Fecal DNA, hormones, and pellet morphometrics as a noninvasive method to estimate age class: an application to wild populations of Central Mountain and Boreal woodland caribou (<i>Rangifer</i> <i>tarandus</i> <i>caribou</i>)
Bibliographic record
Abstract
Determining age structure of populations is a valuable parameter in wildlife management, but is often difficult to obtain. Here, we tested a noninvasive method via fecal DNA, hormones, and pellet morphometrics to distinguish calf from adult in Central Mountain and Boreal woodland caribou (Rangifer tarandus caribou (Gmelin, 1788)) populations. Annual surveys of fall-sampled Central Mountain caribou were done in Jasper National Park, Alberta, between 2006 and 2011 and winter-sampled Boreal caribou were surveyed in the North Interlake area, Manitoba, between 2004 and 2010. Samples were amplified at 10 microsatellite loci to identify unique individuals and capture histories were used to identify putative calves and adults. Fecal pellets were measured for length, width, depth, dry mass, and analyzed for progesterone, estrogen, and testosterone concentrations. Results showed significant differences in fecal pellet size between putative calves and adults for both sexes and populations–seasons. Progesterone concentration was significantly higher in Jasper–fall and North Interlake–winter adult females. Testosterone was significantly higher in Jasper–fall adult males. North Interlake–winter males exhibited no significant difference in hormone concentrations between age classes. When applied to the entire Jasper data set, 98% of females and 88% of males were assigned to an age class. This study illustrates the possibilities of using noninvasive methods to determine an age class in wild ungulate populations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".