Seagrass community-level controls over organic carbon storage are constrained by geophysical attributes within meadows of Zanzibar, Tanzania
Bibliographic record
Abstract
Abstract. The aim of this work was to explore the feasibility of using seagrass functional traits to predict differences in sediment carbon storage. At 19 sites within highly diverse seagrass meadows of Zanzibar, Tanzania, species cover was estimated along with three community traits hypothesized to influence sediment carbon storage (amount of above and belowground biomass, seagrass tissue nitrogen content, and shoot density). We identified five distinct seagrass communities that had notable variations in key plant traits but these differences did not translate into differences is sediment organic carbon (OC) storage. Across all communities, sediment OC was very low (ranging from 0.15 % to 0.75 %) and there were no differences in OC storage among communities, which was considerably lower (33.97.7 Mg C ha−1) than the global average (194.220.2 Mg C ha−1) reported for other seagrass ecosystems. In spite of high seagrass diversity and clear zonation among plant communities, sediments in all communities were shallow (ranging from 19 to 78 cm) and composed of medium-coarse grained carbonate sand on top of carbonate rock. We propose that geophysical conditions of the sediment were not conducive to OC stabilization, and outweighed any variation in the quantity or quality of plant litter inputs, ultimately leading to low OC storage within all seagrass communities. This highlights the complexity of OC cycling in seagrass ecosystems and cautions against the use of plant traits as a proxy for OC storage across all seagrass ecosystems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".