Deep Sea Laser Raman – Past, Present and Future Developments for In Situ Chemical Analysis and Applications
Bibliographic record
Abstract
Abstract The Deep Ocean Raman In Situ Spectrometers (DORISS instruments) where developed at the Monterey Bay Aquarium Research Institute (MBARI) for the purposes of identifying compounds and studying in situ chemical reactions in a non-destructive manner while working with solids, liquids and gases. The approach taken began with modification of a standard bench top lab unit for use at sea to demonstrate feasibility. The second generation then solved several impediments to improved performance in the deep sea as well as development of additional tools to enhance capabilities. The third generation is now past the design process and in assembly for bench top testing to bring the fluorescence signal (noise) down to achieve a 19 fold increase in sensitivity. This allows the use of Raman on compounds that have high order organic compounds such as some oils and makes the detection of CO2 much easier. The high power approach in this system will enable faster operations for subsea mining. The first instrument (DORISS I) was greatly successful and perform science experiments from Canadian waters to the Gulf of California looking at hydrates as well as higher order compounds, gas vents, phase boundaries and the ice cage structures entrapping methane as well as other gases. DORISS I was able to determine thermogenic versus biogenic methane for example. DORISS II increased sensitivity and performance by reducing the pressure effects on the optical system. Precision pointing devices were incorporated to look below the surface of some compounds as well as vertical and horizontal scanning of in situ chemical processes. A pore water probe was developed which allowed for very precise vertical profiling of sediments showing to properly access the methane content in the ocean bottom sediments which in general is greatly underestimated using tube cores and other sampling methods with shipboard analysis. The next generation DORISS, currently in development, promises to give gains in sensitivity as well as the ability to obtain Raman signatures from volatile compounds which would normally be unclassified due to fluorescence. The application of Raman has revolutionized the medical industry and promises to do the same for oil & gas as well as subsea mining. With the trends in land based Raman spectrometers toward lower costs it can be argued that these techniques enable industry to employ a new tool and manage ocean resources more wisely.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".