Tick infestation effects on haemoglobin levels of deer mice (<i>Peromyscus maniculatus</i>)
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".