Eye and Ear Temperature Using Infrared Thermography Are Related to Rectal Temperature in Dogs at Rest or With Exercise
Bibliographic record
Abstract
Rectal body temperature (BT) has been documented in exercising dogs to monitor thermoregulation, heat stress risk, and performance during physical activity. Eye (BTeye) and ear (BTear) temperature measured with infrared thermography (IRT) were compared to rectal (BTrec) temperature as the reference method and assess alternative sites to track hyperthermia, possibly to establish BTeye IRT as a passive and non-contact method. BT measures were recorded at 09:00, 11:30, 12:30, and 02:30 from Labrador Retrievers (N=16) and Beagles (N=16) while sedentary and with 30-min play-exercise (pre- and 0, 15, 30-min post-exercise). Total exercise locomotor activity counts were recorded to compare relative intensity of play-exercise between breeds. BTrec, BTeye, and BTear were measured within 5 min of the target time. Each BT method was analyzed by ANOVA for main effects of breed and time. Method differences were compared using Bland-Altman plots and linear regression. Sedentary BT differed by breed for BTrec (p<0.0001), BTear (p<0.0001), and BTeye (p=0.06) with Labs having on average 0.3-0.8oC higher BT compared to Beagles. Readings also declined over time for BTeye (p<0.0001) and BTear (p<0.0001), but not for BTrec (p=0.63) for both breeds. Total exercise (30-min) activity counts did not differ (p=0.53) between breeds. Time and breed interaction was significant in response to exercise for both BTrec and BTear (p=0.035 and p=0.005, respectively), with a marginal interaction (p=0.09) for BTeye. All 3 methods detected hyperthermia with Labs having a higher increase compared to Beagles. Both BTear and BTeye were significantly (p<0.0001) related to BTrec in all dogs with sedentary or exercise activity. The relationship between BTeye and BTrec improved when monitoring exercise hyperthermia (r=0.674) versus measures at rest (r=0.381), whereas BTear was significantly related to BTrec regardless of activity (r=0.615-0.735). Although BT readings were significantly related, method bias (p<0.02) was observed for BTeye to slightly underestimate BTrec, whereas no bias was observed between BTear and BTrec. This study demonstrates that IRT technology effectively measures both ear and eye temperature and enables effective monitoring of BT changes at rest, with exercise, and between breeds. However, ear, and not eye, temperature is a better reflection of rectal temperature.
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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 | 0.001 | 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".