Theoretical Considerations of High-frequency Air Temperature Variations and Their Application to the Identification of Physical Heterogeneities in Canadian Temperature Time Series
Bibliographic record
Abstract
Theoretical considerations of high-frequency air temperature variations primarily address the representativeness of diurnal temperature extrema in climatological analysis and application and ask the questions related to extrema characterization, causes of systematic temperature biases, and presence of physically based heterogeneities in Canadian temperature time series. The main objective of this thesis is to lay the theoretical foundation for the study of physical heterogeneities in air temperature samples and to offer a practical algorithmic solution for the separation of temperature time series into the radiative and advective temperature components. The initial research question was, Is diurnal air temperature variation accurately represented by the average of daily temperature extrema, and what are the implications of using such averages in the estimation of temperature-related quantities? The answers led to more questions: Can observing windows bias air temperature observations, and which observing window is the most suitable for identification of diurnal mathematical extrema of the temperature-time function? What can improve the representation of diurnal temperature variation? The final question, and a confluence of all previous ones, was, Is high-frequency air temperature sample from northern midlatitudes physically heterogeneous, and if it is, what is the relationship between its components? The results indicate that: (i) Modification of the degree-day formula significantly improves the prediction accuracy of temperature-related quantities. (ii) An improved climatological observing window identifies correctly radiatively driven diurnal minima and remedies the “cold bias” in Canadian temperature observations. (iii) The critical element to an improved representation of diurnal air temperature variation is diurnal extrema timing. (iv) Separation of Canadian air temperature time series into physically distinct populations yields homogeneous radiative and advective air temperature components. The findings suggest that inattention to the physical make up of the air temperature sample can potentially lead to a significant underestimation of the radiatively driven air temperature signal. Finally, the information on the position of diurnal temperature extrema is a key to a plethora of climatological applications, few of which are the main subjects of this thesis.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".