Bibliographic record
Abstract
Coastal Wetlands of the World follows the book by Scott, Medioli and Schafer (2001) on Monitoring in Coastal Environments . We are motivated to write this new book based on concern about the status of mangroves and salt marshes all over the world, from pole to pole, and by the fact that few students have the chance to look at our changing shorelines from both a geological and an ecological perspective. Coastal wetlands are being destroyed and degraded at alarming rates, and only a fraction remains. These wetlands protect us from storm buffering and have extremely high primary production, making them important storehouses of carbon and energy, habitats that nurture juvenile stages of commercially important fishes and that filter our waste water – yet we continue to damage them faster than we can preserve them. In some areas, less than a third of natural wetlands remain along the coast, and very few are entirely unaffected by direct human impacts. Furthermore, all our coastal wetlands are changing in response to indirect human impacts: global warming, sea level rise and increasing numbers of severe coastal storms. These impacts are further magnified in the Arctic, where the pace of climate warming is four times faster than other places on Earth, and where disappearing sea ice is encouraging rapid expansion of oil and gas exploration, with the associated risks of long-lasting pollution damage. Arctic people say that ‘The Earth is faster now’ – and it appears that traditional methods of coastal living are no longer viable. It is likely that circumpolar regions are already irreversibly changed – and the spill-over impacts on global air and ocean systems is already being felt by people in crowded cities of warm temperate regions.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.663 | 0.514 |
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".