Human intervention causing coastal problems
Bibliographic record
Abstract
Key points The location of coastal wetlands on deltas, estuaries and lagoons make them targets for landscape alteration by dredging, shipping and air industries; land ‘reclamation’ for agriculture, aquaculture, urban development and tourism has transformed ~30% of the world’s wetlands; population growth, rising sea level, dams and soil desiccation increase wetlands flooding from higher water levels and increased storminess; shrinking Arctic sea ice, permafrost and glacier melting increase erosion, adding to greenhouse gases and change ocean–atmosphere circulations pole-to-pole; replacing salt marsh and mangroves by landfill removes natural shoreline protection, but artificial barriers create worse erosion; attempted wetland recolonization often fails because introduced species are invasive; drainage to control mosquitos and tropical diseases changes wetland productivity; pollution from nitrogen loading and oil spills cause long-lasting damage, up to >30 years. Human population growth and landscape alteration Anthropogenic impacts on coastal wetlands include landscape alteration and reclamation of tidal wetlands, accelerated climate warming and sea level rise, spread of alien plant and animal species, construction of dams, draining of tidal wetlands and discharge of pollutants – deliberately or accidentally. Coastal wetlands are particularly vulnerable to the impacts of sea level rise (Cahoon et al ., 2006). In the Stern Review of the Economics of Climate Change (Stern, 2007), the costs of future coastal flooding are projected as around US$7.5 to $11 billion per decade for Europe and North America, respectively. Syvitski et al . (2009) have shown that 17 of the world’s largest deltas (Table 5.1) are critically vulnerable to being flooding and converted to open ocean. In the past decade, 85% of these deltas experienced severe flooding, with a total area of 260 000 km 2 being temporarily submerged. The Syvitski team estimate that the area vulnerable to flooding could increase by 50% under projected values for twentieth-century sea level rise. In contrast, other models predict that increased storminess will transport more sediment into coastal wetlands and enable salt marshes to keep pace with sea level rise (Schuerch et al ., 2013).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.060 | 0.007 |
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".