Field evaluation of a low‐powered, profiling <i>p</i>CO<sub>2</sub> system in coastal Washington
Bibliographic record
Abstract
Abstract Summertime upwelling of deep, corrosive waters on the continental shelf of the northern California Current System can exacerbate ocean acidification conditions, providing unsuitable environments for development of calcifying organisms and finfish that are important to the local economy. To better understand the carbonate system in this dynamic region, two recently developed technologies were combined with other sensors to obtain high‐frequency carbon profile data from July 2017 to September 2017. The compact, low‐power sensor package was composed of an optical sensor for partial pressure of carbon dioxide ( p CO 2 optode, Aanderaa model #4797) integrated onto a wave‐powered PRofiling crAWLER (PRAWLER). The PRAWLER profiled from 3 to 80 m, stopping at fixed depths for varying lengths of time to allow for p CO 2 equilibration. p CO 2 derived from a regional empirical algorithm was used to correct optode drift using data at 80 m. Near‐surface adjusted optode p CO 2 agreed within 6 ± 42 μ atm to surface p CO 2 from a nearby Moored Autonomous p CO 2 instrument. Throughout the water column, optode p CO 2 compared to algorithm p CO 2 within −28 ± 66 μ atm. Overall, optode uncertainty was 35–72 μ atm based on root‐mean‐square errors from all comparison data sets. Errors are attributed to optode calibration, adjustment, algorithm uncertainty, and environmental variability between optode and reference data. Improvements for optode performance within this profiling application include using more stable sensing foils, in situ calibration, and pumped flow over the sensing foil. Additionally, the study revealed undersaturated (corrosive) waters with respect to aragonite below 60 m throughout the deployment that reached up to 40 m by mid‐September.
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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 | 0.002 | 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".