Assessing the long-range transport potential of polybrominated diphenyl ethers: A comparison of four multimedia models
Bibliographic record
Abstract
Data from a comprehensive literature search of environmentally relevant physical-chemical properties for nine polybrominated diphenyl ethers (PBDE), ranging from a monobrominated congener to the fully brominated decabromodiphenyl ether, were evaluated and adjusted to achieve both internal and interhomologue consistency. These data were then used in four model-based long-range transport potential (LRTP) assessment methods. The models TaPL3-2.10, ELPOS-1.1.1, Chemrange-2, and Globo-POP-1.1 were found to yield comparable predictions. A comparison of the LRTP estimates for the PBDEs with those of benchmark chemicals (polychlorinated biphenyls [PCBs]) suggest that the lower-brominated congeners have a LRTP comparable to that of PCBs known to be subject to significant LRT, whereas the highly brominated congeners have a very low potential to reach remote areas. This is in agreement with field measurements in remote regions that indicate that the lighter components of commercially produced PBDE mixtures predominate. Deviations between Chemrange and the models based on the concept of a characteristic travel distance were due to differences in the assumed height of the air compartment, which influences the relative importance of atmospheric degradation and deposition processes. The three models assuming a uniform temperature of 25 degrees C may underestimate the LRTP of the smaller congeners. Only atmospheric parameters had a notable influence on the LRTP estimates by TaPL3, ELPOS, and Chemrange. whereas the relative enrichment of chemicals in the Arctic calculated by Globo-POP is additionally sensitive to the parameters related to the interaction of temperature with air-surface exchange and degradation in surface compartments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".