Cutin and suberin in mixed-wood boreal forest plants and their use as markers for origin of soil organic matter
Bibliographic record
Abstract
Quantifying above and belowground inputs to soil organic matter is important to assess forest soil health and to develop practices that increase the soil organic matter present in forest soils. Cutin and suberin are biopolymers found in leaves and roots, respectively, that protect plants from desiccation. Due to their specific locations within plant tissues, the presence of the polymers cutin and suberin in the soil is used to confirm the contributions of these leaves and roots to organic matter. Specifically, the identity of the monomers comprising these biopolymers is used to infer the contributions of leaves and roots to organic matter. However, previous studies have identified monomers in tissues of leaves or roots that do not concur with published lists of markers, highlighting the importance of using region- and species-specific markers. Cutin and suberin were extracted from leaf, root, and bark samples from the eight most dominant species in a boreal mixed-wood stand in Alberta using hydrolysis. Additionally, I sampled soils from the interface of the organic and mineral soil, treated with the same hydrolysis process to investigate if the monomers present in the plant tissue samples were also detectable in soils. The monomers were identified using GCMS and compared to published lists of cutin and suberin markers. Across the roots and leaves of the eight species, a total of 142 monomers were identified. In soil samples, only 48 monomers were observed, five of which were not present in any of the plant tissues. Due to their presence in other plant tissues aside from leaves and roots, and in microorganisms, several classes of compounds identified in this study cannot determine the origin of soil organic matter. When compared to published lists of cutin and suberin markers, I found that while a select number of markers held true for the samples analysed in this study, others are not appropriate for use in study areas with vegetation similar to the one in this study due to their presence in multiple tissue types. The inconsistencies between the monomers identified in this study and those in published reports highlight the importance of using cutin and suberin markers specific to the dominant species of plants present in the area of interest.
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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) | 0.000 | 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".