All-Fiber Photoacoustic Absorption Spectroscopy: Detecting Small Amounts of Dissolved Water in Jet Fuel
Bibliographic record
Abstract
Small concentrations of dissolved water in hydrocarbons such as lubricating engine oils and jet fuels prove detrimental to engine performance. In particular, high altitude operation can cause dissolved water to freeze, thus disrupting fuel flow and ignition temperatures in the engine. A new method to determine the concentration of dissolved water is explored using a high energy laser to excite the dissolved water in the fluid sample. Past methods include rigorous filtration systems or humidity tests that are time consuming and sometimes extremely temperature sensitive. Using a laser is a safe and non-invasive method that takes a matter of minutes for data collection. This Physics thesis is currently in the stage of establishing the detection of water excited by the high power laser. In the coming weeks, this project will progress to analyzing the collected data to calculate the amount of dissolved water in the fluid samples. The time efficiency and precision of this method provides an opportunity to develop a device that may be extended to industry for engine damage mitigation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".