Disturbance effects on soil water-extractable organic matter as seen with double-shot pyrolysis-gas chromatography/mass spectrometry
Bibliographic record
Abstract
• Vegetation and fire impacted water-extractable organic matter (WEOM) composition • WEOM composition differed between forest floors and mineral soils • The pyrolysis stage of the GC/MS analyses best-detected vegetation and fire impacts • The thermal desorption phase was best for forest floors and mineral soils Land disturbances are a significant concern for watershed managers as they can affect the quantity and quality of organic compounds entering surrounding waterways. Yet little is known about how wildfire and harvesting practices may impact the composition of dissolved organic matter in mountain forest soils. In this study, the water extractable organic matter (WEOM) components from forest floors and the underlying surficial mineral soils (0-10 cm) from four watersheds (a recently burnt forest, two undisturbed reference forests and a recently clear-cut harvested forest) were characterized using several double-shot gas chromatography-mass spectrometry (GC/MS) analytical techniques, including thermal desorption (TD) and pyrolysis (Py), with or without tetramethylammonium hydroxide (TMAH). Ordinations combined with indicator-specific analyses were an efficient statistical approach for the multivariate data generated from double-shot pyrolysis. Wildfire had a major impact on WEOM chemical composition, and several black carbon biomarkers were identified. Differences in WEOM composition were also detected between forest floors and mineral soils, as well as in response to the dominant canopy vegetation. On the other hand, harvesting effects were minimal, and WEOM from harvested soils showed a strong legacy effect from the prior canopy vegetation. Lastly, our results showed that a specific study focus needs to be considered when choosing the best GC/MS method. Differences between forest floors and mineral soils were best detected during the thermal desorption phase with TMAH addition. In contrast, differences among vegetation types were best identified during the pyrolysis phase with no TMAH.
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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 | 0.000 | 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) | 0.000 | 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".