Assessing stream temperature response and recovery for different harvesting systems in northern hardwood forests using 40 years of spot measurements
Bibliographic record
Abstract
Abstract Stream temperature is a critical control on aquatic habitat and a key forest management concern in many jurisdictions. Most research on stream temperature response to forest harvesting is from coniferous forests in rain‐dominated watersheds and focused on the first few years following harvesting. In contrast, we know less about the harvesting impacts on stream temperature for silviculture approaches typically used in northern hardwood forests that are influenced by snow. We addressed this knowledge gap by using four decades (1980 to 2020) of spot water temperature measurements recorded at three treatment and two reference catchments (areas 4.5 to 69 ha) as part of a long‐term water quality monitoring programme at the Turkey Lakes Watershed study near the eastern shores of Lake Superior. We were able to control for diel and seasonal biases in the spot temperature measurements and found that clearcut harvesting showed a summer temperature increase that persisted for 5 to 7 years after harvesting. Shelterwood and selection harvest did not exhibit a detectable change in stream temperature. These responses are consistent with observed changes in forest canopy through time and between harvesting approaches. In addition, the stream temperature responses were likely muted due to the streams being short and characterized by intermittent flow conditions, as well as the potential moderating influence of increased subsurface runoff following harvesting. Our results highlight how insights can be extracted from routine water quality programmes that were hitherto unrecognized.
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 | 0.001 | 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 | 0.000 | 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".