Bibliographic record
Abstract
Water-soluble heavy metal salts injure health when they leach into water supplies. It is important that students who may later be employed in industries generating aqueous solutions of such salts are aware of the methods that can be used to recover the metal salt or transform it to non-health threatening products. The research was in the management of small quantities of hazardous wastes, such as are generated in school, college, and university teaching laboratories; in research laboratories; in industrial quality control and testing laboratories; and in small industries. Methods for the recovery of silver, nickel, and cobalt salts from relatively small volumes of aqueous solutions of their soluble salts were developed and tested. Where it was not practical to recover the metal salt, the practice has been to convert it to a water-insoluble salt, often the sulfide. This requires the use of highly toxic reagents. It was found that a number of heavy metal salts can be precipitated as the silicates, returning them to the form in which they are found in the natural ore. These salts show similar solubility properties to the sulfides in neutral, acidic, and basic aqueous solutions. The work has determined the conditions, quantities, and solution acidity that result in the most effective precipitation of the heavy metal salt. The concentration of the metal ions remaining in solution was measured by AA and ICP spectrometry. Specific methods have been developed for the conversion of salts of mercury and chromium to nonsoluble products.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.143 | 0.063 |
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".