Long-term annual climate trends around the Breton Plots area, Alberta: is there any evidence of local climate change?
Bibliographic record
Abstract
The objective of this research was to investigate the long-term trends in historical climate variables using the data collected near the classical Breton Plots (Alberta, Canada) and to determine if the data show any evidence of local climate change. The climate data used for the study were obtained from the Alberta Climate Information Service for the years 1901–2020. Various parametric statistical analyses were conducted to determine if monotonic trends occurred in the climate variables over time, and the analyses were conducted on the annual data as well as the 30 year climate normals. Large fluctuations in annual climate variables occurred, but a positive linear trend was observed in the average annual and growing season minimum air temperatures over time. Between 1901 and 2020 the annual minimum air temperature average increased at a rate of 0.3 °C for every 10 years. During the winter periods (December, January, and February, inclusive) from 1901 to 2020, the minimum air temperature average increased at an even higher rate of 0.5 °C every 10 years. Overall, the 30 year climate normals for minimum and maximum air temperatures increased for most seasons. The rates of increase were largest over the winter period, at 0.5 °C every 10 years for minimum air temperature and at 0.3 °C every 10 years for maximum air temperature. Strong linear increases occurred over time for growing degree days, number of frost-free days, total annual precipitation, growing season precipitation, and off-season precipitation.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".