ICE FOG A S A PROBLEM OF AIK POLLUTION I N THE ARCTIC
Bibliographic record
Abstract
I T HE term “arctic ” frequently evokes an image of vast expanses of ice and snow and rock and tundra, with widely separated and sparsely populated settlements, where many of the normal problems of community life in the temperate regions are unknown. Whereas this picture may be geneyally true in respect to small, relatively static settlements, the rapid growth and develop-ment of larger communities, such as Fairbanks, Alaska, have brought wirh them many of the problems inherent in typical industrial communities through-out the world. Air pollution is one such problem. Air pollutants may be solid, liquid, or gaseous, and are usually produced by domestic and industrial heating plants. If certain meteorological conditions and topographical features combine, these products of combustion are held in suspension in the air and increase in concentration until troublesome effects occur. In many com-munities in the Alaskan and Canadian Arctic, air pqllution during the winter months manifests itself primarily as ice fog. These fogs are troublesome because they frequently reduce visibility sufficiently to hamper both aircraft and automobile operations.
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 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".