An Evaluation of the Performance of Sea-Bird Scientific's Autonomous SeaFET <sup>TM</sup> : Considerations for the Broader Oceanographic Community
Bibliographic record
Abstract
Abstract. The commercially available Sea-Bird SeaFETTM provides an accessible way for a broad community of researchers to study ocean acidification and obtain robust measurements of seawater pH via the use of an in situ autonomous sensor. There are pitfalls, however, that have been detailed in previous best practices for sensor care, deployment, and data handling. Here, we took advantage of two distinctly different coastal settings to evaluate the Sea-Bird SeaFETTM and examine the multitude of scenarios in which problems may arise confounding the accuracy of measured pH. High-resolution temporal measurements of pH were obtained during 3- to 5-month field deployments in three separate locations (two in south-central, Alaska, USA, and one British Columbia, CA) spanning a broad range of nearshore temperature and salinity conditions. Both the internal and external electrodes onboard the SeaFETTM were evaluated against robust benchtop measurements for accuracy utilizing either the factory calibration, an in situ single-point calibration, or in situ multi-point calibration. In addition, two sensors deployed in parallel in Kasitsna Bay, AK, USA, were compared for inter-sensor variability in order to quantify other factors contributing to SeaFETTM intrinsic inaccuracies. Based on our results, the multi-point calibration method provided the highest accuracy (
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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 | 0.004 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".