Prediction of in situ root decomposition rates in an interspecific context from chemical and morphological traits
Bibliographic record
Abstract
BACKGROUND AND AIMS: We quantitatively relate in situ root decomposition rates of a wide range of trees and herbs used in agroforestry to root chemical and morphological traits in order to better describe carbon fluxes from roots to the soil carbon pool across a diverse group of plant species. METHODS: In situ root decomposition rates were measured over an entire year by an intact core method on ten tree and seven herb species typical of agroforestry systems and were quantified using decay constants (k values) from Olson's single exponential model. Decay constants were related to root chemical (total carbon, nitrogen, soluble carbon, cellulose, hemicellulose, lignin) and morphological (specific root length, specific root length) traits. Traits were measured for both absorbing and non-absorbing roots. KEY RESULTS: From 61 to 77 % of the variation in the different root traits and 63 % of that in root decomposition rates was interspecific. N was positively correlated, but total carbon and lignin were negatively correlated with k values. Initial root traits accounted for 75 % of the variation in interspecific decomposition rates using partial least squares regressions; partial slopes attributed to each trait were consistent with functional ecology expectations. CONCLUSIONS: Easily measured initial root traits can be used to predict rates of root decomposition in soils in an interspecific context.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".