Seasonal- and Event-Scale Stream DOC Dynamics in Northern Hardwood-Dominated Headwater Catchments of Contrasting Forest Harvest History
Bibliographic record
Abstract
Forests are critical source regions of high-quality drinking water but forest disturbances such as harvesting can alter stream dissolved organic carbon (DOC) concentrations and influence source water treatability. Most stream DOC-centric forest harvesting impact studies report on effects <10 years post-harvest; less is known about the legacy effects of forest harvesting on stream DOC. Here, inter- and intra-catchment variability in stream DOC concentration and export were evaluated in two northern hardwood-dominated headwater catchments (unharvested reference and 24 years post-clearcut). The relationship between stream DOC and the concentration, spatial distribution, and hydrologic connectivity of hillslope solute pool DOC was investigated. Stream DOC concentrations in the legacy clearcut catchment exceeded those in the reference catchment for all flow conditions. Inter-catchment differences in DOC export were inconsistent. Hillslope solute pool DOC concentrations decreased with soil depth but were not significantly different between catchments. Concentration–discharge regression analysis indicated that DOC was primarily transport-limited (flushing) in both catchments. Aqueous potassium silica molar ratio data indicate the influence of groundwater on stream chemistry and streamflow was similar in both catchments. Results suggest that while clearcut harvesting can have detectable decadal-scale effects on stream DOC concentrations in northern hardwood-dominated headwater catchments, the effects are limited and likely do not pose a reasonable threat to downstream drinking water treatment operations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".