Climate Change Effect on Winter Temperature and Precipitation of Yellowknife, Northwest Territories, Canada from 1943 to 2011
Bibliographic record
Abstract
The correlation of the Southern Oscillation Index (SOI), Pacific Decadal Oscillation (PDO), Pacific North American Oscillation (PNA), Arctic Oscillation (AO), and Scandinavia (SCAND) indices with winter (DJF) temperature and precipitation for the period of 1943 to 2011 was analyzed to study climate change and variability of Yellowknife, NWT. SOI correlated negatively with both temperature (r = -0.14) and precipitation (r = -0.06) causing colder, drier conditions during La Nina and warmer, wetter conditions during El Nino. PDO was shown to have a strong positive correlation with both temperature (r = 0.60) and precipitation (r = 0.33) causing warmer, wetter weather in the positive phase and colder, drier weather in the negative phase. PNA showed the strongest positive correlation for both temperature (r = 0.69) and precipitation (r = 0.37) causing very warm and wet conditions in the positive phase and very cold and dry conditions during the negative phase. AO correlated negatively with temperature (r = -0.04) and positively with precipitation (r = 0.24) causing colder, wetter conditions in the positive phase and warmer, drier conditions in the negative phase. Finally SCAND was shown to have a weak negative correlation with both temperature (r = -0.10) and precipitation (r = -0.18). Sunspot area showed a strong negative correlation (r = -0.30) with temperature and a very weak positive correlation (r = 0.07) with total annual precipitation. Yellowknife’s average annual temperature and precipitation has increased by 2.5°C and 120 mm, respectively throughout the past 69 years.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".