Capturing Neonatal Bison With a Net Gun From a Utility Terrain Vehicle
Bibliographic record
Abstract
Abstract Monitoring neonatal bison Bison bison for daily survival is difficult without a proper technique to effectively capture and safely handle neonates. Currently, we are not aware of an effective method to capture neonatal bison. In May 2015, we initiated a study on Olson's Bison Conservation Ranches, Pine River, Manitoba, Canada, to evaluate a new approach to effectively capture and handle neonate bison. We captured bison neonates by using a modified .308 caliber net gun deployed from a utility terrain vehicle. We successfully captured and radio-tagged 10 male and 16 female neonate bison in 37 attempts (70.3% success). Over a period of 4 d, 16.0 labor h were spent pursuing and handling neonates, with an average capture rate of one bison neonate for every 0.6 labor h. Average handling time of bison neonates was 3.7 ± 1.6 min and ranged from 1.0 to 7.5 min. Results of our study indicate that our approach was effective and efficient for capturing and handling bison neonates safely. No injuries or capture related mortalities were observed throughout the handling and monitoring period. This technique will allow biologists and herd managers to capture neonates, monitor their survival, and collect cause-specific data on mortality of neonates in managed bison 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 | 0.000 | 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 | 0.001 |
| 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".