Vertical Crustal Movement along the Coast of South Africa
Bibliographic record
Abstract
Abstract. This study provides an in-depth evaluation of sea level rise (SLR) and its varied effects across the coastal regions of southern Africa. Utilizing data collected between 1993 and 2022, we analyze SLR patterns alongside land subsidence phenomena, based on observations from 10 strategically located tide gauges and X-TRACK satellite altimetry datasets. To ensure greater accuracy, the Coastal Altimetry Approach was adopted to refine nearshore measurements. Findings indicate that in areas such as Cape Town, sea-level rise rates reach around 6.3 mm/year, which is nearly twice the current global average of 3.3 mm/year. The interaction between rapid sea-level rise and subsidence rates surpassing 2.2 mm/year presents significant threats to coastal communities, critical infrastructure, and natural ecosystems. Moreover, the study highlights how seismic activity contributes to coastal dynamics, illustrating the role of earthquake-induced subsidence in magnifying the impacts of SLR. By incorporating seismic factors into the analysis, a more comprehensive understanding of the interplay between natural and human-induced drivers of sea-level variability is achieved. Additionally, the study examines the broader effects of SLR on Africa’s culturally and historically important coastal heritage sites, emphasizing the urgent need for proactive coastal management and climate adaptation efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".