2003b. Monitoring precipitation over the Arctic terrestrial drainage system: Data requirements, shortcomings, and applications of atmospheric reanalysis
Bibliographic record
Abstract
An effort is under way aimed at historical analysis and monitoring of the pan-Arctic terrestrial drainage system. A key element is the provision of gridded precipitation time series that can be readily updated. This has proven to be a daunting task. Except for a few areas, the station network is sparse, with large measurement biases due to poor catch efficiency of solid precipitation. The variety of gauges used by different countries along with different reporting practices introduces further uncertainty. Since about 1990, there has been serious degradation of the monitoring network due to station closure and a trend toward automation in Canada. Station data are used to compile monthly gridded time series for the 30-yr period 1960–89 at a cell resolution of 175 km. The station network is generally sufficient to estimate the mean and standard deviation of precipitation at this scale (hence the statistical distributions). However, as the interpolation procedures must typically draw from stations well outside of the grid box bounds, grid box time series are poorly represented. Accurately capturing time series requires typically four stations per 175-km cell, but only 38 % of cells contain even a single station. Precipitation updates at about a 1-month time lag can be obtained by using the observed precipitation dis-tributions to rescale precipitation forecasts from the NCEP-1 reanalysis via a nonparametric probability transform.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".