Technology Evaluation System for Detection of CO2 and CH4 in Deepwater Fields
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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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".