Estuaries, coastal marshes, tidal flats and coastal dunes
Bibliographic record
Abstract
Introduction For millennia people have valued coastal environments for their rich soils, harvestable food resources and access to the oceans. From ancient times to the present, cities and ports have flourished at the coast and this value continues into the twenty-first century. With globalisation and international trade becoming central to many world economies, coastal populations have continued to grow. In 2003, in the USA approximately 153 million people (53% of the population) lived in coastal counties, an increase of 33 million people since 1980 (Crossett et al ., 2004). By the year 2008, coastal population in the USA is expected to increase by approximately 7 million. Eight of the world ' s top ten largest cities are located at the coast. According to the UN Atlas of the Coast (www.oceansatlas.org/), 44% of the world ' s population (more people than inhabited the entire globe in 1950) live within 150 km of the coast and in 2001 over half the world' s population lived within 200 km of a coastline. Coastal cities also have higher rates of growth than many other areas. Clearly the massive population now existing along the world coast and the rapid growth it is experiencing induce a major stress on the local and regional geomorphology as well as on the local resource base. The importance of coastal areas is intimately linked to their geomorphic character and setting. Many early settlements (e.g. the city of Troy mentioned in the Iliad) and later industrial cities (e.g. Shanghai) are located at river mouths where flat land, ample fresh water and transportation link-ages to both inland and overseas have facilitated urban growth. Port cities developed on naturally deepwater but somewhat sheltered inlets and estuaries, e.g. Singapore, Seattle and Rotterdam, take advantage of both their coastal setting and their linkage to other continents and oceans.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.010 |
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".