Dunsink Landfill \nA Study Of The Health Effects Of Living In Close \nProximity To A Landfill And The Public Perception \nOf Landfills
Bibliographic record
Abstract
This thesis deals with the adverse health effects caused by living in close \nproximity to a landfill and the public perception o f landfills. The main case study \nis that of Dunsink Landfill in Finglas, County Dublin. Other landfills focused on \nare Nantygwyddon Landfill in Rhondda, Wales and Miron Quarry in Montreal, \nQuebec, Canada.\nThis thesis examines all forms of continuous illness found in three areas near \nDunsink Landfill, not just more serious illnesses such as cancers. These illnesses \nrange from runny noses, coughs and colds to the more serious illnesses of \ncancers.\nPublic perception is dealt with by examining two opposing viewpoints of landfills \nand why these viewpoints have been taken. The opinions of the residents, the \nNorth-West Area Manager for Dublin City Council and Dr. Eugene Boyle of the \nHealth Service Executive (HSE) are also taken into account.\nA door-to-door survey o f three areas in Finglas was used in order to obtain the \ninformation needed for the study. This survey included questions relating to \nhealth, opinions and own knowledge.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".