Projected river water temperatures in Poland under climate change scenarios
Bibliographic record
Abstract
70 Polish rivers in Central Europe. This study projects river water temperatures (RWTs) of 70 Polish rivers (125 gauges) under two Shared Socioeconomic Pathway (SSP245 and SSP585) using the ensembled data of 10 CMIP-6 climate models till 2100. The results show that choice of the climate models significantly impacts the projection of RWTs, suggesting the use of ensembles to reduce the uncertainty brought by the individual climate models. For both scenarios, the projected annual averaged RWTs in all gauges increase significantly, and rivers warm at an average decadal rate of 0.14 and 0.36 °C for SSP245 and SSP585, respectively. Irrespective of which scenario, in the future, autumn RWTs tend to increase the fastest, followed by summer and winter, then spring. As for one of the most important biologically relevant metrics, namely the annual number of days when RWTs exceed 20 °C (D 20 ), the results show that for both scenarios, D 20 increases for majority of river stations, with an average decadal rate of 3.58 and 7.53 days for SSP245 and SSP585, respectively. Our results suggest that climate protection measures play an important role in mitigating the impact of climate change on river warming and should be taken immediately. • Water temperatures of 70 Polish rivers covering 125 river gauges are projected. • Choice of climate models significantly impacts the projection of river water temperatures. • Rivers warm at an average rate of 0.14 and 0.36 °C decade −1 for SSP245 and SSP585. • D 20 increases at an average rate of 3.58- and 7.53 days decade −1 for SSP245 and SSP585.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".