Variation in Plant Litter Decomposition Rates across Extreme Dry Environments in Qatar
Bibliographic record
Abstract
Decomposition of plant litter is a key process for transfer of carbon and nutrients in ecosystems. Carbon contained in decaying biomass is released to the atmosphere as respired CO 2 , a greenhouse gas that contributes to global warming. To our knowledge, there have been no studies on litter decomposition in terrestrial ecosystems in the Arabian peninsula. Here we used commercial teabags (green tea, rooibos tea) as standard substrates to study decomposition rates across contrasting ecosystems in Qatar. Teabags were buried under and beside Acacia tortilis trees, in depressions with abundant grass vegetation, in saltmarsh without and with vegetation, under Zygophyllum qatarense in drylands, in natural mangrove and in planted mangrove. There were significant site effects across ecosystems on decomposition rate (k), litter stabilisation factor (S), final weight of green tea and final weight of rooibos tea. Mangrove and depressions with grassland had the smallest amounts of remaining green and rooibos tea after the incubation period (69–82 days), while teabags buried under A. tortilis and in saltmarsh without vegetation had the largest amounts. Thus decomposition rates differ among ecosystems in the desert environment. Further multi-year and site studies are needed to identify factors that influence decomposition rates across sites in extreme environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".