Semivolatile Organic Contaminants in the Hawaiian Atmosphere
Bibliographic record
Abstract
An air sampling campaign on the Island of Hawaii aimed to assess background concentrations of semivolatile organic compounds (SVOCs) over the Central Northern Pacific and to explore their distribution along an altitudinal transect. XAD-resin-based passive air samplers were deployed from May to September 2011 at six sites along a transect from the northeastern coast to the Mauna Loa Observatory and at three other island sites. By crossing the trade wind inversion, the transect comprised the marine boundary layer and free troposphere. At the two ends of the transect, flow-through samplers simultaneously sampled air at monthly resolution. Elevated levels of dieldrin, chlordane- and DDT-related pesticides, polycyclic aromatic hydrocarbons (PAHs), and polybrominated diphenyl ethers (PBDEs) at two urban sites indicated contributions from local sources. The composition of chlordane and DDT-related residues indicated recent emissions. PAHs concentrations that decline more rapidly with increasing elevation than those for PBDEs are consistent with faster atmospheric degradation of PAHs. SVOC levels on Mauna Loa were generally at the lower end of concentration ranges reported for remote sites, including the Arctic. However, in contrast to the Arctic, long-range atmospheric transport is deemed less important than human-induced material flow as the source of SVOCs to the island's atmosphere.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".