Technology Evaluation System for Detection of CO2 and CH4 in Deepwater Fields
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 the deep water pre salt Santos Basin Petrobras is interested in potential monitoring technologies for detection of carbon dioxide (CO2) in seawater at depths between 1200 and 2600 meters. At these depths, CO2 is not a gas but a buoyant liquid with densities similar to sea water, which makes detection challenging. Also potentially present with CO2 is methane (CH4), which at these depths is close to liquid-hydrate combination, which may possibly cause interference with some measurement techniques used for CO2. The Federal University of Rio de Janeiro (UFRJ), Ocean Networks Canada (ONC) and Petrobras are developing a test system where carbon dioxide (CO2) and methane (CH4) can be released in controlled amounts to a variety of test instruments at a depth of 2660 meters located over a secure Internet connection with collecting sensor and live video monitoring. So far, the project has selected several sensors developed by universities, research centers and companies around the world, using different technologies including fiber optic, acoustic and pH. Enabling this in situ real time experimentation is the ONC NEPTUNE cabled observatory. With over 800 kilometers of electro-optic cable to depths of 2660 meters the observatory provides power and Internet connectivity to hundreds of underwater instrumentation making it an ideal laboratory to evaluate technologies in situ. We will describe the design of the experiment at the Cascadia Basin site.
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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 | 0.000 |
| 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 it