Conversion of Pressure to Depth for Moored Instruments Using a Reference Bottom Mounted Pressure Sensor
Bibliographic record
Abstract
A new method is proposed to convert pressure measured by an instrument to water depth using an additional available bottom-moored pressure sensor. A perturbation analysis is used in this analysis, which leads to a simple formula for calculating water depth (defined as one from the mean sea surface to the instrument) from the pressure data. In field experiments, this method is easier to apply than existing methods. Based on the theoretical derivation, the error associated with the method comes from two sources when the instrument depth is known at the beginning of the measurement: temporal variation of deep water density at depths deeper than the instrument and variation in the gravitational acceleration with instrument displacement. These two sources contribute up to 4% of the error relative to the vertical displacement of the instrument, assuming the pressure sensor is accurate. With the vertical displacement of the instrument being on the order of 10 m, the absolute error is on the order of 0.4 m, which is expected and acceptable in oceanic measurements. The method is applied to data from a field experiment that took place along the Myanmar coast in December 2012.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".