Lab-on-Chip Total Alkalinity Sensor for Highly Resolved, Efficient, and Long-Term Monitoring, Reporting, and Verification of Ocean Alkalinity Enhancement
Bibliographic record
Abstract
Increasing atmospheric CO2from human activities is causing shifts in marine chemical and biological cycles. Ocean Alkalinity Enhancement (OAE) shows promise as a potential carbon dioxide removal (CDR) solution. However, comprehensive measurement, reporting, and verification (MRV) of OAE is required for the development and deployment of OAE as a CDR technology. The current observational approaches are not yet standardized or readily available to assess early-stage field trials. In this study, we report using a microfluidic lab-on-a-chip total alkalinity sensor in an OAE field experiment conducted in Tuft's Cove, Halifax, Canada to monitor seawater alkalinity during and after the addition of an alkaline mineral. To the authors knowledge, this is the first reported lab-on-chip sensor used in support of OAE mCDR. Over a 40-day period, our sensor collected 374 measurements along with 60 on-board standard measurements with the measurement frequency of one measurement per three hours. We report a precision of 9.2 μmol/kg which is the standard deviation of the CRM measurements. The data obtained during this trial demonstrates the capability of our technology for total alkalinity monitoring at OAE sites for long periods.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| 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 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".