A quality control system for historical in situ precipitation data
Bibliographic record
Abstract
In situ precipitation data are recorded at observing stations typically using either manual or automated gauges (some countries have ruler measurements of snowfall, which were then converted to their water equivalent using some version ratio). Unfortunately, there are random erroneous values, which could be unusually large values or false 0s. The latter usually arose from mis-recorded missing values (i.e., missing values were mis-recorded as 0 precipitation in the climate Archive). In doing quality control (QC) of Canadian in situ precipitation data records, we have found that it is necessary to apply a pair of QC procedures to identify these two types of random errors: one procedure is applied to the untransformed monthly precipitation series, which is good at finding outliers of unusually large values; another is applied to the log-transformed monthly precipitation series, log(P+0.1) (in mm), which is good at identifying outliers of zero or near-zero monthly total precipitation. The four nearest stations’ data for the same month are used to determine if the suspect outlier is a real extreme value or an erroneous value. All the monthly values identified to be erroneous are set to missing, and so are the corresponding daily values.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.036 |
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".