Analysis of Daily Air Temperatures across a Topographically Complex Alpine Region
Bibliographic record
Abstract
ABSTRACT. This study provides an analysis of six years of daily air temperature data collected using 16 HOBO ® UA-002-64 Pendant data loggers placed along a 280 km transect in southwestern Yukon and northern British Columbia. Correlation and time series analyses, including Seasonal Decomposition of Time Series by Loess (STL) methods, revealed very high correlations among all data series at daily to annual timescales. The two meteorological stations in the region are found to be generally representative of the greater area, and local temperature variability appears to be predominantly determined by synoptic-scale weather patterns. The annual temperature cycle in this region is complex and has annually repeating components at all study sites across the region. The analysis of daily data using the STL method can provide new insight into climate time series and enhance our ability to observe patterns and extremes in temperatures across varying spatial and temporal scales. Data loggers provide a cost-effective way of obtaining similar (and sometimes higher-quality) information compared to meteorological stations or gridded global datasets. Key words: Yukon climate, HOBO data logger, meteorological station network, daily and hourly temperature dataset, time series analysis, seasonal-trend decomposition (STL) RÉSUMÉ. Cette étude présente l’analyse de données sur la température de l’air prélevées au cours de six années à l’aide de 16 enregistreurs de données HOBOMD UA-002-64 Pendant le long d’un transect de 280 km situé dans le sud-ouest du Yukon et le nord de la Colombie-Britannique. L’analyse des corrélations et des séries temporelles, faisant notamment appel aux méthodes
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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 | 0.000 | 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) | 0.000 | 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".