Bibliographic record
Abstract
Introduction Inorganic contaminants include metals, metalloids, and a number of relatively simple molecules such as phosphate and ammonia. The ways in which inorganic substances can become problematic in the environment, and thus be considered as contaminants, are frequently as a result of their being mobilised or modified chemically by human activities. In contrast to organic contaminants (Chapter 7), many of which are xenobiotic, many inorganic contaminants occur naturally; ecosystems do not distinguish between natural and anthropogenic substances. In terms of their regulation, as well as their management, inorganic contaminants are expected to differ in many respects from xenobiotic substances. Another point of contrast between inorganic and organic contaminants is that a number of inorganic substances not only are potentially toxic but also are required as nutrients. Such substances exemplify the oft-quoted statement that the dose defines the poison (Paracelsus, c 1493–1541, cited in Rodricks, 1993). In the context of dose-response, those substances that are nutrients as well as potential toxicants can be put into a simple model, as shown in Figure 6.1, which invokes the concepts of deficiency, sufficiency, and toxicity, successively. For an element such as copper, this succession is theoretically a relatively simple series of transitions. The dose of copper is shown as supply, on the x -axis, and the response as growth rate is shown on the y -axis. Very low doses of copper can result in nutrient deficiency, with below optimum growth; as the supply increases, up to a certain point, there is a positive response. This is the range over which copper is required as a micronutrient.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.068 | 0.040 |
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".