Bibliographic record
Abstract
Overview -- Selenium, which is one of the chalcogen elements in group 16 (or 6A) of the periodic table, is a semiconductor that is chemically similar to sulfur for which it substitutes in many minerals and synthetic compounds. It is a byproduct of copper refining and, to a much lesser extent, lead refining. It is used in many applications, the major ones being a decolorizer for glass, a metallurgical additive to free-machining varieties of ferrous and nonferrous alloys, a constituent in cadmium sulfoselenide pigments, a photoreceptor in xerographic copiers, and a semiconductor in electrical rectifiers and photocells. Refined selenium amounting to more than 1,800 metric tons (t) was produced by 14 countries in 2000. Japan, Canada, the United States, and Belgium, which were the four largest producers, accounted for nearly 85 percent of world production. An estimated 250 t of the world total is secondary selenium, which is recovered from scrapped xerographic copier drums and selenium rectifiers; the selenium in nearly all other uses is dissipated (not recoverable as waste or scrap). The present selenium reserve bases for the United States and the world (including the United States), which are associated with copper deposits, are expected to be able to satisfy demand for selenium for several decades without difficulty.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.063 | 0.028 |
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".