Magnitude, consequences and correction of<scp>temperature‐derived</scp>errors for absolute pressure transducers under common monitoring scenarios
Bibliographic record
Abstract
Abstract Continuous water level monitoring using absolute pressure transducers with onboard data logging is common practice in hydrologic studies. While there has been some discussion and study of temperature‐derived error (TDE), there has not been a systematic evaluation of the problem. We sought to answer three questions: (1) are current best practices enough to avoid these errors, (2) can laboratory correction be used to correct field data from varying conditions, and (3) what is the scale of the additional uncertainty of the correction procedure? We evaluated the magnitude of such errors under laboratory conditions that mimicked common monitoring scenarios. Using field data, we also demonstrated the impact of TDEs on calculated daily mean water level and diurnal signal decomposition to estimate evapotranspiration (ET). To address instrument and model uncertainty, we fit 1000 possible correction models using a double‐bootstrap approach. Correction models fit expected error as a function of water and air temperature and rate of change of air temperature. TDEs were a significant source of error, resulting in recorded data outside of manufacturer‐stated instrument uncertainty, with 45% of bootstrap models showing significant but small TDEs under best‐practice deployment. Correction equations did introduce additional error, often on a much smaller scale than instrument uncertainty. When tested against a validation data set, correction equations effectively reduced total measurement uncertainty below instrument uncertainty by up to 65%. The effects of TDEs on case‐study field data resulted in 56% of daily mean values outside of instrument error bounds (errors: −1.5 to 4.2 cm). Our results suggest that a single laboratory correction equation can be used across monitoring scenarios, though we suggest matching deployment conditions as closely as practical. Identification and correction of TDEs is essential to avoid erroneous conclusions, downstream analyses and water resources management.
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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".