Planning for Ex-Landfill Redevelopment: Assessing What Community Have in Mind
Bibliographic record
Abstract
Malaysia, a fast growing developing country is now facing the issue of inadequate urban spaces for futuredevelopment, which leads to the need to redevelop the brownfield, particularly ex-landfills. A total of 296ex-landfills have been planned to undergo redevelopment by the year 2020. Although there is a policy forex-landfill development, a question arises if the policy reflects the needs of the society as the end-recipient thatdetermines the success of the planned development. Therefore, this study was carried out to assess what thecommunity has in mind as a way to identify the community needs in ensuring the success of the futuredevelopment of ex-landfill. Based on the objectives to identify the perceptions of the community on (i)ex-landfill issues, (ii) the appropriate type of re-development for the ex-landfills, and (iii) the function of thepublic park in ex-landfills, which have been the main priority of the development type considered by thegovernment, this study produced a priority ranking result that could assist the urban administrator or specificallythe Local Authorities in Malaysia to plan an effective and an acceptable development of the ex-landfill for thebenefits of current and future communities.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".