Bibliographic record
Abstract
Metallic elements, ions and compounds produce varying degrees of toxicity in organisms with which they come into contact. Metal speciation is critical to understanding these adverse effects; the adjectives “heavy” and “toxic” are not helpful in describing the biological properties of individual elements, but detailed chemical structures are. As a broad generalization, the metallic form of an element is inert, and the ionic salts are the species that show more significant bioavailability. Yet the salts and other chelates of a metal ion can give rise to quite different toxicities, as exemplified by a range of carcinogenic potential for various nickel species. Another important distinction comes when a metallic element is organified, increasing its lipophilicity and hence its ability to penetrate the blood brain barrier, as is seen, for example, with organic mercury and tin species. Some metallic elements, such as gold and platinum, are themselves useful therapeutic agents in some forms, while other species of the same element can be toxic, thus focusing attention on species interconversions in evaluating metal-based drugs. The therapeutic use of metal-chelating agents introduces new species of the target metal in vivo, and this can affect not only its desired detoxification, but also introduce a potential for further mechanisms of toxicity. Examples of therapeutic iron chelator species are discussed in this context, as well as the more recent aspects of development of chelation therapy for uranium exposure.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".