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Record W2087461553 · doi:10.1017/s0950268800004945

The prevalence of viral antibodies during a large population fluctuation of house mice in Australia

2000· article· en· W2087461553 on OpenAlexaff
Grant R. Singleton, Adrian L. Smith, Charles J. Krebs

Bibliographic record

VenueEpidemiology and Infection · 2000
Typearticle
Languageen
FieldMedicine
TopicCytomegalovirus and herpesvirus research
Canadian institutionsUniversity of British Columbia
FundersGrains Research and Development Corporation
KeywordsSeroprevalenceMinute virus of micePopulationBiologyHouse miceDoubling timeHouse mouseParvovirusVirusVirologyVeterinary medicineAntibodyAnimal scienceSerologyImmunologyMedicineZoologyParvoviridae

Abstract

fetched live from OpenAlex

We studied the seroprevalence of three viruses (mouse cytomegalovirus (MCMV), minute virus of mice (MVM), and mouse parvovirus (MPV)) in house mice (Mus domesticus) in 1995 7. In the first year average mouse density was less than 1 mouse/ha. From November 1995 to May 1996 the population increased at an average rate of 7% per week, a doubling time of about 10 weeks. From August 1996 to May 1997 the population increased at an average rate of 10% per week, a doubling time of about 7.5 weeks. From a peak around 250 mice/ha in May 1997, the mouse population fell 19% per week to 5 mice/ha in October 1997. The seroprevalence for all three viruses varied dramatically over time. MCMV had the highest seroprevalence (61.7%), followed by MVM (8.5%) and MPV (18.4%). Time series data indicated that MCMV spread rapidly through the population of mice once trap success was greater than 14% (40-100 mice/ha). By contrast MVM and MPV seroprevalence occurred with a 2-3 month and 3-4 month time lag, respectively. The current study supports the contention that MCMV would be a good carrier for an immunocontraceptive vaccine for controlling field populations of mice.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.047
GPT teacher head0.375
Teacher spread0.328 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations38
Published2000
Admission routes1
Has abstractyes

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