Isotopic composition of H<sub>2</sub> from wood burning: Dependency on combustion efficiency, moisture content, and δD of local precipitation
Bibliographic record
Abstract
Differences in isotopic composition between the various sources of H-2 are large, but only a few measurements have been carried out to constrain them. Two conflicting values have been published for H-2 from biomass burning. Both rely on the assumption that the isotopic composition of H-2 should scale with the isotopic composition of the precipitation at the location where the biomass grew. Here we test this hypothesis using 18 wood samples collected from various locations around the globe. The sample locations cover a range of delta D content of H-2 in precipitation, from below -120 parts per thousand in Siberia and Canada to -15 parts per thousand in Zimbabwe. The results confirm the predicted dependence of the H-2 isotopic composition on the precipitation in the sampling region. The water content itself is found to at most slightly affect the results. Furthermore, delta D of H-2 depends strongly on combustion efficiency. Thus, the isotopic composition of H-2 from biomass burning shows a strong variability around the globe and between different stages of a fire. It is suggested that, rather than a global bulk number, global models that attempt to reproduce the spatial and temporal distribution of delta D in H-2 should incorporate explicitly the variability of delta D(H-2) from biomass burning on delta D in precipitation.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".