Identification of radiative and advective populations in Canadian temperature time series using the Linear Pattern Discrimination algorithm
Bibliographic record
Abstract
Abstract Long‐term high‐frequency air temperature time series, typically considered the most authoritative observed records for the detection of climate changes, appear physically heterogeneous by nature. We examine multiple Canadian air temperature records for the presence of physical heterogeneities, using the analysis of their diurnal temperature patterns as the main criterion for the separation of temperature series into ‘homogeneous’ populations. Based on the key differences observed in their diurnal air temperature patterns, two distinct populations of the air temperature sample are identified and assumed to be the result of different heat exchange mechanisms. The Linear Pattern Discrimination (LPD) algorithm, implemented in the R‐code, is introduced in this work and applied to 66‐year long hourly temperature records of 25 Canadian stations for the separation of the radiative temperature population from the advective temperature population and examination of incidences of specific, advective cases in air temperature data. The LPD analysis reveals a predominance of a remarkably warmer, radiatively driven air temperature regime. In contrast, the significantly colder and geographically controlled advective temperature regime plays a counterbalancing role on the overall magnitude of the midlatitude air temperature signal. Our findings suggest that a substantial temperature increase in annual averages of advective minima amplifies the effect of a positively shifted radiative temperature range, intensifying the overall heating observed in the Canadian North and northwestern regions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".