Global changes, mangrove forests and implications for hazards along continental shorelines.
Bibliographic record
Abstract
Coastal zones are becoming more hazardous due to increased population and greater susceptibility to flooding and salinization, as a result of compaction and subsidence, surface sealing and loss of buffer zones between the coast and settled areas. These conditions are exacerbated in the developing (largely equatorial) world, where rates of population increase are highest and protection schemes and mitigation strategies among the most poorly advanced. The major climate change threat to coastal forest environments is the projected increase in sea level and associated vulnerability of coastal land and inhabitants. In many places, existing coastal forests will be squeezed between rising sea levels and agricultural land, while farmers will have to contend with higher instances of damaging floods and increased salinity. The absence of adaptation strategies, such as the adoption of salt-tolerant crops and associated technologies, will force the abandonments of formerly productive land. Displacement of people will increase population pressures inland. The prognosis for forested coastlines in equatorial regions is poor and is unlikely to improve while coastal environments remain locations for the juxtaposition of dynamic natural environments with relatively static, ecologically disruptive, economic activities. Mangrove forests face the combined threat of global change-induced variations in environmental conditions and clearance and over-exploitation by humans. By degrading mangrove forests, however, humans will remove one of the few effective means of protecting coastal areas from the impacts of global change. Where removal of coastal forests proceeds along with the construction of new drainage channels and the canalization of existing rivers, impacts of flood surges and intrusions of saline water are likely to be felt sooner and over larger areas than on undisturbed coastal plains. Human activity may therefore enhance the possibility of catastrophic events in the future and, therefore, their own vulnerability to cumulative hazards.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".