Formation, Occurrence, Significance, and Analysis of Organoselenium and Organotellurium Compounds in the Environment
Bibliographic record
Abstract
Among all environmentally-relevant trace elements, selenium has one of the most diverse organic chemistries. It is also one of the few trace elements that may biomagnify in food chains under certain conditions. Yet, the exact chemical forms of selenium involved in the uptake into organisms and transfer to higher trophic levels, as well as the biochemical mechanisms that lead to their subsequent metabolism in organisms, are still not well understood. This is in part due to the analytical challenges associated with measuring the myriad of discrete Se species occurring in organisms. While there are generalized concepts of selenium metabolism, there is a lack of conclusive analytical evidence supporting the existence of many postulated intermediates. Likewise, there is a disconnect between the major selenium species encountered in abiotic compartments (waters, soils, and sediment), and those found in organisms, which renders the qualitative and quantitative description of the bioaccumulation process uncertain. Here, we summarize the knowledge on important selenium and tellurium species in all environmental compartments, and identify gaps and uncertainties in the existing body of knowledge, with emphasis on problems associated with past and current analytical methodology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".