The ‘Massana soil system’ project: untargeted metabolomics to unravel chemical landscapes in Massane forest soils
Bibliographic record
Abstract
Advancements in soil analytical techniques offer new insights into ecosystem structure, functions, and dynamics. Metabolomics has the potential to serve as a unique and efficient way to characterize the soil "chemical landscape." Thirty-three soil samples were collected from the Massane old-growth forest reserve, a UNESCO world heritage site located in the Eastern piedmont of the French Pyrenees. The study sites aimed to investigate three types of forest stands, namely beech forests, beech/oak stands, and mixed forest stands, subsets being also defined within stand types based on forest facies. We hypothesized that soil chemical heterogeneity would reflect forest spatial heterogeneity. Non-targeted metabolomics using liquid chromatography coupled with high-resolution tandem mass spectrometry (UHPLC-MS/MS) and molecular network analyses were employed to map the chemical diversity across the sampled sites. This approach unveiled the presence of various compounds, including lipids (fatty acids and their derivatives, sphingolipids, prenol lipids, steroids), terpenoids (triterpenoids and sesquiterpenes), coumarins, and oligopeptides and lipopeptides. Along with the presence of a rich core metabolome, some heterogeneity was also underscored, suggesting unique chemical structures associated with specific types of sampling sites, notably mature beech stands. These results call for a deeper investigation into these specific compounds, with regards to their biological origin and the diversity and heterogeneity of soil microbial communities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".