Ombrotrophic peatlands: natural, holistic, integrated, long-term monitoring systems for atmospheric deposition of environmental contaminants to terrestrial and aquatic ecosystems
Bibliographic record
Abstract
Sphagnum moss and age-dated peat cores from bogs have long been used to study contemporary and past atmospheric deposition of trace elements (TEs). However, other components of bog ecosystems represent additional scientific opportunities. Snowpack sampling, for example, represents a chance to study winter deposition while providing the perfect matrix for ICP-MS analyses of TEs. The berries that grow in bogs, including blueberry (Vaccinium myrtilloides), cloudberry (Rubus chaemomorus), cranberry (Vaccinium oxycoccus) and lingonberry (Vaccinium vitisidaea), provide insight into the bioavailability of micronutrients (and contaminants) at the surface of the bog, as well as an index of dust deposition onto the fruits themselves. Labrador Tea (Rhododendron groenlandicum) provides similar information, but with greater relevance for Indigenous Peoples, as this is an important medicinal plant for them, along with the Pitcher Plant (Sarracenia purpurea). The acidic, organic-rich waters which represent > 90 % of the mass of these ecosystems, presents an even greater opportunity: the chance to quantify the extent to which aerosols and dusts dissolve, subsequent to deposition from the air. In this study, we present data for TEs in all of these media, with a view to exploring the broader potential of ombrotrophic peatlands as natural, holistic, integrated, long-term monitoring systems. The approach presented here not only addresses our need for information regarding atmospheric deposition of environmental contaminants to terrestrial ecoystems, but also insight into their release, or potential release, to downstream aquatic ecosystems.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".