Introduction: what is covered in this coastal wetlands book?
Bibliographic record
Abstract
Coastal wetlands, including tidal salt marshes and mangrove swamps, are environmentally stressful and variable habitats, and yet are teeming with life. Their biological productivity exceeds that of coral reefs and matches that of tropical rainforests. These wetlands provide resident plants and animals with shelter, food and continuous renewal of nutrients on each tidal cycle. These coastal wetlands are also vital to neighbouring ecological communities and have important values to humans, serving as natural carbon-capture systems, as sources of oceanic ‘blue carbon’, as filters of sediment or nutrient-loaded flood water and as buffers against storm tides and rising sea levels. Concern about the twentieth-century destruction of many wetlands in North America, Europe, Australia and New Zealand, and the degradation of wetlands worldwide led to the 1971 Ramsar Convention on Wetlands, held on the shore of the Caspian Sea in Iran (see Box 1.1 The Ramsar Convention). The Convention provides foundations for planning of ‘wise use’ for all wetlands; preservation of wetlands with international importance for ecology, biodiversity or hydrology; and co-operative protection of internationally shared species. Coupled with this landmark environmental agreement, the United Nations designated 2 February as ‘World Wetlands Day’, bracketing it with programmes to raise awareness of the strong link between global freshwater supplies and wetland resources. These two themes ‘Water’ and ‘Wetlands’ highlight global efforts to promote understanding that without coastal wetland conservation, there will not be enough water for sustainable development, human health and, ultimately, the survival of humankind.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".