Incorporation and Distribution of Polycyclic Aromatic Hydrocarbons in Experimental Sea-Ice
Bibliographic record
Abstract
Rapid melting of sea-ice makes the Arctic more accessible for marine shipping and other industrial activities, increasing the risk of oil spills in the Arctic Ocean. Polycyclic aromatic hydrocarbons (PAHs) are among the most toxic substances in petroleum oil, yet their behavior in sea-ice-covered waters remains poorly studied. Here, we report an outdoor microcosm study to examine the partitioning behavior of four PAHs (naphthalene, phenanthrene, pyrene, and benzo(a)pyrene) across the seawater-sea-ice-atmosphere interface in the presence of particulate humic acid as a surrogate for particulate organic carbon (POC). We show that the higher the molecular weight of the PAH, the higher its concentration in sea-ice and the POC fraction. The POC-aqueous phase (seawater or bulk sea-ice) partition coefficients, K d, are reasonably well explained by temperature and salinity for all four PAHs in seawater and for phenanthrene and pyrene in sea-ice. Relationships of K d with temperature and salinity in sea-ice and freezing seawater are complex and nonunidirectional, most likely due to the dynamic nature of sea-ice and seawater under such temperatures. This suggests that conventional equilibrium-based approaches developed for open-water conditions need to be revisited when describing the behavior of PAHs in ice-covered waters.
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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) | 0.001 | 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".