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Record W4296833020 · doi:10.1002/mlf2.12036

Assessing global fungal threats to humans

2022· review· en· W4296833020 on OpenAlexafffund
Jianping Xu

Bibliographic record

VenuemLife · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPopulationAgricultureEnvironmental healthGeographyBiologyEnvironmental resource managementEcologyBiotechnologyEnvironmental planningEnvironmental scienceMedicine

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.088
GPT teacher head0.380
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations88
Published2022
Admission routes2
Has abstractyes

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