Bibliographic record
Abstract
In October 2002, I attended a wildlife capture course for veterinarians in South Africa. The course was attended by 6 veterinarians. Three from Canada, (Drs. Casey Ringleberg and Dr. Bob Georgeson, both from Ottawa, and myself, Dr. Irene Phillips from Calgary), 1 from Alaska, 1 from England, and 1 from Norway. The instructor was Andre Pinaar, a wildlife capture professional. We spent 8 days learning how and why to capture and transport native African wildlife, such as lions, rhinos, giraffe, buffalo, and zebra. We spent 2 days in Kruger National Park. One was spent at the veterinary camp with the park's veterinarian, Dr. Roy Bengis, and the head gunmaster, Fritz Rohr, who taught us how to load and fire the DAN-INJECT capture rifle. The 2nd day we spent as tourists touring the park. It was fabulous. On the last day of my trip, I went on a horseback riding safari. It was cut short, however, by the discovery of a dehydrated baby zebra that was left behind by his mother and was collapsed. We captured him and transported him back to the farm's barn and treated him. The capture was not without incident though. The baby zebra didn't appreciate being caught and he bit me on the leg. Lucky for me I was wearing jeans and they took the worst of it. The South African guys were wearing (and riding in) shorts. It was a great experience; not only to see the wildlife but to treat them. I would recommend this course to anyone interested in African wildlife. For more information on this program, contact Parawild Safaris at az.oc.dliwarap@irafas (submitted by Dr. Irene Phillips)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.457 | 0.195 |
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".