Building and Endangering Urban Landscapes: the Case of Construction Wastes in Bamenda Cameroon
Bibliographic record
Abstract
Building and construction is an ongoing process in urban landscapes given the available technology, obsolescence in buildings and the need to improve on the urban scenery. This activity is however accompanied by the generation of huge amounts of degradable and non-degradable wastes which if not well managed can constitute an eyesore and a potent danger to the urban population. Construction waste can also be of immense economic benefits to the population and the construction industry because it can be salvaged, recycled and reused. A random sampling of wastes generated at selected construction sites for ten neighbourhoods (two within the Central Business District (CBD) and eight at the periphery) in Bamenda town indicate that construction waste represents large amounts of material such as zinc, wood, iron rods, broken tiles, sand and plastic which is often illegally dumped by roadsides, river banks and building sites. Poor waste disposal/handling methods cause health and environmental problems such as flooding, and pollution in the municipality. While, such waste generate income and provide cheap equipment/material to the population and construction industry through informal recycling and reuse for other purposes, there is need for improved management as part of a growing movement toward sustainable city development due to increasing population and urbanization.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".