Bibliographic record
Abstract
Managing air quality through emissions control entails significant societal benefits in Canada and around the world.As the public health impacts of emissions depend on the atmospheric conditions conducive to pollutant transport and transformation, sophisticated atmospheric models are necessary to link public health impacts with sources of emissions directly.This thesis develops a novel method to integrate health benefit assessment and formal sensitivity analysis tools.It employs a reverse sensitivity analysis technique, infused with epidemiological and economic data, to attribute air pollution health effects to emissions sources.This linkage creates a streamlined approach for assessing the damages incurred by anthropogenic emissions, and the benefits of their control, on a source-by-source basis.The findings presented in this thesis indicate that the public health benefits of emission controls vary considerably from source-to-source and by emitted species.A main feature of emission control benefits is their dependency on the composition of the atmosphere and hence on emission quantities.As the atmosphere becomes cleaner with progressive emission reduction policies, the benefits-per-ton of emissions control are likely to change, particularly for pollutants that undergo nonlinear transformations in the atmosphere.Further, the shape of the concentration-response function (CRF) between pollutant exposure and mortality plays a determining role in estimating these benefits.This thesis investigates how both atmospheric chemistry and assumptions about the CRF influence the health benefits of emission control.For secondary pollutants such as ozone, the benefits-per-ton of NO x control are found to increase substantially as the atmosphere
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".