SUBMERSIBLE OPTICAL SENSORS EXPOSED TO CHEMICALLY-DISPERSED CRUDE OIL: WAVE TANK SIMULATIONS FOR IMPROVED OIL SPILL MONITORING
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In situ fluorometers were deployed during the Deepwater Horizon (DWH) Gulf of Mexico oil spill to provide critical measurements for tracking the subsea oil plume. In the wake of the spill, uncertainties regarding instrument specifications, capabilities and reliability necessitated performance testing of sensors (commonly used during spill response) exposed to simulated, dispersed oil plumes. Moreover, concerns on the applicability of laboratory calibrations (at high concentrations and insufficient mixing energies) to field conditions and on sensor reliability to detect dispersed oil persist. To address these uncertainties the performance of select commercially-available sensors (from Chelsea Technologies Group, Satlantic, Turner Designs, WetLabs Inc) was evaluated using a wave tank facility at the Bedford Institute of Oceanography in Halifax, Nova Scotia. Breaking waves were generated within the tank to simulate mixing energies and achieve dispersant effectiveness observed in the field. Presented here are the results of the sensors exposed to chemically-dispersed MC252 crude oil using Corexit 9500, DOR=1:20. Stepwise additions of dispersed oil (0.3 – 12 ppm) to the tank were used to establish linearity. Model 1 linear least squares regressions were calculated and applied to sensor data during validation experiments to simulate dilution of an oil plume. Dynamic ranges of the sensors, exposed to fresh and artificially weathered crude oil, were determined. Sensors were standardized against known oil volumes and measured Total Petroleum Hydrocarbons (TPH) and Benzene-Toluene-Ethylbenzene-Xylene (BTEX) values – both collected during spills, providing oil estimates during dilution experiments. Results were validated against particle size data (Sequoia LISST). All sensors estimated oil concentrations down to 300 ppb oil, refuting previous reports. Low percent differences and absolute errors between chemistry and sensor results were metrics to evaluate performance. Discussed will be the application of this vicarious calibration approach as a means to calibrate the DWH fine-scale fluorescence data into oil concentrations. This allows for filling in coarse-scale field chemistry data, improved assessment of DWH spill measurements mined from the NOAA NODC, and understanding the fate and transport of the DWH oil plume.
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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 | 0.001 |
| 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) | 0.001 | 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 it