Do coastal salt mudflats (sabkhas) contribute to the blue carbon sequestration?
Bibliographic record
Abstract
Abstract Blue carbon represents the organic carbon retained in marine coastal ecosystems. Sabkhas (an Arabic for “mudflats”), formed in tidal environments under arid conditions, have been proposed to be capable of carbon sequestrating. Despite the growing understanding of the critical role of blue carbon ecosystems, there is a current dispute about whether sabkhas around the Persian Gulf can contribute to carbon retention as a blue carbon ecosystem. The arguments often lack data on a critical contributor, inorganic carbon in the form of carbonates, which can drive the net carbon exchange with the atmosphere. In this study we inventory organic and inorganic carbon retention capacity in two contrasting sabkhas of the Qatar Peninsula: carbonaceous Dohat Faishakh and siliciclastic Khor Al-Adaid. Despite the differences in organic carbon stock between the two sabkhas, the Dohat Faishakh sabkha has higher (37.17 ± 0.81 Mg C org ha −1 ) than it is in the Khor Al-Adaid sabkha (13.75 ± 0.38 Mg C org ha −1 ) for 0. 44 m sediment depth, the organic carbon retained in sabkhas is similar to those reported for mangroves and salt marshes. Notably, calculated CO 2 net sequestration indicated that both sabkhas evade CO 2 into the atmosphere. Thus, carbonate formation negated organic carbon accumulation in carbonaceous sabkha. Consequently, for proper evaluation of sabkhas as a blue carbon ecosystem, an inorganic carbon analysis, especially of carbonate formation, is inevitable. Considering only organic carbon stock may ay overestimate carbon sequestration capacity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".