Glider-Based Observations of CO <sub>2</sub> in the Labrador Sea
Bibliographic record
Abstract
Abstract. Ocean gliders can provide high spatial and temporal resolution data and target specific ocean regions at a low cost compared to ship-based measurements. An important gap, however, given the need for carbon measurements, is the lack of capable sensors capable for glider-based CO2 measurements. We need to develop robust methods to evaluate novel CO2 sensors for gliders. Here we present results from testing the performance of a novel CO2 optode sensor (Atamanchuk et al., 2014), deployed on a Slocum glider, in the Labrador Sea and on the Newfoundland Shelf. We demonstrate our concept of validating data from this novel sensor during a long glider deployment using a secondary autonomous observing platform – the SeaCycler. Comparing data between different sensors and observing platforms can improve data quality and identify problems such as sensor drift. SeaCycler carried an extensively tested gas analyzer: the Pro-Oceanus's CO2-Pro CV, as part of its instrument float. The CO2-Pro CV has shown stable performance during lengthy observations (e.g. Jiang et al., 2014), but has a slow response time for continuous profiling, and its power consumption is not affordable for glider operations. This CO2 optode is an early prototype sensor that has not undergone rigorous testing on a glider, but is compact and uses little power. This paper summarizes the test results for this sensor on a Slocum glider. We capture the performance of the sensor, and for the Labrador Sea mission, comparing the glider data against the SeaCycler's measurements to compute an in-situ correction for the optode. We use the referenced data set to investigate trends in spatial and temporal variability captured by the glider data, pointing to short time and distances scales as drivers of change in this region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".