Bibliographic record
Abstract
Climatological observations are available for Fairbanks, Interior Alaska, for up to 100 years. This is a unique data set for Alaska, insofar as it is of relatively high quality and without major breaks. Applying the best linear fit, we conclude that the mean annual temperature rose from -3.6°C to -2.2°C over the century, an increase of 1.4°C (compared to 0.8°C worldwide). This comparison clearly demonstrates the well-known amplification or temperature change for the polar regions. The observed temperature increase is neither uniform over the time period nor uniform throughout the course of a year. The winter, spring, and summer seasons showed a temperature increase, while autumn showed a slight decrease in temperature. For many activities, the frequencies of extremes are more important than the average values. For example, the frequency of very low temperatures (below -40°C, or -40°F) has decreased substantially, while the frequency of very high temperatures (above 26.7°C, or 80°F) increased only slightly. Finally, the length of the growing season increased substantially (by 45%) as a result of an earlier start in spring and a later first frost in autumn. Precipitation decreased for Fairbanks. This is a somewhat counter-intuitive result, as warmer air can hold more water vapor. The date of the establishment of the permanent snow cover in autumn showed little change; however, the melting of the snow cover now occurs earlier in the spring, a finding in agreement with the seasonal temperature trends. The records for wind, atmospheric pressure, humidity, and cloudiness are shorter, more broken, or of lower quality. The observed increase in cloudiness and the decreasing trend for atmospheric pressure in winter are related to more advection and warmer temperatures during this season.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".