Metrological Evaluation of Deep-Ocean Thermometers
Bibliographic record
Abstract
An accurate metrological investigation was performed on several units of the two de facto standards for deep-ocean temperature measurements—the SBE35 (the reference thermometer) and the SBE3 (the working thermometer) from SeaBird Scientific. Four SBE35 units were repeatedly calibrated against state-of-the-art fixed-point cells (triple point of water at 0.01 °C and melting point of gallium at 29.7646 °C), with calibration uncertainties of approximately 0.2 mK and 0.5 mK, respectively. Three SBE35 units and one SBE3 unit were calibrated in the temperature range 0 °C to 30 °C, again with sub-millikelvin calibration uncertainties, in a recently-developed water–bath calibration facility. All these calibrations evidenced (1) the deviation of each unit from its original manufacturer’s calibration (up to 1.7 mK), which were found to be inconsistent with the manufacturer’s uncertainty claims and (2) unexplained irreproducibilities, which could not be attributed to the calibration system of up to 1.5 mK. The effect of high pressures (up to 60 MPa) on the response of two SBE35 units was investigated by pressurizing the SBE35 units inside a purpose-built compact pressure enclosure. The results of the pressure investigation confirmed the existence of a small device-dependent pressure effect (approximately 0.3 mK at 60 MPa) and the need for individual temperature–pressure calibration of each SBE35 unit.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".