Biofouling of an All-Optical Sensor for Seafloor Monitoring of Marine Carbon Capture and Storage Sites
Bibliographic record
Abstract
Fiber-optic sensors for dissolved CO2 are an emergent technology for monitoring marine geologic CO2 sequestration sites. Fiber- optic sensing technology has been used successfully in the oil and gas sector and is advantageous because it is capable of cost- effective, instantaneous, distributed sensing. This is an improvement over current practice, which normally requires samples to be pumped to the surface for analysis. Biofouling of fiber-based sensors is a concern for the marine environment, as the biofouling can cause signal drift and also adversely impact the sensor's ability to detect dissolved CO2. Single mode optical fibers with long period gratings etched onto the core of the fiber were used for this study. Pseudoalteromonas sp. NCIMB 2021 was cultured and grown on sensing elements using nutrient-dense synthetic seawater. Biofouling was shown to cause shifts in the baseline signal. Post-fouling sensitivity of the sensor was also reduced relative to pre-fouling levels. Mechanical cleaning of the sensors restored sensor sensitivity to that seen prior to bacterial colonization.
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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 | 0.000 | 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 | 0.000 | 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".