Bias corrections of precipitation measurements across experimental sites in different ecoclimatic regions of western Canada
Bibliographic record
Abstract
Abstract. This study assesses a filtering procedure on accumulating precipitation gauge measurements and quantifies the effects of bias corrections for wind-induced undercatch across four ecoclimatic regions in western Canada, including the permafrost regions of the subarctic, the Western Cordillera, the boreal forest, and the prairies. The bias corrections increased monthly precipitation by up to 163 % at windy sites with short vegetation and sometimes modified the seasonal precipitation regime, whereas the increases were less than 13 % at sites shielded by forest. On a yearly basis, the increase of total precipitation ranged from 8 to 20 mm (3–4 %) at sites shielded by vegetation and 60 to 384 mm (about 15–34 %) at open sites. In addition, the bias corrections altered the seasonal precipitation patterns at some windy sites with high snow percentage ( > 50 %). This study highlights the need for and importance of precipitation bias corrections at both research sites and operational networks for water balance assessment and the validation of global/regional climate–hydrology models.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".