Biomonitoring of atmospheric depositions of rare earth elements and other elements in Quebec, Canada
Bibliographic record
Abstract
Despite the growing demand for rare earth elements (REE) in technological applications and the potential risks associated with their environmental release, few studies have focused on atmospheric levels of REE. Nowadays, in Canada, only one active mine is exploiting REE ores. In Quebec (eastern Canada), several exploration projects are underway; however, no mine is currently active, and few human activities are likely to represent a source of atmospheric REE. Thus, we established the first estimates of atmospheric depositions of REE and other elements (e.g., transition metals) in Quebec and identified the current emission sources by comparing these depositions across different land uses (forest, rural, and anthropogenic). We also report on some elements of interest for moss physiology and biogeochemistry (e.g., K, Na, Mg, and Ca). We employed a biomonitoring approach, utilizing Pleurozium schreberi as an indicator of elemental composition in atmospheric deposition. 210 sites were sampled across southern Quebec, in the most densely populated areas of Quebec. Elements were analyzed by ICP-MS and ICP-OES. Atmospheric depositions of REE (and other elements) are low in Quebec compared to other biomonitoring studies worldwide, with ∑REE average values of 2.63 ppm in the mosses. Hotspots were detected in urban, tourist, or industrial areas, with concentrations reaching up to ∑REE = 32.14 ppm. Overall, the origin was mainly terrigenous, as shown by the enrichment factor (EF ≈ 1 for all REE). However, a few elements (Cd, Zn, Ag, B, Mn, Sb, K, Ca, and Cu) exhibited EF > 10, indicating contamination at nearly all locations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".