Tellurite-dependent blackening of bacteria emerges from the dark ages
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Environmental context Although tellurium is a relatively rare element in the earth’s crust, its concentration in some niches can be naturally high owing to unique geology. Tellurium, as the oxyanion, is toxic to prokaryotes, and although prokaryotes have evolved resistance to tellurium, no universal mechanism exists. We review the interaction of tellurite with prokaryotes with a focus on those unique strains that thrive in environments naturally rich in tellurium. Abstract The timeline of tellurite prokaryotic biology and biochemistry is now over 50 years long. Its start was in the clinical microbiology arena up to the 1970s. The 1980s saw the cloning of tellurite resistance determinants while from the 1990s through to the present, new strains were isolated and research into resistance mechanisms and biochemistry took place. The past 10 years have seen rising interest in more technological developments and considerable advancement in the understanding of the biochemical mechanisms of tellurite metabolism and biochemistry in several different prokaryotes. This research work has provided a list of genes and proteins and ideas about the fundamental metabolism of Te oxyanions. Yet the biomolecular mechanisms of the tellurite resistance determinants are far from established. Regardless, we have begun to see a new direction of Te biology beyond the clinical pathogen screening approaches, evolving into the biotechnology fields of bioremediation, bioconversion and bionanotechnologies and subsequent technovations. Knowledge on Te biology may still be lagging behind that of other chemical elements, but has moved beyond its dark ages and is now well into its renaissance.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 it