Considering temporal flow variability of non-perennial rivers in assessing ecosystem service provision
Bibliographic record
Abstract
The basis for the assessment of ecosystem services (ES) of non-perennial rivers to date has been a comparison of the ES provision among three different hydrological phases: flowing conditions, isolated pools, and dry streambeds. Being of an opinion that this practice might promote an incomplete and hence biased ES assessment, we propose two considerations for the ES assessment of non-perennial rivers. First, the conditions of each hydrological phase can vary based on the nature of multiple aquatic states. Second, the duration, frequency, timing, and magnitude of the aquatic states matter in the ES provision. Different scenarios of flow regime should be compared instead of hydrological phases in ES assessments. Our proposal sheds some light on the complexity of non-perennial rivers and allows for a better understanding of relationships between non-perennial rivers and society. Therefore, it can serve as the basis for the proper and participatory ES assessment, face for trade-offs between ecosystem conservation and resource use and reduce conflicts among stakeholders within river and water management.
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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 | 0.007 |
| 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 | 0.001 |
| 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".