New policy directions for global pond conservation
Bibliographic record
Abstract
Abstract Despite the existence of well‐established international environmental and nature conservation policies (e.g., the Ramsar Convention and Convention on Biological Diversity) ponds are largely missing from national and international legislation and policy frameworks. Ponds are among the most biodiverse and ecologically important freshwater habitats, and their value lies not only in individual ponds, but more importantly, in networks of ponds (pondscapes). Ponds make an important contribution to society through the ecosystem services they provide, with effective conservation of pondscapes essential to ensuring that these services are maintained. Implementation of current pond conservation through individual site designations does not function at the landscape scale, where ponds contribute most to biodiversity. Conservation and management of pondscapes should complement current national and international nature conservation and water policy/legislation, as pondscapes can provide species protection in landscapes where large‐scale traditional conservation areas cannot be established (e.g., urban or agricultural landscapes). We propose practical steps for the effective incorporation or enhancement of ponds within five policy areas: through open water sustainable urban drainage systems in urban planning, increased incentives in agrienvironment schemes, curriculum inclusion in education, emphasis on ecological scale in mitigation measures following anthropogenic developments, and the inclusion of pondscapes in conservation policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.071 | 0.005 |
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".