Bibliographic record
Abstract
Today there are approximately 222 million acres (90 million ha) of federal land in Alaska - that’s about 60 percent of the state. And of that vast acreage, there are about 57.5 million acres (23.3 million ha) of designated wilderness, along with some 16.5 million acres (6.7 million ha) of proposed wilderness areas. Alaska’s designated wilderness acreage makes up approximately 54 percent of the entire nation’s wilderness, but it’s only about 26 percent of Alaska’s public lands. So depending on your point of view, the amount of Alaska’s wilderness acreage is either a triumph or an opportunity not yet fulfilled. And Alaska has one more singular distinction: more than 99 percent of the state’s existing and proposed wilderness areas were established by the stroke of one man’s pen. How those wilderness areas came to be, and why so much wilderness acreage was preserved all at one time, has as much to do with Alaska’s geography and politics as with any other factor. In the popular book, The Nine Nations of North America, Joel Garreau (1981) characterized a huge expanse that included Yukon Territory and Alaska, where climate dictated that people and their improvements would be scattered more thinly than elsewhere, as the “Empty Quarter.” Not surprisingly, quite a few of our country’s wilderness areas are found in the Empty Quarter, but the scattered few that live there have usually been pragmatic thinkers who are far more concerned about utilization and commercial development on the land than the esthetic joy of preservation.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".