Bibliographic record
Abstract
This paper describes the collection, checking and homogenisation of a Canadian atmospheric surface pressure database. The object of the exercise was to create a database of monthly mean surface pressure for as many stations as possible across Canada as far back in time as possible. Data sources included the World Weather Records, Monthly Climatic Data for the World Bulletins, the Global Historical Climate Network and the electronic meteorological report archives of Environment Canada. Much of the earlier data was in paper form and had to be digitized by hand. Over 66,000 individual mean monthly pressure values were obtained, with a missing value rate of 5.9%. The homogenisation procedures used were the Standard Normal Homogeneity Test (SNHT; Alexandersson and Moberg 1997) and Multiple Comparison Analysis (MCA; as used by Slonosky et al 1999). In addition, simple subtraction of sea-level pressure from station-level pressure revealed a major inhomogeneity which took place in 1977, when computer generated pressure reduction tables were used for the first time by the Meteorological Service of Canada, and when the meteorological reporting procedure was brought into alignment with the World Meteorological Organisationâs guidelines. As a result, the final homogenised database shows appreciable differences in trends compared to the unhomogenised series. The final database has been used by Slonosky & Graham (2003) in the statistical analysis of trends and variability of surface pressure across Canada during the 20th century. Published in Proceedings of Fourth seminar for homogenisation and quality control in climatological databases. Budapest, Hungary. 6-10, October 2003.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 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".