Three-Dimensional Analysis of Reindeer Antler Regeneration using Stereophotogrammetry
Bibliographic record
Abstract
Reindeer antlers are the only mammalian organs that completely regenerate. Thegoal of this project was to define growth characteristics of the antler during regeneration.The regions of the antler found to have the highest growth rates would indicate theregions that are enriched with growth factors that control antler regeneration. Thedetermination of these growth factors could lead to advances in regenerative medicine.Antler growth rates in three reindeer were determined using stereophotogrammetry.Using two cameras, simultaneous images of the antlers were taken over 6 weeks.Markers with known coordinates were used to calculate the orientation parameters ofthe cameras. Distinct antler points were chosen in both images, and a least squaressolution of the collinearity equations was used to calculate their 3D coordinates.Distances between points were compared between data sets to determine growth ratesof portions of the antlers.The results from both antlers of one reindeer are available with the followinglevel of precision. The variances of the calculated distances were found to be at themillimetre level, with standard deviations at the centimetre level. Distances from theantler base to branching points remained relatively constant suggesting that growthoccurs almost exclusively at the antler tips. The growth rates of the tips remained atapproximately 0.5-1 cm of daily growth. However, the growth rate of the lowest branchtip began to decelerate by the end of the study.Although this method had only the previously mentioned level of precision, thisdoes not threaten the conclusions made. The results define the locations of highestgrowth, which could be used in the study of the growth factors controlling regeneration.If this project were to be repeated, changes could be made to improve accuracy,including placing visible markers on the antlers so that consistent points could bechosen.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".