Bibliographic record
Abstract
Ptarmigan stew for breakfast, pilot biscuits for lunch, 'catch-of-the-day' stew for dinner-walking 10 miles or more a day, usually over rough terrain, often in snow or slush, sometimes wading knee-to waist-deep across frigid streams-shooting and trapping birds and mammals for specimens for the National Museum of Canada.Each day, cold, wet, and tired.Living and working with only two changes of clothing for several months.Sleeping on the ground in a small tent that seldom stops flapping in the brisk winds from the northern quadrants, in sleeping bags that get wetter by the day, and longing for a sunny day.Could life get any better?As a teenager, Andrew was already living adventures that are the dreams of many teenage boys.From 1949 to 1957, before joining the Canadian Wildlife Service (CWS), he gained a lifetime of memories and valuable experience as a member of eight scientific expeditions to the Canadian Arctic.He served as a seasonal field assistant to scientists, working on contracts for the Department of Mines and Technical Surveys, the Defence Research Board, the Arctic Institute of North America, the Department of Northern Affairs, and the National Museum of Canada.He made most of his early trips to the Arctic in the company of his mentor and lifelong friend Thomas (Tom) Henry Manning, famed Arctic explorer and geographer-
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.018 | 0.015 |
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; both teacher heads agree on what is shown here.
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".