Bibliographic record
Abstract
Fungi are an integral part of the earth's biosphere. They are broadly distributed in all continents and ecosystems and play a diversity of roles. Here, I review our current understanding of fungal threats to humans and describe the major factors that contribute to various threats. Among the 140,000 or so known species out of the estimated six million fungal species on Earth, about 10% directly or indirectly threaten human health and welfare. Major threats include mushroom poisoning, fungal allergies, infections of crop plants, food contamination by mycotoxins, and mycoses in humans. A growing number of factors have been identified to impact various fungal threats, including human demographics, crop distributions, anthropogenic activities, pathogen dispersals, global climate change, and/or the applications of antifungal drugs and agricultural fungicides. However, while models have been developed for analyzing various processes of individual threats and threat managements, current data are primarily descriptive and incomplete, and there are significant obstacles to integration of the diverse factors into accurate quantitative assessments of fungal threats. With increasing technological advances and concerted efforts to track the spatial and temporal data on climate and environmental variables; mycotoxins in the feed and food supply chains; fungal population dynamics in crop fields, human and animal populations, and the environment; human population demographics; and the prevalence and severities of fungal allergies and diseases, our ability to accurately assess fungal threats will improve. Such improvements should help us develop holistic strategies to manage fungal threats in the future.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".