Extended Abstract: An Empirical Study Reviewing Occupational Exposure Leading to Lung Related Diseases and COVID-19 Incidences in Louisiana Parishes
Bibliographic record
Abstract
Per the Louisiana Department of Health, mesothelioma and asbestos related deaths are higher than the national average (https://ldh.la.gov/Coronavirus/ accessed 11_17_2021). Occupational exposure to various chemicals in different industries including, petrochemical, construction, plumbing, manufacturing etc. can lead to lung cancer and even mesothelioma. Louisiana has a comparatively higher fatality rate (66.6 in 100,000 people) than the USA national average (58.7 in 100,000 people) for lung cancer and other lung related disorders. Louisiana’s five Mississippi River Ports together, make the largest port complex in the world. These ports and waterways carry a quarter of the nation’s waterborne commerce including half of the nation’s grain, and nearly a quarter of the nation’s coal. They allow connection to major industries, such as agriculture, manufacturing, transportation/warehousing, mining, and oil/chemical. Unfortunately, these industries brought about most occupational exposure sources. However, there is no existing data source that accurately tracks the location of high-risk parishes and the predominant occupations in those parishes. Therefore, the aim of this study is to analyze the route of occupational exposure of asbestos or other lung related carcinogens in Louisiana which would help in exposure mitigation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".