ARCTOX: a pan-Arctic sampling network to track mercury contamination across Arctic marine food webs
Bibliographic record
Abstract
Arctic marine ecosystems are threatened by new risks of Hg contamination under the combined effects of climate change and human activities. Rapid change of the cryosphere might for instance release large amounts of Hg trapped in sea-ice, permafrost and terrestrial glaciers over the last decades. Sea-ice disappearance is opening new shipping areas to polluting human industries. The general warming of ocean water masses is expected to affect the cycle of Hg, thereby increasing exposure of marine organisms. Hence, Hg could have high impacts on Arctic organisms, biodiversity and ecosystems and is still a source of major environmental concerns. In that context, providing a large-scale and comprehensive understanding of the Arctic marine food-web contamination is essential to better apprehend impacts of anthropogenic activities and climate change on the exposure of Arctic species and humans to Hg. In 2015, an international sampling network (ARCTOX) has been established, allowing the collection seabird samples all around the Arctic. Seabirds are indeed good indicators of Hg contamination of marine food webs at large spatial scale. Gathering researchers from 10 countries, ARCTOX allowed the collection of >5000 samples from twelve seabird species at >40 Arctic sites in 2015 and 2016. These different species have different trophic ecologies (diets and habitats) and will therefore provide information on Hg contamination for the different compartments of Arctic marine ecosystems (i.e benthic, pelagic, epontic, coastal, oceanic). By relying on this new network and by combining Hg analyses with biotelemetry, we aim at (1) monitoring spatio-temporal variations of Hg in Arctic biota. (2) Defining Arctic hotspots of Hg contamination and highlighting sensitive areas that require particular attention and protection. (3) Identifying non-Arctic sources of Hg contamination for migratory Arctic predators.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".