A Quality Assurance System for Canadian Hourly Pressure Data
Bibliographic record
Abstract
Abstract In this study a comprehensive quality assurance (QA) system, which includes the hydrostatic check combined with a statistical homogeneity test, is designed and applied to hourly pressure records (for 1953–2002) from 761 Canadian stations, to produce a high-quality database of hourly station and sea level pressures for various climate studies. The main principles of the QA system are described in detail, followed by a brief emphasis on the error correction algorithms. The general performance of the QA system and the main problems in the Canadian historical hourly pressure database are discussed and illustrated through various examples. The results show that there are serious systematic errors (i.e., sudden changes in the mean, or mean shifts) in the Canadian hourly pressure database, which are caused either by the use of incorrect station elevation values in the reduction of barometer readings to station or sea level pressure values (e.g., the “50-ft rule” or station relocation without updates to the station elevation), by transposing/swapping station and sea level pressure values, or by mistakes made in the archive data ingestion or data recording/digitization processes (e.g., use of a wrong base number). Random errors also exist and are mainly due to the transposition of two digits or miscoding of one or two digits. These errors must be corrected before the data are used in various climate studies, especially climate change–related studies.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".