Defining river thermal sensitivity as a function of climate
Bibliographic record
Abstract
Abstract Water temperature is increasingly acknowledged as a key variable for the sustainable management of lotic environments. Thermal variability in rivers dictates in large part the ecosystem functions of these water bodies. River thermal sensitivity ( TS ), defined in this work as the value of the regression slope between water and air temperature ( Tair ) measurements, is often used to determine how river temperature regime is expected to vary as climate evolves. This study proposes a method to contextualize climate conditions during the river temperature‐monitoring period in order to define a common basis to compare TS values calculated for different stations distributed across various climatic regions in the province of Québec (Canada). Nine climate classes were defined based on Tair and precipitation. Annual climate conditions were classified according to their anomalies compared to a reference period (1981–2010). For the reference climate class (“Normal–Normal” conditions), results indicate contrasted summer TS values between Québec rivers and within rivers. Furthermore, observed summer TS variations between climate classes underline the need to account for climate conditions when studying spatial variations in river thermal sensitivity. Climate contextualization also allows us to study the potential role of previous conditions. Rivers were grouped according to their drainage basins characteristics to examine spatial variability in TS for the chosen reference climate conditions. River slope, station elevation and geographic location partly explained TS spatial variability. Useful proxies are needed to describe the relative contribution of groundwater to river temperature and to potentially improve the group classification results.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".