The potentiation of zinc toxicity by soil moisture in a boreal forest ecosystem
Bibliographic record
Abstract
Northern boreal forests often experience forest dieback as a result of metal ore mining and smelting. The common solution is to lime the soil, which increases pH, reducing metal toxicity and encouraging recovery. In certain situations, however, such as in Flin Flon, Manitoba, Canada, liming has yielded only moderate benefits, with some locations responding well to liming and other locations not at all. In an effort to increase the effectiveness of the ecorestoration strategy, the authors investigated if these differences in liming responsiveness were linked to differences in toxicity. Toxicity of metal-impacted Flin Flon soils on the oribatid mite Oppia nitens and the collembolan Folsomia candida was assessed, with a view toward identifying the metal of concern in the area. The effects of moisture content on metal sorption, uptake, and toxicity to the invertebrates were also investigated. Toxicity tests with the invertebrates were conducted using either Flin Flon soils or artificial soils with moisture content adjusted to 30%, 45%, 60%, or 75% of the maximum water-holding capacity of the soil samples. The Relative to Cd Toxicity Model identified Zn as the metal of concern in the area, and this was confirmed using validation tests with field contaminated soils. Furthermore, increasing the moisture content in soils increased the amount of mobile Zn available for uptake with the ion exchange resin. Survival and reproduction of both invertebrates were reduced under Zn exposure as moisture level increased. Thus, moisture-collecting landforms, which are often also associated with high Zn concentrations at Flin Flon, have, as a result, higher Zn toxicity to the soil ecosystem because of increases in soil moisture.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".