Bibliographic record
Abstract
Landfills are unpopular. They are dirty, smelly, environmentally unfriendly and potentially dangerous. Working with landfills is, perhaps, more unpopular. Being employed to expand the major landfill for a major city is a task that forces self-assessment, particularly when the land in question is pristine woodland with hiking and horse –riding trails. The understanding dawns when one focuses not on what is happening as a result of one’s work, but rather, what is not happening as a result of the work. Thousands of homes will be saved from polluted drinking water. The environment will not be severely affected and the area of influence of the landfill will not be so large. During my internship with Golder Associates, I had the opportunity to participate in the Site Investigation done to assess the technical viability of expanding a landfill. After an initial walkover, a grid was set up on the site, using machetes and chain saws. The forest was cleared on these lines by lumberjacks and the engineering team moved in to conduct a geophysical survey of the subsurface to assess the thickness of an underlying clay layer. That clay layer, if it is sufficiently thick, will protect the local water supply from escaped landfill leachate.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".