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Record W3204921088 · doi:10.1017/s0031182021001700

Tick infestation effects on haemoglobin levels of deer mice (<i>Peromyscus maniculatus</i>)

2021· article· en· W3204921088 on OpenAlexafffund

Bibliographic record

VenueParasitology · 2021
Typearticle
Languageen
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsLaurentian University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTickTick infestationInfestationHost (biology)PopulationTick-borne diseaseIxodidae

Abstract

fetched live from OpenAlex

Deer mice (Peromyscus maniculatus) are hosts to ixodid ticks as well as the associated tick-borne pathogens they can spread. As the ranges of black-legged ticks (Ixodes scapularis) and American dog ticks (Dermacentor variabilis) expand northwards, naïve host populations of deer mice are likely to become infested by ticks and experience the physiological effects that ticks can have on them via blood-feeding. The prevalence of these haematophagous ticks can affect the haemoglobin levels of the mice they infest. Haemoglobin levels were compared and analysed in deer mice populations at three different sites with varying tick exposure. These results suggested that without confounding effects, the abundance of black-legged and American dog ticks on individual mice had a significant negative effect on the hosts' haemoglobin levels, but only in an area with high tick infestation. This was seen across the average haemoglobin levels between populations, where there was a significant difference between the source population with the longest established tick populations and the source population where neither black-legged nor American dog ticks were prevalent. As the ticks' ranges expand and they become more abundant, it is important to understand how their prevalence and intensity can alter host physiology, potentially affecting their own range expansion and the spread of the diseases they may carry.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.311
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.001

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 head0.012
GPT teacher head0.279
Teacher spread0.266 · 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 designBench or experimental
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

Citations1
Published2021
Admission routes2
Has abstractyes

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