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Record W2277860485 · doi:10.1016/j.pt.2015.12.004

Microsporidia – Emergent Pathogens in the Global Food Chain

2016· review· en· W2277860485 on OpenAlexaff
Grant D. Stentiford, James J. Becnel, Louis M. Weiss, Patrick J. Keeling, Elizabeth S. Didier, Ben Williams, S. Bjørnson, ML Kent, Mark Freeman, Mark J. F. Brown, Emily R. Troemel, Kristina Roesel, Yuliya Y. Sokolova, Karen F. Snowden, Leellen F. Solter

Bibliographic record

VenueTrends in Parasitology · 2016
Typereview
Languageen
FieldImmunology and Microbiology
TopicParasitic Infections and Diagnostics
Canadian institutionsSaint Mary's UniversityUniversity of British ColumbiaCanadian Institute for Advanced Research
FundersNational Institute of Allergy and Infectious DiseasesNational Center for Research ResourcesNational Institute of General Medical SciencesNational Institutes of Health
KeywordsMicrosporidiaFood chainBiologyProtozoaVirologyZoologyMicrobiologyEcologySpore

Abstract

fetched live from OpenAlex

Intensification of food production has the potential to drive increased disease prevalence in food plants and animals. Microsporidia are diversely distributed, opportunistic, and density-dependent parasites infecting hosts from almost all known animal taxa. They are frequent in highly managed aquatic and terrestrial hosts, many of which are vulnerable to epizootics, and all of which are crucial for the stability of the animal-human food chain. Mass rearing and changes in global climate may exacerbate disease and more efficient transmission of parasites in stressed or immune-deficient hosts. Further, human microsporidiosis appears to be adventitious and primarily associated with an increasing community of immune-deficient individuals. Taken together, strong evidence exists for an increasing prevalence of microsporidiosis in animals and humans, and for sharing of pathogens across hosts and biomes.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.042
GPT teacher head0.383
Teacher spread0.341 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations325
Published2016
Admission routes1
Has abstractno

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