Blue carbon stock in Tunisian coastal sediments: First assessment and implications for ecosystem conservation and climate change mitigation
Bibliographic record
Abstract
This work presents the first comprehensive study on carbon definitive sequestration in coastal marine sediments in Tunisia. It study aimed to provide insights into the potential role of coastal Tunisian sediments in mitigating carbon dioxide (CO 2 ) emissions and climate change. It compiles carbon data from 32 cores sampled in eight distinct ecosystems along the Tunisian coast, including lagoons (Ghar El Melh, Bizerte and Korba), Lake Ichkeul, seagrass meadows ( Posidonia oceanica ) in Sidi Rais and Monastir bays along with two gulfs (Tunis and Gabes). The study revealed carbon sequestration rates ranging from 2.1 to 177.6 gC m −2 yr −1 , with the highest rates found in P. oceanica meadows while both lagoons seemed recalcitrant to sequestration due to enhanced organic matter degradation. Conversely, Lake Ichkeul, constitutes a good trap with carbon sequestration rate up to 49 gC m −2 yr −1 and remarkably Tunis and Gabes gulfs exhibit exceptionally high levels exceeding 40 %. Although P. oceanica meadows exhibited the highest sedimentation rates (414 and 115 gC m −2 yr −1 , respectively), their carbon sequestration was low (40 %). The carbon permanently sequestered in the sediment of the studied environments was around 1243 KtCO 2 yr −1 , accounting for approximately 4 % of the total CO 2 emissions by Tunisia in 2021. However, considering the short and medium-term sequestration potentials of the 1,33,2815 ha of seagrass meadows in Tunisia, this percentage could attain >100 %.
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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 | 0.001 |
| 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".