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 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. This paper (1) investigates the performance of the CO2 optode on two glider deployments, (2) demonstrates the utility of using the autonomous SeaCycler profiler mooring (Send et al., 2013; Atamanchuk et al., 2020) to improve in situ sensor data, and (3) presents data from moored and mobile platforms to resolve fine scales of temporal and spatial variability of O2 and pCO2 in the Labrador Sea. The Aanderaa CO2 optode is an early prototype sensor that has not undergone rigorous testing on a glider but is compact and uses little power. Our analysis shows that the sensor suffers from instability and slow response times (τ95>100 s), affected by different behavior when profiling through small (<3 ∘C) vs. large (>10 ∘C) changes in temperature over similar time intervals. We compare the glider and SeaCycler O2 and CO2 observations and estimate the glider data uncertainty as ± 6.14 and ± 44.01 µatm, respectively. From the Labrador Sea mission, we point to short timescales (<7 d) and distance (<15 km) scales as important 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".