A SENSOR FOR IN SITU SPECTROPHOTOMETRIC MEASUREMENTS OF SEAWATER pH
Bibliographic record
Abstract
The design of small autonomous sensors for measuring parameters in the ocean has been an important area of research. The development of a seawater pH sensor which is highly accurate and precise has unlimited potential for oceanographic studies. A seawater instrument for pH measurements was built based upon the design of the SAMI-pH for freshwater. The performance of the freshwater design in a seawater setting was unknown. During a cruise in the Labrador Sea the first design of the SAMI-pH for seawater based upon the previous instrument design was tested. The results showed promise but indicated that improvements needed to be made to the stability and reproducibility of the instrument to achieve the desired accuracy of 0.002 pH units and precision of 0.001 pH units. Since a full scale optimization of the SAMI-pH had never been undertaken, multiple parameters were tested. The old Delrin flow cell was replaced with a custom made Z-cell, which improved the absorbance precision. A commercially available static mixer was used to produce reproducible mixing of the indicator and sample. The indicator concentration and indicator pH were optimized to maximize the linearity of the [mCP]T vs. pH plots and to reduce the flushing volume which increased the deployment length. A new method of calculating molar absorptivity ratios was evaluated and the SAMI-pH accuracy was found to be 0.0017 when compared to the Cary UV/Vis. The precision showed excellent improvement from 0.004 to 0.0007 with the new SAMI-pH design. In situ testing of two SAMI-pH instruments was completed at Scripps Institution of Oceanography. Agreement of -0.0047 pH units between the two SAMI-pH instruments during a 22-day period with 883 measurements illustrates the highly accurate and stable performance of the SAMI-pH. An alternative method for pH determination based on theoretical calculations was also tested. The average offset between pH values calculated from the linear extrapolation technique and the theoretical model was -0.0008 pH units illustrating that the linear extrapolation is a rigorous method for determining the pH of the solution.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".