Effects of initial leaching for estimates of mass loss and microbial decomposition—Call for an increased nuance
Bibliographic record
Abstract
Decomposition is essential to carbon, nutrient, and energy cycling among and within ecosystems. Several methods have been proposed for studying litter decomposition by using a standardized and commercially available substrate. One of these methods is the Tea Bag Index (TBI) which uses tea bags (green and rooibos tea) incubated for ~90 days. The TBI is now applied all over the globe, but despite its usefulness and wide application, the TBI (as well as other methods) does not explicitly account for the differences in potential loss of litter mass due to initial leaching in habitats with large differences in moisture. We, therefore, studied the short-term mass losses (3-4 h) due to initial leaching under field and laboratory conditions for green and rooibos tea using the TBI and contextualized our findings using existing long-term mass loss (90 days) in the field for both aquatic and terrestrial environments. For both tea litter types, we found a fast initial leaching rate, which could be mistaken for decomposition through microbial activity. This initial leaching was higher than the hydrolyzable fraction given in the description of the TBI. We also found that leaching increased with increasing temperature and that leaching in terrestrial environments with high soil moisture (>90%) is almost as large as in aquatic environments. When comparing our findings to long-term studies, we found that up to 30-50% of the mass loss of green tea reported as decomposition could be lost through leaching alone in high moisture environments (>90% soil moisture and submerged). Not accounting for such differences in initial leaching across habitats may lead to a systematic overestimation of the microbial decomposition in wet habitats. Future studies of microbial decomposition should adjust their methods depending on the habitat, and clearly specify the type of decomposition that the study focuses on.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".