Management options for river conservation planning: condition and conservation re‐visited
Bibliographic record
Abstract
Summary 1. Systematic conservation planning is a process widely used in terrestrial and marine environments. A principal goal is to establish a network of protected areas representing the full variety of species or ecosystems. We suggest considering three key attributes of a catchment when planning for aquatic conservation: irreplaceability, condition and vulnerability. 2. Based on observed and modelled distributions of 367 invertebrates in the Australian state of Victoria, conservation value was measured by calculating an irreplaceability coefficient for 1854 subcatchments. Irreplaceability indicates the likelihood of any subcatchment being needed to achieve conservation targets. We estimated it with a bootstrapped heuristic reserve design algorithm, which included upstream–downstream connectivity rules. The selection metric within the algorithm was total summed rarity, corrected for protected area. 3. Condition was estimated using a stressor gradient approach in which two classes of geographical information system Layers were summarised using principal components analysis. The first class was disturbance measures such as nutrient and sediment budgets, salinisation and weed cover. The second class was land use layers, including classes of forestry, agricultural and urban use. The main gradient, explaining 56% of the variation, could be characterised as agricultural disturbance. Seventy‐five per cent of the study area was classified as disturbed. 4. Our definition of vulnerability was the likelihood of a catchment being exposed to a land use that degrades its condition. This was estimated by comparing land capability and current land use. If land was capable of supporting a land use that would have a more degrading effect on a river than its current tenure, it was classified vulnerable (66% of the study area). 79% of catchments contained more then 50% vulnerable land. 5. When integrating the three measures, two major groups of catchments requiring urgent conservation measures were identified. Seven per cent of catchments were highly irreplaceable, highly vulnerable but in degraded condition. These catchments were flagged for restoration. While most highly irreplaceable catchments in good condition were already protected, 2.5% of catchments in this category are on vulnerable land. These are priority areas for assigning river reserves.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.063 | 0.004 |
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".