Noninvasive Fluid Identification: Potential of Micro-Raman Spectroscopy
Bibliographic record
Abstract
Abstract Conservation of the preserving medium is an essential element for the proper preservation of specimens in fluid collections. However, the preservatives can become chemically altered over time or be lost by processes such as evaporation. To combat such changes and properly care for and maintain immersed specimens, it is therefore necessary to know what preservative fluid was initially chosen and how its chemistry may have evolved with time. The present work explores the possibilities offered by Raman spectrometry for a rapid, nondestructive, noninvasive alternative to commonly employed chemical identification tests, which are often limited to the identification of simple fluids. In a first step, fluids were reconstituted and analyzed in small standard glass containers to evaluate the potential of the technique. Then we successfully applied the procedure to real cases and considered its possible use to estimate the concentration of ethanol and to detect small quantities of formaldehyde (down to 1%). The results demonstrate the power of this technique, which opens up new possibilities for the management of fluid collections.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".