Bibliographic record
Abstract
Several recent studies have attempted to link refined coal tar-based sealer to possible cancer-causing compounds. The latest study attempts to link refined coal tar-based sealer to concentrations of polycyclic aromatic hydrocarbons (PAHs) in dust collected from ground floor apartments in Austin, Texas. A similar earlier study led to a ban of refined coal tar-based sealer in Austin, Texas in 1996. This article describes how the sealcoating industry is mounting a multi-pronged defense of the coal tar-based sealer. The Pavement Coatings Technology Council (PCTC) has hired a specialist who will analyze the new Austin study and a related Canadian Settled Dust Study and report back in March about the studies' process, results, and conclusions. In addition, a PCTC follow-up study that refutes the 2005 Austin study is in the final stages of the scientific review process. The sealcoating industry argues that PAHs are in many other sources other than just sealers, and have not be shown to be a known human carcinogen. The industry maintains that they have safely used this product for 60 years and have no reason to think there are any adverse effects when used according to manufacturers recommended specifications.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".