Bibliographic record
Abstract
The activities of hunting, fishing and trapping in northwest Alberta have histories, in all likelihood, as long as that of human occupation in the region. Whereas the original purpose of hunting, fishing and trapping were for subsistence purposes, these activities assumed market value with the arrival of Euro-Canadians. Today, non-aboriginal people who hunt, fish, and trap do so primarily for recreational value. Hunting, trapping, and fishing remain important traditional activities of aboriginal people in northwest Alberta. Of the various big game species hunted in northwest Alberta, highest number of hunters, hunter effort (hunter-days), and harvest occurs for moose, followed in rank declining order by white-tailed deer, mule deer, black bear, elk, and grizzly bear. Highest number of hunting days per animal harvested occurred for grizzly bear, followed in rank declining order by mule deer, white-tailed deer, elk, moose, and black bear. In general, the number of hunters in Alberta and northwest Alberta have declined during the past two decades. Changes in availability of big game populations to hunt, in societal attitudes towards hunting, and the urban/rural composition of the population, are likely contributors to these temporal changes in participation of hunting. Sport fishing, through both direct and indirect expenditures, contributes significantly to regional and provincial
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.658 | 0.308 |
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; the direct Gemma label and the distilled Codex classifier 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".