<i>Sphagnum</i> Moss as an Indicator of Contemporary Rates of Atmospheric Dust Deposition in the Athabasca Bituminous Sands Region
Bibliographic record
Abstract
Sphagnum moss was collected from ombrotrophic (rain-fed) peat bogs to quantify dust emissions from the open-pit mining and upgrading of Athabasca bituminous sands (ABS). A total of 30 bogs were sampled in the ABS region, and 5 were sampled in central Alberta. Ash was separated into the acid-insoluble ash (AIA) and acid-soluble ash (ASA) fractions using HCl. The AIA concentrations increase toward industry from 0.4 ± 0.5% to 4.7 ± 2.0% over a distance of 30 km; the control site at the Utikuma Region Study Area (URSA) yielded 0.29 ± 0.07% (n = 30). Mass accumulations rates showed similar spatial variation. The morphology and mineralogy of the AIA particles were studied using scanning electron microscopy and energy-dispersive X-ray analysis and the particle size distributions using optical methods. Particle size was more variable in moss closer to industry. Major ions in the ASA fraction showed elevated accumulation rates of Ca, K, Fe, Mg, P, and S, with P being up to 5 times greater in samples nearest industry compared to those in distal locations. Given that P has been regarded as the growth-limiting nutrient in bogs, fertilization of nutrient-poor ecosystems, such as these from fugitive emissions of dusts from open-pit mining, may have long-term ecological ramifications.
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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 | 0.000 | 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) | 0.000 | 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".