An Analysis of Alberta’s climate. Part II: Homogenized data
Bibliographic record
Abstract
This report is the second of two exploratory analyses of climate data for the Province of Alberta (precipitation and temperature) with the ultimate goal of developing new stochastic models for the processes involved. Its predecessor, Part I, presents a large number of findings that, like their confirmations in this report as well as its additional conclusions, provide foundations for statistical modeling these climate data and derivatives from them such as extreme values. However the data explored in Part I unlike those investigated here, had not been homogenized, that is adjusted for anomalies due to such things as changes in instrumentation over time. Thus its conclusions might better be viewed as hypothesis for further investigation and confirmation. However, on the positive side that data came from a very large number of monitoring sites covering the Province extensively. Like Part I, Part II uses standard tools for exploratory data analysis. The lessons learned from that analysis are summarized as a basis for future modeling. Some of background material in Part I is repeated in this report for completeness. 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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 teacher head, 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".