Enhancing bioassessment approaches: development of a river services assessment framework
Bibliographic record
Abstract
There has been a trend toward increasing anthropocentrism in definitions of river health through the explicit inclusion of societal valuation of ecosystem services provided by rivers. New frameworks and associated indicators of river health are therefore required to centralize ecosystem services within river assessment and management activities. Here, we adopt an anthropocentric conceptualization of rivers to focus on a river’s ability to maintain ecological function and structure that support ecosystem services valued by society. We apply this approach to further existing conceptual models of river assessment by identifying how benthic indicators can be linked to valued ecosystem services in a river services assessment framework. This approach extends bioassessment from a focus on assessing departure from reference condition to also include the evaluation of rivers based on their delivery of ecosystem services. Indicators based on benthic processes and assemblages are widely used in river health assessments; thus, these are reviewed to identify those indicators most closely linked with the provision of river ecosystem services. Finally, we illustrate how our approach can be applied to management through contrasting watershed examples, including a highly modified agricultural region and relatively pristine Arctic watersheds. The proposed approach supports an explicit connection between valued ecosystem services and benthic indicators, providing more targeted assessment results for use in river management decision-making.
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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 | 0.027 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".