Divergent options to cope with vulnerability in subsiding deltas
Bibliographic record
Abstract
Net subsidence of most major deltas in the world and related vulnerability are thought to be increasing, and this is often linked causally to human activities. This paper examines this causality against a range of co-varying factors. We do so with a principal component analysis of co-variability of a range of geophysical and socio-economical indicators of 33 deltas mainly derived from the DIVA tool. Land potentially lost and people at risk of flooding are our indicators of vulnerability. The former correlated positively with maximum surge height and negatively with net sea level rise. The latter correlated positively with delta area, average river discharge, and maximum surge and negatively with net uplift (or subsidence). Thus, variation in societal vulnerability across deltas depends on short-term, instantaneous risks linked to lowland area, river discharge and storm surges rather than on longer-term, slow, net sea level rise. Delta management should focus on precautionary spatial planning, and on maintenance or restoration of historical sediment delivery and accretion rates. Especially larger deltas with high population densities combine a high risk with the potential to accommodate flood water and mitigate flooding risks. The deltas of the Yangtze-Kiang and Ganges-Brahmaputra share these characteristics. Here space should allow engineering of flood retention, sedimentation and diversion channels as well as refuges and safe economic hotspots. At the other end, in deltas with a high population density and limited space, like the Chao Praya, means for adaptation must be sought outside the delta proper. In deltas with low population densities, such as the Lena, Yukon or Fly, natural delta dynamics can prevail.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".