Blue carbon cycling in the coastal areas of Qatar
Bibliographic record
Abstract
Abstract Coastal wetland sediments are vital to the global carbon cycle as they represent large sinks of blue carbon – carbon from atmospheric and oceanic sources – which are threatened by ecosystem loss. The forms of sequestered carbon and the sequestration capability are affected by many bio- and geochemical factors that change unpredictably along coastal locales. In the present study, we investigated three unique coastal sites – a coastal mangrove and two sabkhas with contrasting geology and tidal influence in the Qatar peninsula – for their carbon capture ability to determine how biogeochemical indices affect their blue carbon sequestration potential. We applied a suite of biological and geochemical tools, collecting the sediment cores of approximately 40 cm depth; analysed sediment porewater; performed depth-profiling of the organic matter, sedimentary minerals, microbial community and analysis of sediment surface for pH, oxygen (O 2 ); redox potential and hydrogen sulfide (H 2 S) by microsensors. High-resolution transmission electron microscopy with energy-dispersive X-ray spectroscopy (TEM-EDXS) and scanning transmission X-ray microscopy (STXM) revealed templating effects that promoted Mg-carbonate nucleation in coastal hypersaline environments. Microsensing reveals the intricacy of the oxic/anoxic transition at the sediment surface. Microbial DNA sequencing at various sediment depths shows the occurrence of microbial genera, whose functions explain the geochemical trends and carbon sequestration pathways observed at each site. Notably, we found that carbon sequestration in the mangrove and carbonate-sand sabkha was correlated with organic matter degradation and inorganic carbon content, while in the siliciclastic sabkha it was solely influenced by sediment density and depth.
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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 | 0.001 | 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".