Bibliographic record
Abstract
Summary 1. The thermal regime of rivers plays an important role in the overall health of aquatic ecosystems, including water quality issues and the distribution of aquatic species within the river environment. Consequently, for conducting environmental impact assessments as well as for effective fisheries management, it is important to understand the thermal behaviour of rivers and related heat exchange processes. 2. This study reviews the different river thermal processes responsible for water temperature variability on both the temporal (e.g. diel, daily, seasonal) and spatial scales, as well as providing information related to different water temperature models currently found in the literature. 3. Water temperature models are generally classified into three groups: regression, stochastic and deterministic models. Deterministic models employ an energy budget approach to predict river water temperature, whereas regression and stochastic models generally rely on air to water temperature relationships. 4. Water temperature variability can occur naturally or as a result of anthropogenic perturbations, such as thermal pollution, deforestation, flow modification and climate change. Literature information is provided on the thermal regime of rivers in relation to anthropogenic impacts and such information will contribute to the better protection of fish habitat and more efficient fisheries management.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".