Interactions of polychlorinated biphenyls and organochlorine pesticides with sedimentary organic matter of retrogressive thaw slump‐affected lakes in the tundra uplands adjacent to the Mackenzie Delta, NT, Canada
Bibliographic record
Abstract
Abstract Using a comparative spatial analysis of sediment cores from eight lakes in tundra uplands adjacent to the Mackenzie Delta, NT, we examined how the presence of retrogressive thaw slumps on lake shores affected persistent organic pollutant (POPs, including polychlorinated biphenyls and organochlorine pesticides) accumulation in lake sediments. Sediments of slump‐affected lakes contained higher total organic carbon (TOC)‐normalized POP concentrations than nearby reference lakes that were unaffected by thaw slumps. Mean focus‐corrected inorganic sedimentation rates were positively related to TOC‐normalized contaminant concentrations, explaining 58–94% of the variation in POP concentrations in sediment, suggesting that reduced organic carbon in slump‐affected lake water results in higher concentrations of POPs on sedimentary organic matter. This explanation was corroborated by an inverse relationship between sedimentary POP concentrations and TOC content of the lake water. Inferred chlorophyll a, S2, and S3 carbon fluxes to sediment were not significantly correlated to POP fluxes. Higher POP concentrations observed in sediment of slump‐affected lakes are best explained by simple solvent switching processes of hydrophobic organic contaminants onto a smaller pool of available organic carbon when compared to neighboring lakes unaffected by thaw slump development.
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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 | 0.001 |
| 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".