Glacial Melt Inputs of Organophosphate Ester Flame Retardants to the Largest High Arctic Lake
Bibliographic record
Abstract
Abstract Organophosphate esters (OPEs) have been detected in the Arctic environment, but the influence of glacial melt on the environmental behavior of OPEs in recipient Arctic aquatic ecosystems is still unknown. In this study, water samples were collected from Lake Hazen (LH) and its tributaries to investigate the distribution of 14 OPEs in LH and to explore the input of OPEs from glacial rivers to LH and the output of OPEs from LH in 2015 and 2018. Σ14OPE concentrations in water of LH were lower than glacial rivers and its outflow, the Ruggles River. In 2015, a high melt year, we estimated that glacial rivers contributed 7.0 ± 3.2 kg OPEs to LH, compared to a 16.5 ± 0.3 kg OPEs output by the Ruggles River, suggesting that residence time and/or additional inputs via direct wet and dry deposition and permafrost melt likely result in OPE retention in the LH watershed. In 2018, a lower melt year, Σ14OPE concentrations in glacial rivers were much lower, indicating that the rate of glacier melt may govern, in part, the concentrations of OPEs in the tributaries of LH. This study highlights long-range transport of OPEs, their deposition in Arctic glaciers, landscapes, and lakes.
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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".