History of the Aircraft Control and Warning System in Alaska: Air Defense of Arctic Skies, 2013 Update
Bibliographic record
Abstract
This report discusses a general history of the Soviet relations that lead to the development of the Aircraft Control and Warning System (AC&W) in Alaska. It provides a general assessment of the archaeological sites at AC&W along with sites eligible for inclusion in the National Register of Historic Places (NRHP). It also provides an oral history of worker experiences at AC&W. In 1997, the 611th Air Support Group (611 ASG), in coordination with the Alaska State Historic Preservation Office (SHPO), determined that the Aircraft Control and Warning (AC&W) System meets the eligibility criteria for inclusion in the National Register of Historic Places (NRHP) for its significant contribution to national and Alaskan history and its important engineering design (i.e., National Register Criteria A and C). The 611 ASG also determined that Cape Lisburne, Fort Yukon, Kotzebue, and Tin City retained enough original structures to be eligible for inclusion in the NRHP (611 CES 1998). Cape Lisburne was determined to be the best example among the four eligible sites. This report is an updated version of the 2002 report of the same name. (https://core.tdar.org/document/459179)
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 | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".