Weather Data Quality Control Procedures in the Alberta Agriculture Drought Monitoring Network (AGDMN)
Bibliographic record
Abstract
Quality controlled weather data is a key component of a modern agricultural and environmental protection program. In order to meet the need for near- real-time quality weather data in Alberta, the Alberta Agriculture Drought-Monitoring program initiated the development of a standard automated weather station network across Alberta, known as Agricultural Drought Monitoring Network (AGDMN). The network started with 21 stations in 2001, and has grown to 37 stations with current plans under way to build more than 60 new stations. Alberta Agriculture also makes use of historical and near- real- time reported weather data collected by different agencies in the province. Effective design and operation of a modern weather monitoring networks should take a systems approach that considers all aspects of the weather stations network system ranging from station siting, operation, maintenance and quality data reporting that meets the needs of potential users. A science based, reliable quality control and assurance procedure is vital in delivering credible quality data to the users. While establishing and implementing quality control and assurance procedure, it is important to build on the experience of existing networks. This paper discusses the quality control assurance procedure adapted by AGDMN to provide a research quality, standard weather data to be delivered to users online. The quality control and assurance procedure consists of field and laboratory inspections, computerized prescreening procedure, flagging suspect and invalid data points, followed by manual data inspection, which are tied together with efficient communication.
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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 | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".