Empirical Stream Thermal Sensitivities May Underestimate Stream Temperature Response to Climate Warming
Bibliographic record
Abstract
Abstract Stream temperature has been increasing in tandem with air temperature, with potentially negative impacts on cold‐water fish such as salmon. Assessing future stream temperature change is critical for developing effective management responses. Empirical models of stream thermal sensitivity generally predict less future warming compared to physically based models. Here we reconcile these discrepancies by using a process‐based hydrology and temperature model to simulate daily flow and water temperature for forested headwater catchments in a maritime region under both historic and projected future climatic conditions. The primary reason that the empirical approach underestimates thermal response to climate change is that it does not account for thermal memory in the catchment, especially related to the effect of snow cover. Empirical thermal sensitivities thus may underestimate stream temperature response to future climate warming. In addition, groundwater‐fed streams may only resist warming in the short‐medium term, due to lagged response of groundwater temperature. More process‐based understanding and modeling of stream thermal regimes is needed to effectively manage aquatic ecosystems.
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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Open science | 0.001 | 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".