Measuring precipitation with a geolysimeter
Bibliographic record
Abstract
Abstract. Using the relationship between measured groundwater pressures in deep observation wells and total surface loading, a geological weighing lysimeter (geolysimeter) has the capability of measuring precipitation event totals independently of conventional precipitation gauge observations. Correlations between groundwater pressure change and event precipitation were observed at a co-located site near Duck Lake, SK, over a multi-year and multi-season period. Correlation coefficients (r2) varied from 0.99 for rainfall to 0.94 for snowfall. The geolysimeter was shown to underestimate rainfall by 7 % while overestimating snowfall by 9 % as compared to the unadjusted gauge precipitation. It is speculated that the underestimation of rainfall is due to unmeasured run-off and evapotranspiration within the response area of the geolysimeter during larger rainfall events, while the overestimation of snow is at least partially due to the systematic undercatch common to most precipitation gauges due to wind. Using recently developed transfer functions from the World Meteorological Organization's (WMO) Solid Precipitation Intercomparison Experiment (SPICE), bias adjustments were applied to the Alter-shielded, Geonor T-200B precipitation gauge measurements of snowfall to mitigate wind-induced errors. The bias between the gauge and geolysimeter measurements was reduced to 3 %. This suggests that the geolysimeter is capable of accurately measuring solid precipitation and can be used as an independent and representative reference of true precipitation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".