Hydrology Predominates Over Harvest History and Landscape Variation to Control Water Quality and Disinfection Byproduct Formation Potentials in Forested Pacific Coast Watersheds
Bibliographic record
Abstract
Despite the global importance of forested watersheds as sources of drinking water, few studies have examined the effects of forestry on drinking water treatability. Relatively little is known about how the interaction between landscape variation and flow impacts source water quality and what this interaction means for drinking water treatability. To address this knowledge gap, we examined variability in sediments, dissolved organic matter, and disinfection byproduct formation potentials (DBP-FPs) across a range of flow conditions in four small watersheds with contrasting forest harvest histories and soil characteristics on Vancouver Island. Storm event-driven change in streamflow was the primary driver of water quality and DBP-FPs at our sites, with greater changes during stormflow (e.g., a 3-fold increase in dissolved organic carbon concentrations) than those across contrasting watersheds. Flow-driven changes in water quality and DBP-FPs were not significantly different across watersheds with different harvest histories; muted responses may be attributed to widespread second growth forests (i.e., recent harvesting effects may be confounded by historical harvest), forestry practices (e.g., slash burning), or soils with low organic carbon storage. This study suggests that variation in hydrology predominates over harvest history and soil characteristics to drive water quality and DBP-FPs on the east coast of Vancouver Island.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.001 |
| 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 teacher head, 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".