Persistent organic pollutants in ocean sediments from the <scp>N</scp>orth <scp>P</scp>acific to the <scp>A</scp>rctic <scp>O</scp>cean
Bibliographic record
Abstract
Abstract Concentrations of polychlorinated biphenyls (PCBs), organochlorine pesticides (OC pesticides), and polybrominated diphenyl ethers (PBDEs) are reported in surficial sediments sampled along cruise transects from the Bering Sea to the central Arctic Ocean. OCs and PCBs all had significantly higher concentrations in the relatively shallow water (<500 m depth) of the Bering‐Chukchi shelf areas (e.g., ΣPCB 286 ± 265 pg g −1 dw) compared to the deeper water regions (>500 m) of the Bering Sea and Arctic Ocean (e.g., Canada Basin ΣPCB 149 ± 102 pg g −1 dw). Concentrations were similar to, or slightly lower than, studies from the 1990s, indicating a lack of a declining trend. PBDEs (excluding BDE‐209) displayed very low concentrations (e.g., range of median values, 3.5–6.6 pg/g dw). In the shelf areas, the sediments comprised similar proportions of silt and clay, whereas the deep basin sediments were dominated by clay, with a lower total organic carbon (TOC) content. While significant positive correlations were observed between persistent organic pollutant (POP) concentrations and TOC (Pearson correlation, r = 0.66–0.75, p <0.05), the lack of strong correlations, combined with differing chemical profiles between the sediments and technical formulations (and/or marine surface waters), indicate substantial chemical processing during transfer to the benthic environment. Marked differences in sedimentation rates between the shallow and deeper water regions are apparent (the ∼5 cm‐depth grab samples collected here representing ∼100 years of accumulation for the shelf sediments and ∼1000 years for the deeper ocean regions), which may bias any comparisons. Nonetheless, the sediments of the shallower coastal arctic seas appear to serve as significant repositories for POPs deposited from surface 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".