Evaluating velvet antler growth in red deer stags (Cervus elaphus) using hand-held and digital infrared thermography
Bibliographic record
Abstract
The objectives of this study were to evaluate whether velvet antler (VA) surface temperature gradients, as measured by either a single-spot infrared temperature (SST) sensor (SSTS; exp. 1) or digital infrared temperature (DIT) imaging (DITI; exp. 2), would pattern VA growth. In exp. 1, growth rates and SST were obtained from yearling (n = 8) and mature (n = 17) red deer stags (Cervus elaphus) every 14 d following eruption through day 56 in yearlings and day 112 in mature stags. In exp. 2, growth rates and DIT (main beam VA base, mid and tip temperatures) were obtained from red deer stags (n = 31) every 14 d following eruption through day 126. Background temperatures were recorded in conjunction with thermal antler measurements. In exp. 1, yearling VA base and tip SST were positively correlated with one another (P < 0.01); however, both measurements were also positively correlated with background SST (P < 0.05). In mature stags, VA base SST paralleled (P < 0.05) background SST measures, while tip SST did not change from day 56 through day 112. In exp. 2, VA DIT changed (P < 0.01) over time and differed (P < 0.01) between base, mid and tip. During the early growth period, VA temperatures increased (P < 0.05) from 38.9 ± 0.2°C at the base to 39.3 ± 0.2°C at the tip of the antler. In contrast, during the late growth period, DIT was higher (P < 0.01) at the base (36.8 ± 0.3°C) than at the tip (35.7 ± 0.3°C) of the antler. In conclusion, SSTS did not have the sensitivity to signify changes in antler growth rates. However, in exp. 2 using DITI, VA thermogenesis paralleled VA growth suggesting that DITI may have value in monitoring VA growth. Key words: Velvet antler, red deer, thermography
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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 | 0.001 | 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) | 0.000 | 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".