Devleoping a methodology to measure the regeneration of reindeer antlers
Bibliographic record
Abstract
Antler regeneration is widely known as one of the best cases of complete organregeneration in mammals; however, little is known about the mechanism behindantler regeneration or the directional growth of antlers during the growth period.To grasp a better understanding of how and when different branches of the antlergrow, a pilot study was launched in May 2012 to try and develop a methodology tomeasure the regeneration of reindeer antlers. Using a Swiss Ranger 4000 (SR4000)range camera, data was captured from three reindeer at the University of CalgarySpy Hill Campus Farm once a week until mid-July. Using empirical data from initialepochs, a photo capture time of 63.6ms (16 FPS) was chosen for the camera, withdata of interest captured at a range varying from approximately 1.2 - 2.0 metres. Aftersegmenting the antlers from the range image in 3D, path lengths were computedalong the skeletonized, two-dimensional range image of individual antlers. Distancesfrom each epoch were then differenced in order to generate an overall growth rateof the antler. While more time is required for conclusive results, preliminary resultsshow that reasonable lengths can be calculated using this method; the final threedimensionalpath length of one of the antlers being measured at approximately 2.243m,giving an approximate growth rate of about 2.67cm/day across the longest path ofthe antler, which is within our expected values of about 1-4cm/day of overall dailygrowth. However, the time-cost of post-processing remains large, and is primarilylimited by factors such as antler-extraction time, post-identification of antler regions,and computation time. Future work may involve automating the extraction and postidentification,to improve efficiency of the method. Furthermore, greater consistencyin the data capture methodology is desired to increase both quantity and quality ofacquired data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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) | 0.000 | 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 teacher head, 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".