Are temperature time series measured at hydrometric stations representative of the river’s thermal regime?
Bibliographic record
Abstract
River temperature is a key variable for water quality assessment. It can alter different chemical water properties. Indeed, it is considered as determining criterion in the adequacy of cold water fish habitat, and the overall health of the river ecosystem and aquatic biota. Consequently, monitoring this variable and understanding the river thermal variation are highly important. Temperature monitoring along the rivers is often done by deploying autonomous temperature loggers. However, recently, temperature sensors were installed at hydrometric stations in conjunction with water level gauges for monitoring the river temperature, thereby providing an opportunity to expand the temperature network across the region and eventually, the country. In this study, a comparative analysis was conducted to find if the temperatures at the hydrometric station are representative of the river thermal variation upstream and downstream of that location. This comparative analysis was completed using a number of different statistical tools: entropy analysis, Gaussian function fit, and thermal sensitivity analysis. These statistical analyses confirm that temperature loggers that are collocated with the level gauges at hydrometric stations are generally representative of the thermal variation of the river main stem over a distance of a few tens of kilometres. However, the thermal variation observed in temperature loggers located at distances of the order of 100 km or in a different river reach than the hydrometric station, is different from that of the temperature logger located at the hydrometric station.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".