Quickflow response to forest harvesting and recovery in a northern hardwood forest landscape
Bibliographic record
Abstract
Abstract Forest harvesting often increases catchment quickflow ( QF , water delivered rapidly to the stream channel), a metric of high‐flow events controlling a catchment's solute and sediment export. Nevertheless, our understanding of QF responses to various silvicultural strategies (e.g., clearcutting, selection harvest, and shelterwood harvest) is incomplete. We present a 31‐year examination of QF delivery from treatment (clearcut, selection harvest, and shelterwood harvest) and control catchments in a deciduous forest landscape in central Ontario, Canada. Growing season root‐zone storage capacity was estimated using a water balance approach to evaluate temporal changes in QF response to precipitation ( P ) for pretreatment and posttreatment periods. Threshold relationships between QF and P were assessed for control and treatment catchments for pretreatment and posttreatment periods using piecewise regression. Root‐zone storage capacity demarcated shifts in the hydrologic regime arising from forest harvesting and subsequent regeneration. This was particularly pronounced for clearcutting where postharvest decline in root‐zone storage capacity was followed by a rise to preharvest values. Similar pretreatment threshold relationships between QF and P , and near‐identical P thresholds for producing significant QF , reflected similar soil and overburden depths in the catchments. Harvesting effects were indicated by increases in QF / P ratios for relative small P and the number of P events that generated QF , thus changing treatment QF vs. P threshold relationships. Prior to harvesting there was no significant increase in QF with P below a threshold P of 35–45 mm; however, there was a significant QF vs. P relationship below this threshold for all treatments postharvest. Clearcutting increased the number of QF events for the entire postharvest period and the first 9‐year postharvest compared to the other treatments; nevertheless, evidence for intertreatment differences in total QF depth delivered from the catchments during the growing season was inconclusive. Our work suggests that changes in threshold relationships between QF and P , coupled with knowledge of the physical processes underlying them, are useful when evaluating hydrologic responses to forest harvesting.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".