A new robust oxygen‐temperature sensor for aquatic eddy covariance measurements
Bibliographic record
Abstract
Abstract The fragility of thin Clark‐type glass microelectrodes used in aquatic eddy covariance measurements of benthic oxygen fluxes is a challenge when using this powerful technique. This study presents a new fast‐responding dual oxygen‐temperature sensor for eddy covariance measurements that is far more robust. Response time tests in the lab, where the sensor was inserted from air into water, revealed 90% response times of 0.51 s and 0.34 s for oxygen and temperature measurements, respectively. In wave tank tests, the new sensor showed no stirring sensitivity in contrast to Clark‐type microelectrodes. Other tests in a flume and in a particle‐free water tank revealed how close the sensor can be positioned to the measuring volume of the Acoustic Doppler Velocimeter without disturbing velocity recordings. In field tests at river sites, all > 24 h, the new sensor recorded high‐quality eddy covariance data for the entire deployment. Similar positive results were obtained in deployments at a marine site with unidirectional current flow overlaid with minor wave action. Concurrently deployed eddy covariance systems based on the new sensor and a traditional Clark‐type microelectrode revealed that they recorded statistically equivalent fluxes and similar velocity‐oxygen cospectra until the microelectrode broke after 2 h. The significant increase in robustness of the new sensor was achieved by relying on a larger sensor tip. This put some constrains on how the sensor should be deployed and fluxes extracted, but given the substantial gain in performance, it is a viable alternative for eddy covariance measurements in many aquatic environments.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".