Vapours and Aerosols of Bitumen: Exposure Data Obtained by the German Bitumen Forum
Bibliographic record
Abstract
The recently published paper by Rühl et al. (2006) describes the results of measurements of exposure to bitumen fumes and vapours in a variety of industries where hot bitumen is handled in Germany. Overall, it is to be applauded that results of studies assessing exposure in a variety of industries and workplaces are being published in peer-reviewed literature and do not stay hidden in large inaccessible databases or grey literature. We consider the Annals of Occupational Hygiene to be the world-class forum for publishing such studies and it has done so increasingly over the last two decades. Given that quantitative exposure data are becoming more and more essential to modern epidemiology for establishing exposure–response relations and risk assessment (Stewart et al., 1996; Loomis and Kromhout, 2004) it is of utmost importance that peer review of such studies is done rigorously. (By doing so, the readership and future users of the data are offered the opportunity to fully evaluate and appreciate the presented data.) Having carefully appraised the paper by Rühl et al. (2006) we are of the opinion that in this particular case adequately critical review by the journal has not taken place. We therefore would like to highlight several important issues that will enable readership to appreciate the value of exposure survey described by Rühl et al., as well as the appropriateness of the manner in which it was presented and interpreted in the paper.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".