Thermal properties of arctic fox fur and the effect of fur lice infestation.
Bibliographic record
Abstract
In 2019, a new species of sucking louse was observed in arctic foxes (Vulpes lagopus) on Svalbard and Northern Canada. Abnormal patterns of fur loss, inconsistent with normal moult, were observed across the neck, shoulders and back, raising concerns as to how the animals would cope with damaged fur during the cold Arctic winter. This study investigates the impact of these lice on Svalbard arctic foxes, focussing on louse prevalence, fur condition and thermal properties. A total of 23 fur samples from 17 arctic foxes were used to estimate louse prevalence, abundance and fur damage compared to thermal properties. Louse density was determined by dissolving skin biopsies and counting lice visually. The thermal properties were determined by establishing a steady heat flow through a system of a standard conductor and a fur sample and measuring the temperature at each interface.\nThe prevalence was lower (44%) compared to the prevalence estimated (70%) from the previous trapping season (2021-2022). Conductivity values ranged from 0.0304 – 0.0869 W/m°C and conductance from 0.974 to 2.94 W/m2°C. These values broadly agree with previous studies.\nNo linear relationship was found between louse density and fur state, suggesting an underlying louse population dynamic. While no linear correlation was found between louse density and thermal conductivity of the fur or fur damage and conductivity, a significant (p < 0.05) relationship was found between louse density, fur damage and thermal conductance, implying that infested arctic foxes, as hypothesised, experience excess heat loss compared to non-infested foxes and that this heat loss is due to a loss of fur rather than a change in the internal structure of the coat.\nA pilot study tested the potential use of thermal imaging in the detection and monitoring of fur loss and lice infestation in wild arctic foxes. The initial results showed promise, but the system requires further refinement before large-scale field trials. An image of an arctic fox was captured with evidence of fur loss.
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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 | 0.001 |
| 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".