water and phosphorus (P) exports during the peakflow season increased after a fire in early summer 1998 burned 89% of an upland watershed on the Boreal Plain of western Canada. The change in water export between pre- (1983) and post-fire (1998 to 2000) periods was higher in the burned (4th order) than reference stream (3rd order) (P = 0.01). Burned to reference stream ratios of particulate P (PP) flow-weighted mean concentrations (FWMC) and export were 1.5 and 2.8, respectively, in year 1 and 2.8 and 6.7, respectively, in year 2 post-fire. Particulate P comprised a similar proportion of total P export in the burned stream before fire and in the reference stream (65%), but a higher proportion after fire during the peakflow season only (77%) (P < 0.02). Phosphorus concentration and discharge (Q) were positively related in both streams, across all Q intensities measured in the case of dissolved P, but only at Q > 1.5 m3 s1 for PP. Changes in P export after fire were evident during peakflow and were largely restricted to the PP fraction. These changes appear to be driven by higher discharge, which enhanced loading of P-rich particulates from the watershed and (or) from the stream channel. Key words: watershed disturbance, watershed management, stream, water quality, fire, phosphorus, discharge.
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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 | 0.000 | 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) | 0.001 | 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".