Examples of South American coastal wetlands
Bibliographic record
Abstract
Key points Most South American wetlands are on the low-lying eastern shores, with few on the tectonically active Andean coast; there are some extensive mangrove forests, but many are reduced by aquaculture and pollution; indigenous mangal people and endemic biota are now endangered; tropical mangroves thrive on deltas and beach barriers of huge rivers, including the Amazon; subtropical wetlands grow in lagoons (‘gamboa’) with small ocean entrances and tidal creeks – these are permanently closed in Uruguay; the southernmost red, white and black mangroves are at 28.9° S; temperate coastal lagoons of Argentina have cordgrass and pickleweed in the intertidal zone and halophytic herbs in high marshes; temperate wetlands along the desert coast of Chile support beds of sea anemones and mussels. Subarctic wetlands are sparse because strong winds, ozone-hole irradiation and oil spills are added stressors. As in North America, the West and East Coasts of South America are very different from a geological viewpoint, which is reflected in the Neotropical coastal wetland distributions (Figure 8.1). Tides on both coasts are semi-diurnal or mixed and mostly of medium range (2–3 m) except for macrotidal areas in the northwest, southeast and at the mouth of the Amazon River (Eisma, 1997). The West Coast, part of the ‘Pacific Ring of Fire’, is tectonically active and bordered by the high Andean mountain ranges, which restrict the amount of lowland available for spread of intertidal wetlands. Here rivers and streams are small, wave erosion is high and earthquakes followed by tsunami waves can liquefy the marsh sediments, resulting in subsidence of up to 1.6 m over large areas (e.g. 200 km in 1979). In contrast, the East Coast passive margin is formed mainly from sedimentary basins and it has extensive low-lying plains crossed by long rivers. There are large deltas at the mouths of the Orinoco, Amazon and São Francisco rivers, which supply sediment to huge mudflats (Figure 8.2). The Amazon River is the source for vast amounts of sediment that coastal currents transport to the giant mudbanks (‘slikke’) off Surinam and French Guiana.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.033 | 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".