Trend and pattern classification of surface air temperature change in the Arctic region
Bibliographic record
Abstract
Abstract Monthly seasonally adjusted temperatures above latitude 45°N were investigated from January 1973 to November 2013. The study area was divided into 69 sub‐regions of similar size each in the shape of an igloo brick. The data were filtered with a second‐order autoregressive process to remove autocorrelation. Two sub‐regions did not have sufficient data due to substantial numbers of missing values. Factor analysis was then applied to the remaining 67 sub‐regions and was used to classify regions with similar temperature changes. As a result, 63 sub‐regions could be classified based on 12 factors but 4 sub‐regions could not be grouped due to uniqueness. The temperatures for each group of sub‐regions were found to increase during 1973–2013. The largest temperature increases of 0.19 °C/decade were found in northern and southern Siberia and part of the Arctic Ocean. In northern Canada, Alaska, the northern Pacific Ocean and eastern Siberia the temperatures increased by at least 0.16 °C/decade. In Iceland, Norway, Sweden and part of the Pacific and Arctic Oceans the temperature increased by around 0.15 °C/decade. In northeastern Canada, Greenland and its surrounding Atlantic Ocean and the Arctic Ocean the temperature increased by about 0.15 °C/decade.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".