Contribution of dusts to trace element inventories in pioneering plants growing on a pilot-scale pit lake watershed
Bibliographic record
Abstract
Dusts containing trace elements (TEs) are deposited onto and can easily be retained on the surfaces of plants and adjacent soil, especially in areas with intense open pit mining, aggregate extraction, and road traffic. In this study, a greenhouse experiment was designed to evaluate the uptake of selected TEs from soil by Foxtail Barley (FB) in the absence of dust. The plant seeds and the soils used in the pot experiment were collected at Lake Miwasin (LM), a pilot-scale pit lake, with the soil from a stockpile prepared before bitumen mining began. Plants were grown inside laminar flow, class 100, metal-free clean air cabinets (CACs), in this soil and in soil spiked with elements that are enriched in bitumen: V (15 and 75 mg/kg), Ni (5 and 25 mg/kg), and Mo (1 and 5 mg/kg). Hoagland's solution was used as a nutrient source for one set of treatments. Conservative lithophile elements in plant shoots were far more abundant in plants growing at LM compared to those grown inside the CACs, indicating significant contributions from dust at LM; this was confirmed using SEM analyses. The addition of V, Ni, and Mo to the soils significantly increased their concentrations in the plants (p < 0.05), but plant growth was not significantly impacted. In regard to the bioaccumulation of TEs by plants, the study highlights the importance of determining their primary sources, beginning with atmospheric dust, in order to accurately assess root uptake from soil, and translocation into the shoots.
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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) | 0.000 | 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".