Variability in litter quality and its relationship to litter decay in Canadian forests
Bibliographic record
Abstract
Predicting the effects of climate change on litter decomposition requires an improved understanding of the link between organic composition and the parameters used to define litter quality. Carbon-13 nuclear magnetic resonance spectroscopy with cross-polarization and magic-angle spinning (CPMAS NMR) was used to characterize 36 foliar litters, including the species used in the Canadian Intersite Decomposition Experiment (CIDET), a Canada-wide long-term litter decomposition study. The litters showed a wide range of organic composition, qualitatively interpreted as the sum of component biopolymers (mainly carbohydrates, cutin, tannins, and lignin). Only weak correlations were found between NMR parameters and Klason lignin (KLIG); however, cluster analysis based on elemental, NMR, and proximate analysis gave good separation of botanical classes. NMR also had little predictive value for 3-year CIDET mass losses, which were negatively correlated with both KLIG and KLIG/N. Mass loss generally decreased in the fo...
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".