A framework for a decision support system for municipal solid waste landfill design
Bibliographic record
Abstract
A decision support system (Landfill Advisor or LFAdvisor) was developed to integrate current knowledge of barrier systems into a computer application to assist in landfill design. The program was developed in Visual Basic and includes an integrated database to store information. LFAdvisor presents the choices available for each liner component (e.g. leachate collection system, geomembrane liner, clay liners) and provides advice on their suitability for different situations related to municipal solid waste landfills (e.g. final cover, base liner, lagoon liner). Unique to LFAdvisor, the service life of each engineered component is estimated based on results from the latest research. LFAdvisor considers the interactions between liner components, operating conditions, and the existing site environment. LFAdvisor can be used in the initial stage of design to give designers a good idea of what liner components will likely be required, while alerting them to issues that are likely to arise. A systems approach is taken to landfill design with the ultimate goal of maximising long-term performance and service life.
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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".