Freshwater sample preservation for the analysis of dissolved low molecular mass thiols
Bibliographic record
Abstract
Abstract Low molecular mass (LMM) thiols are ubiquitous organosulfur peptides that play key roles in biogeochemical element cycling. These LMM thiols, found at low concentrations in the water column, are highly sensitive to oxidation and degradation processes which may lead to problems with detection and analysis. Natural water samples should be stored and handled appropriately to reduce thiol loss in the time period from sampling to analysis. Storage temperatures were investigated for optimal preservation of four thiols species. Thiol degradation varied between species and was generally slower in samples stored at −80°C and 4°C compared to −20°C and 21°C. Two natural freshwater matrices were tested, one from a fluvial lake subject to agricultural inputs and one from an oligotrophic pristine lake. After 6 d of storage at −20°C, the most affected thiol species was glutathione with a degradation rate (kD) of (3.0 ± 0.5) × 10−3 h−1 in the fluvial lake water and l‐cysteine‐l‐glycine (CYS‐GLY) with a kD of (3.8 ± 0.8) × 10−3 h−1 in the oligotrophic lake water. Argon purging of samples did not prevent thiol degradation or oxidation after storage for 7 d. Pre‐concentration by freeze‐drying techniques led to significant loss for every thiol species tested (up to 65% for cysteine). We recommend improving storage methods by using temperatures of −80°C or 4°C. The use of correction factors to estimate initial thiol concentrations is possible but should be used with caution due to highly site‐specific kD in natural waters.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".