Selenium in terrestrial ecosystems and implications for drastically disturbed land reclamation
Bibliographic record
Abstract
Selenium (1) can be beneficial or toxic to plants and animals (including humans) depending on its concentration. It occurs in low crustal abundance in most geological materials but is found in higher concentrations in Cretaceous and early Tertiary age sedimentary rocks, tuffaceous sediments, roll front deposits, and in association with sulfide minerals in metaliferous deposits. In arid regions soils developed from such parent rocks can contain relatively high concentrations of selenium. Higher concentrations of selenium can occur in ecosystems impacted by human actives such as irrigation projects, air pollution, mining, or long term use of soil amendments (e.g. fly ash) containing elevated levels of selenium. Plant uptake and incorporation of selenium into tissue varies widely between species and ecotypes within plant species. Selenium may be essential to some plant species, particularly those that accumulate it in higher concentrations. The element is essential for animals but the range between deficiency and toxicity is relatively narrow. Selenium is frequently deficient in animal diets in higher moisture environments. In arid environments the higher dietary selenium intake from forage and water sources rarely leads to mortalities from acute selenium toxicity. To evaluate the impact of Se on land use several factors should be considered: 1) careful analytical definition of the total and available selenium content of earthen materials on the site; 2) identification of the new depositional environments for these materials; 3) description of post-disturbance planned and potential land uses; 4) an understanding of the components and interactions of the 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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".