A dynamic dendritic connectivity assessment tool for the planning and design of barrier mitigation strategies in river networks
Bibliographic record
Abstract
Abstract Context Increasing fragmentation of rivers caused by barriers continues to impact watersheds, especially disruption of fish migration patterns and loss of access to spawning and nursery habitats. Infrastructure expansion and ageing installations exacerbate the problem, reducing effectiveness of management in addressing barriers. Reduction in watershed connectivity requires effective tools capable of guiding river managers in remediation actions. Objectives Our objective was to develop a watershed dynamic connectivity assessment tool (D-CAT), a customizable geospatial tool capable of river analyses at varying watershed scales. The tool uses proven watershed connectivity modelling combined with an effective evaluation approach to provide measurable change model outcomes. Methods Three different barrier encounter scenarios were examined using the D-CAT tool to study watershed connectivity. The tool was tested on a watershed area of the River Derwent, a major tributary of the River Trent system in the United Kingdom. Results For each barrier scenario tested, the D-CAT tool indicated which barriers could be removed or modified to provide the greatest watershed connectivity improvement. The tool provided a prioritized list of barriers for each scenario under varying choices for the number of barriers to remove, demonstrating the utility of the D-CAT tool to planning and design. Conclusions The D-CAT tool is built to handle geographical data and variable user input, allowing in-depth watershed connectivity analysis. The tool forecasts the cumulative effects of alternative change scenarios for watershed connectivity, providing prioritization and optimization of removal strategies. The D-CAT tool offers significant support for landscape management towards better barrier removal/remediation decisions.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".