Detecting changing river temperatures in England and Wales
Bibliographic record
Abstract
Abstract Changes in water temperature can have important consequences for aquatic ecosystems, with some species being sensitive even to small shifts in temperature during some or all of their life cycle. While many studies report increasing regional and global air temperatures, evidence of changes in river water temperature has, thus far, been site specific and often from sites heavily influenced by human activities that themselves could lead to warming. Here we present a tiered assessment of changing river water temperature covering England and Wales with data from 2773 locations. We use novel statistical approaches to detect trends in irregularly sampled spot measurements taken between 1990 and 2006. During this 17‐year period, on average, mean water temperature increased by 0.03 °C per year (±0.002 °C), and positive changes in water temperature were observed at 2385 (86%) sites. Examination of catchments where there has been limited human influence on hydrological response shows that changes in river flow have had little influence on these water temperature trends. In the absence of other systematic influences on water temperature, it is inferred that anthropogenically driven climate change is driving some of this trend in water temperature. © 2014 The Authors. Hydrological Processes published by John Wiley & Sons Ltd.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".