Rising environmental temperatures and polluted surface waters: the prelude to the rise of mycoses in South Africa
Bibliographic record
Abstract
South Africa’s rivers are frequently used by communities lacking proper sanitation infrastructure for domestic purposes; however, these surface waters may pose a health risk to immunocompromised individuals due to the presence of opportunistic pathogenic fungi in the polluted water. Although only a few studies have focused on the presence of clinically relevant fungal species in South African rivers, many known opportunistic pathogenic species were found to be predominant in these waters. Furthermore, strong evidence exists that increased numbers of clinically relevant species may be observed in future due to fungi acquiring thermotolerance in response to the global increase in temperature. Thermotolerance is a major factor contributing to pathogenesis in fungi, due to the generally low tolerance of most fungi toward mammalian body temperatures. It is therefore contended that combinatorial effects of water pollution and rising environmental temperatures could lead to an increase in the incidence of mycoses in South Africa. This is especially concerning since a relatively large population of immunocompromised individuals, represented mostly by HIV-infected people, resides in the country.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".