Pressure‐monitored headspace analysis combined with compound‐specific isotope analysis to measure isotope fractionation in gas‐producing reactions
Bibliographic record
Abstract
RATIONALE: Processes that lead to pressure changes in closed experimental systems can dramatically increase the total uncertainty in enrichment factors (ε) based on headspace analysis and compound-specific isotope analysis (CSIA). We report: (1) A new technique to determine ε values for non-isobaric processes, and (2) a general approach to evaluate the experimental error in calculated ε values. METHODS: ε values were determined by monitoring the change in headspace pressure from the production of CO2 in a decarboxylation reaction using a pressure gauge and measuring the δ(13) C values using CSIA. The statistical error was assessed over shorter reaction progress intervals to evaluate the impact of experimental error on the total uncertainty associated with calculated ε values. RESULTS: As an alternative to conventional compositional analysis, calculation of CO2 produced during the reaction monitored with a pressure gauge resulted in rate constants and ε values with improved correlation coefficients and confidence intervals for a non-isobaric process in a closed system. Further, statistical evaluation of the ε values as a function of reaction progress showed that uncertainty in data points for reaction progress (f) at late stages of the reaction can have a significant impact on the reported ε value. CONCLUSIONS: Pressure-monitored headspace analysis reduces the uncertainty associated with monitoring the reaction progress (f) based on estimating substrate removal and headspace dilution during sampling. Statistical calculations over shorter intervals should be used to evaluate the total error for reported ε values.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".