Using the Tea Bag Index to characterize decomposition rates in restored peatlands
Bibliographic record
Abstract
Peatlands characteristically accumulate organic matter due to low decomposition rates, but peatland disturbance alters local physicochemical conditions often resulting in loss of soil organic matter and emission of CO2. Restoration may reduce peat oxidation, but traditional measurements of decomposition are time-consuming. The Tea Bag Index (TBI) is a simple, standardized method to measure decomposition rates in soils. We used the TBI to measure decomposition rate at four restored peatland sites across Canada that were used for peat extraction or disturbed by oil extraction (former well-sites), comparing to undisturbed and unrestored sites. We measured environmental conditions including soil temperature, water table position and peat pH from May to August 2016. Litter bags were buried for one year alongside tea bags at one site for a direct comparison of decomposition rates between the methods. There were no significant differences for TBI decay constant (kTBI) between treatments of restored, unrestored or undisturbed sites across the whole data set, but some differences were found among treatments within the same peatland site for sections restored at different times in the past. Soil temperature, pH, and water table were not significantly related to kTBI, but were negatively correlated with the stabilization factor (S). The kTBI and litter bag k were significantly different but positively correlated. The TBI is not easily comparable to traditional litter bags, but is less costly in both time and money, and may be used in conjunction with additional parameters to determine decomposition patterns with potential for use as a metric for evaluating restoration outcomes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".