Simulation of hydro-ecological indices in a long-term hydrologic model using downscaled climate data
Bibliographic record
Abstract
The effects of climate change are likely to have a significant impact on environmental flows, which are often represented by alterations in hydrologic and ecological indices. Changes in ow regimes caused by climate change have implications for river ecology, and projections of future ow regimes must be reliable. In this study, the performance of hydrological models was evaluated with hydro-ecological indices to determine if stream ow characteristics could be reasonably modelled with RCM (Regional Climate Model) driven data. In general, it was found that RCM driven hydrological models could well simulate ecological stream ow characteristics with seasonal or monthly bias correction. However, characteristics that represented the frequency and rate of change of stream ow were not well simulated even with bias correction. RCM data driven models resulted in comparable error to the simulation of ERSS in a regional analysis. This gave confidence to the use of RCM driven data to simulate stream ow characteristics.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".