Seasonal riverine inputs may affect diet and mercury bioaccumulation in Arctic coastal zooplankton
Bibliographic record
Abstract
Climate change driven increases in permafrost thaw and terrestrial runoff are expected to facilitate the mobilization and transport of mercury (Hg) from catchment soils to coastal areas in the Arctic, potentially increasing Hg exposure of marine food webs. The main aim of this study was to determine the impacts of seasonal riverine inputs on land-ocean Hg transport, zooplankton diet and Hg bioaccumulation in an Arctic estuary (Adventfjorden, Svalbard). The Adventelva River was a source of dissolved and particulate Hg to Adventfjorden, especially in June and July during the river's main discharge period. Stable isotope and fatty acid analyses suggest that zooplankton diet varied seasonally with diatoms dominating during the spring phytoplankton bloom in May and with increasing contributions of dinoflagellates in the summer months. In addition, there was evidence of increased terrestrial carbon utilization by zooplankton in June and July, when terrestrial particles contributed substantially to the particulate organic matter pool. Total (TotHg) and methyl Hg (MeHg) concentrations in zooplankton increased from April to August related to increased exposure to riverine inputs, and to shifts in zooplankton diet and community structure. Longer and warmer summer seasons will probably increase riverine runoff and thus Hg exposure to Arctic zooplankton.
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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".