Bibliographic record
Abstract
This paper discusses the common stereotype/fantasy that every Canadian owns and rides a polar bear and whether this would be possible in real life. The paper begins with a background on polar bear range and eating habits, and then goes on to discuss sources of food in Canada. It was assumed only everyone of driving age would own a polar bear, allowing a population of 2.99x10 7 polar bears. It would take either 9.02x10 5 cows, 2.3x10 6 hogs, or 7.4x10 8 chickens per day to feed that amount of bears. Using cows and chickens as the model animals, the amount of pasture needed to support that much food for a year is calculated to be 4.5x10 7 km 2 for cows, which is larger than the total landmass of Canada, and 2.7x10 8 km 2 for chickens. While the landmass of Canada could support the chickens, due to their waste and pollution, it is concluded that it would not be possible for every Canadian to own a polar bear.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".