The Observation Record Length Necessary to Generate Robust Soil Moisture Percentiles
Bibliographic record
Abstract
Abstract The ability to use in situ soil moisture for large-scale soil moisture monitoring, model and satellite validation, and climate investigations is contingent on properly standardizing soil moisture observations. Percentiles are a useful method for homogenizing in situ soil moisture. However, very few stations have been continuously monitoring in situ soil moisture for 20 years or more. Therefore, one challenge in evaluating soil moisture is determining whether the period of record is sufficient to produce a stable distribution from which to generate percentiles. In this study daily in situ soil moisture observations, measured at three separate depths in the soil column at 15 stations in the United States and Canada, are used to determine the record length that is necessary to generate a stable soil moisture distribution. The Anderson–Darling test is implemented, both with and without a Bonferroni adjustment, to quantify the necessary record length. The authors evaluate how the necessary record length varies by location, measurement depth, and month. They find that between 3 and 15 years of data are required to produce stable distributions, with the majority of stations requiring only 3–6 years of data. Not surprisingly, more years of data are required to obtain stable estimates of the 5th and 95th percentiles than of the first, second, and third quartiles of the soil moisture distribution. Overall these results suggest that 6 years of continuous, daily in situ soil moisture data will be sufficient in most conditions to create stable and robust percentiles.
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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 | 0.009 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".