Tea Bag Index to Assess Carbon Decomposition Rate in Cranberry Agroecosystems
Bibliographic record
Abstract
In cranberry production systems, stands are covered by 1–5 cm of sand every 2–5 years to stimulate plant growth, resulting in alternate layers of sand and litter in soil upper layers. However, almost intact twigs and leaves remain in subsurface layers, indicating a slow decomposition rate. The Tea Bag Index (TBI) provides an internationally standardized methodology to compare litter decomposition rates (k) and stabilization (S) among terrestrial ecosystems. However, TBI parameters may be altered by time-dependent changes in the contact between litter and their immediate environment. The aims of this study were to determine the TBI of cranberry agroecosystems and compare it to the TBI of other terrestrial ecosystems. Litters were standardized green tea, standardized rooibos tea, and cranberry residues collected on the plantation floor. Litter decomposition was monitored during two consecutive years. Added N did not affect TBI parameters (k and S) due to possible N leaching and strong acidic soil condition. Decomposition rates (k) averaged (mean ± SD) 9.7 × 10−3 day−1 ± 1.6 × 10−3 for green tea, 3.3 × 10−3 day−1 ± 0.8 × 10−5 for rooibos tea, and 0.4 × 10−3 day−1 ± 0.86 × 10−3 for cranberry residues due to large differences in biochemical composition and tissue structure. The TBI decomposition rate (k) was 0.006 day−1 ± 0.002 in the low range among terrestrial ecosystems, and the stabilization factor (S) was 0.28 ± 0.08, indicating high potential for carbon accumulation in cranberry agroecosystems. Decomposition rates of tea litters were reduced by fractal coefficients of 0.6 for green tea and 0.4 for rooibos tea, indicating protection mechanisms building up with time in the tea bags. While the computation of the TBI stabilization factor may be biased because the green tea was not fully decomposed, fractal kinetics could be used as additional index to compare agroecosystems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".