Bibliographic record
Abstract
Abstract The monitoring of cloud amount and type in Canada is discussed in detail, including observing, archiving, data transmission procedures and practices, and automation. There have been some major monitoring challenges since 1953. In 1977, the network‐wide replacement of detailed cloud layer amounts and obscuring phenomena by broad sky conditions, based on summation amounts, imposed analysis of frequency of occurrence of mainly cloudy conditions rather than actual amounts. Partial automation with Automated Weather Observing Systems resulted in the cessation of observations of higher clouds and cloud types, as well as the incompatibility of sky coverage with human observations at eight percent of stations. For every hourly report from eighty‐four airport stations from 1953 to 2003, each layer is classified according to cloud type and related standard base height into three levels of low, middle, and high clouds. Trends in occurrence of summation amounts of mainly cloudy conditions at each of these three levels are computed on annual, seasonal, daytime and nighttime scales, together with annual trends in occurrence of selected convective and stratiform clouds. Based on annual anomalies averaged over the country and provinces, no major network‐wide systematic discontinuities were noted; on average, on an annual basis over the entire network, slight decreasing trends are noted for summation amounts of mainly cloudy conditions at low and middle levels, and increasing trends at high levels. The increasing trend at high levels is indeed remarkable. The rate of increase, especially rapid until 1974, has been shown to be caused by a prominent increase in cirrus cloud reports. The link between this rise and the increase in air traffic was established by others in the United States.. This link may also apply in Canada, which experienced a similar expansion in aviation. Notably, the largest increase in high nighttime cloudiness and decrease in low‐middle cloudiness is evident in western Canada, possibly contributing to the recently observed warming of daily minimum and maximum temperatures there. The occurrence of stratiform clouds at all levels exhibits significant decreasing trends across the country, except for southern Ontario. Clouds of intense convection show pronounced decreasing trends in western Canada, while not much change is evident elsewhere. Similar to cirrus, stratocumulus is notable as it shows strong positive trends everywhere in the country. On the other hand combined stratus and stratus fractus clouds exhibit decreasing trends, except over British Columbia where the opposite occurs. The findings concerning stratocumulus, stratus and stratus fractus clouds in Canada are similar to the findings in the United States.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".