Investigation of enhancements to two fundamental components of the statistical interpolation method used by the Canadian Precipitation Analysis (CaPA)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Canadian Precipitation Analysis (CaPA) generates gridded precipitation data outputs based on the assimilation of both observation and climate model data. CaPA outputs are highly valuable to modelling efforts dependent on precipitation inputs, and as such the quality of CaPA outputs is crucial. Two improvements to CaPA were investigated: reducing transformation bias though correction against moving-window averaged CaPA output that avoids transformation, and enhancing semivariograms through anisotropy and convection considerations. Accounting for convection in the semivariogram proved ineffectual, while the bias correction technique and anisotropic semivariograms both reduced bias and improved related metrics. No methods improved the Equitable Threat Score. If implemented separately, the bias correction or anisotropic semivariogram approaches will yield targeted benefits for CaPA users, particularly for applications focused on extreme precipitation values. Improvements were not so comprehensive as to warrant adoption in the operational CaPA configuration, although availability in experimental versions is recommended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 it