Influence of initial chemistry on decomposition of foliar litter in contrasting forest types in British
Bibliographic record
Abstract
Abstract: We compare rates of decay of foliar litters of British Columbia tree species in two field studies, and assess which initial litter chemistry parameters best predict the decay rates. Nutrient concentrations, tannins, and carbon frac-tions (based on proximate analysis and nuclear magnetic resonance spectroscopy) were measured in fresh litter of 14 tree species in one experiment and seven species in a second experiment. Each study was replicated in a different site in order to assess the transferability of results. Broadleaf litters decayed faster than needle litters only during the first year; thereafter, they decayed slower. Lignin concentration was a good predictor of mass loss only during the first year and only in one of the two experiments, which may have resulted from all foliar litters having high lignin concen-trations (>170 mg·kg–1). Litter chemistry effects on first-year decay were consistent and transferable among sites. None of the initial litter chemistry parameters were good predictors of mass remaining after 4 or 5 years, because mass loss of most litters was similar by this time. The convergence in mass losses of litters after 4–5 years despite initial differ-ences indicates that decomposition estimates extrapolated from early rates or initial chemistry may not accurately pre-dict long-term decay. Résumé: Nous avons comparé le taux de décomposition de la litière de feuilles d’espèces d’arbres de la Colombie-Britannique dans le cadre de deux études sur le terrain et nous avons évalué quels paramètres chimiques initiaux de la litière permettaient le mieux de prédire le taux de décomposition de la litière. La concentration des nutriments ainsi
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".