Microbially controlled selenate reduction in nutrient limited systems.
Bibliographic record
Abstract
The global cycling of toxic metals is of great concern to people world wide. Understanding the factors, biotic and abiotic, controlling metal mobility and persistence in natural aquatic environments has been the topics of many studies in recent decades. One of the toxic metals whose mobility and toxicity is of great concern is selenium. Selenium poisoning in fish and aquatic birds can cause terratogenic mutations and increased mortality in contaminated systems. Selenium mobility, and therefore bioavailability is dominated by its oxidation state. The selenium oxyanions selenate and selenite are far more mobile than elemental selenium. Elemental selenium precipitates and binds out of aquatic systems. As such, the goal of many modern remediation strategies has been to reduce selenate and selenite to elemental selenium. Bacteria have been explored as potential natural remediators of selenium and other toxic ions in natural systems. (Abstract shortened by UMI.) Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .K43. Source: Masters Abstracts International, Volume: 44-03, page: 1307. Thesis (M.Sc.)--University of Windsor (Canada), 2005.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".