Spring Melt and the Redistribution of Organochlorine Pesticides in the Sea-Ice Environment: A Comparative Study between Arctic and Antarctic Regions
Bibliographic record
Abstract
Complementary sampling of air, snow, sea-ice, and seawater for a range of organochlorine pesticides (OCPs) was undertaken through the early stages of respective spring sea-ice melting at coastal sites in northeast Greenland and eastern Antarctica to investigate OCP concentrations and redistribution during this time. Mean concentrations in seawater, sea-ice and snow were generally greater at the Arctic site. For example, α-HCH was found to have the largest concentrations of all analytes in Arctic seawater and sea-ice meltwater samples (224–253 and 34.7–48.2 pg·L –1 respectively compared to 1.0–1.3 and <0.63 pg·L –1 respectively for Antarctic samples). Differences in atmospheric samples were generally not as pronounced however. Findings suggest that sea-ice OCP burdens originate from both snow and seawater. The distribution profile between seawater and sea-ice showed a compound-dependency for Arctic samples not evident with those from the Antarctic, possibly due to full submersion of sea-ice at the former. Seasonal sea-ice melt processes may alter the exchange rates of selected OCPs between air and seawater, but are not expected to reverse their direction, which fugacity modeling indicates is volatilisation in the Arctic and net deposition in the Antarctic. These predictions are consistent with the limited current observations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".