Land subsidence dynamics and their interplay with spatial and temporal land-use transitions in the Douala coastland, Cameroon
Bibliographic record
Abstract
Abstract. The Douala coastland (DCL), situated within the Douala sedimentary basin along the Gulf of Guinea, is characterised by its low elevation and alluvial geology, making it particularly susceptible to coastal erosion, land subsidence, and relative sea-level rise. The DCL is home to numerous rapidly growing cities, such as Douala, Tiko, and Limbe, which are currently experiencing alarming rates of coastal erosion, frequent flooding, and significant loss of land. Regional and continental investigations have provided evidence of coastal subsidence in this region; however, knowledge of its drivers and impact on the DCL remains limited. To address this knowledge gap, interferometric synthetic aperture radar (InSAR) datasets from the Sentinel-1 C-band satellite were used to quantify vertical land motion (VLM) between 2018 and 2023 with respect to the IGS14 global reference frame and assumed to represents absolute VLM. Digital Elevation Model datasets were used to analyse the elevation of the study area. The results revealed that the rate of VLM ranges from -17.6 mm/year to 3.8 mm/year (standard deviation of 0.2 mm/year), with a mean and median land subsidence rate of 2.7 mm/year and 2.5 mm/year. The analysis of land cover datasets from 1992 to 2022 suggests that urbanisation increased fivefold from 1992 to 2022 and that all contemporary urban areas experienced land subsidence, with the highest rates observed in non-residential zones with building heights ranging between 3 and 6 m. Subsidence rates of the DCL are inversely proportional to the time at which a particular land use and land cover (LULC) class changed into an urban area, highlighting the impact of the timing of LULC changes and urban expansion on present-day subsidence. The land subsidence rates decreased with an increase in building height, suggesting the potential influence of foundation type on land subsidence.
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".