Aquatic areas of ecological importance as inputs into surface water resource protection areas in Zambia
Bibliographic record
Abstract
Abstract Although the network of national parks in Zambia offers a degree of protection for freshwater diversity, the protection status of numerous systems outside of these parks requires further action. The biodiversity associated with its freshwater systems, both lotic and lentic, is unique, covering a climatic gradient from tropical to subtropical across the Zambezi and Congo basins. Recent Zambian legislation allows for the delineation of water resource protection areas (WRPAs), with one of the criteria being that they include aquatic areas of ecological importance (AEIs). In this study, a systematic conservation planning approach was used to identify aquatic AEIs objectively. Importantly, the approach included a rigorous and iterative stakeholder engagement and review process. The conservation planning software marxan was chosen because of its ability to integrate upstream–downstream connectivity. In total, 5,671 planning units (sub‐catchments with an average area of approx. 14,000 ha) were populated with 77 biodiversity features: data were drawn from a wide range of sources, and included fishes, semi‐aquatic mammals, molluscs, amphibians, and ecotonal physiographic features, such as waterfalls. Sub‐catchments were preferentially chosen using a combination of area‐ and distance‐weighted boundary costs. The final solution highlights critical clusters in each of the major freshwater ecoregions in Zambia, with all conservation targets being met. Results show that although the existing protected area network also coincides with identified aquatic AEIs, approximately 80% of all aquatic AEIs fall outside of formally protected areas. The outcomes of this process serve as one of three prioritization layers (the other two being water provision and sensitivity to human impacts) that are integrated in a larger study to select and prioritize WRPAs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".