Searching Towards Creating a Sustainable Integrated Mesonet for the Canadian Prairie Provinces
Bibliographic record
Abstract
We assess how weather events on the Canadian Prairies during the 2012 growing season were temporally and spatially represented by a high-resolution, non-World Meteorological Organization (WMO) standard private provider network, Earth Networks (EN), relative to the WMO standard climate stations operated by federal and provincial governments. We found that there was a large amount of missing station data in the EN network. We noticed the appearance of two significant patterns. The EN stations had higher hourly temperature values later in the day and higher daily Tmin than their nearest neighbouring governmental stations. The EN stations also recorded less 24-hour and 1-hour precipitation than their nearest neighbouring governmental stations. However, overall, the EN stations and the various governmental stations were complementary, often with one network being dense where the other one was sparse and vice versa, thereby giving a more spatially explicit picture of five storms during the 2012 growing season. Our ultimate objective is to show the benefits of including the high-resolution EN weather data, together with government station data, in a permanent, formally established, integrated mesonet for the Canadian Prairies, with the benefits of both the governmental station data and the private station data.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".