Land Cover Dynamics along the Urban–Rural Gradient of the Port-au-Prince Agglomeration (Republic of Haiti) from 1986 to 2021
Bibliographic record
Abstract
The landscape of the Port-au-Prince agglomeration in the Republic of Haiti has undergone profound changes linked to (peri-)urban expansion supported by rapid demographic growth. We quantify the land cover dynamics along the urban–rural gradient of the Port-au-Prince agglomeration using Landsat images from 1986, 1998, 1999, 2010, and 2021 coupled with geographic information systems and landscape ecology analysis tools. The results show that over 35 years the acreage of the urban zone increased seven-fold while that of the peri-urban area increased five-fold, to the detriment of the rural zone, which was reduced by 14%. The dynamics of the landscape composition along the urban–rural gradient are characterized by a rapid progression of built-up and bare land in urban and peri-urban zones and by fields in the rural zone, in contrast to the more accentuated regression of vegetation in the peri-urban zone. The landscape of the study area has undergone significant changes due to the high demand for housing resulting from rapid population growth, in the context of a lack of territorial development planning by public authorities. This impacts the sustainability of socio-economic and ecological processes in an area where populations are highly dependent on plant resources. Our results underline the necessity to orient territorial development planning in urban, peri-urban and rural zones through an integrated and participatory approach.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".