Contribution of Synthetic and Naturally Occurring Organobromine Compounds to Bromine Mass in Marine Organisms
Bibliographic record
Abstract
An extraction, separation, and purification method was developed for the identification and quantification of total bromine (TBr), extractable organobromine (EOBr), and five classes of identified EOBrs. Instrumental neutron activation analysis (INAA) was utilized to quantify EOBr and TBr. The method was then applied to liver samples of tuna, albatross, and polar bear collected from remote marine locations. Polybrominated biphenyls (PBBs), polybrominated diphenyl ethers (PBDEs), bromophenols (BRPs), hydroxylated (OH-) and methoxylated (MeO-) PBDEs were analyzed as identified EOBr. The majority of the bromine in these marine organisms was nonextractable or inorganic, with EOBr accounting for 10-28% of the TBr. Of the identified EOBr, in tuna and albatross, naturally occurring compounds, including MeO-PBDEs, OH-PBDEs, and BPRs, were prevalent. However, the identifiable EOBr in polar bears consisted primarily of synthetic compounds, including PBDEs and PBBs. Overall, 0.08-0.11% and 0.008-0.012% of EOBr and TBr, respectively, were identified. The proportion of EOBr that was identified in marine organisms was relatively small compared to the proportions for organofluorine and organochlorine compounds. This could be related to the great diversity of naturally occurring organobromine compounds in the environment. Naturally occurring brominated fatty acids were estimated to be the predominant compounds in the EOBr fraction.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".