Soil carbon stock densities in mangrove and forested wetland ecosystems of Panama
Bibliographic record
Abstract
Mangrove and forested wetland ecosystems represent critical carbon reservoirs, yet uncertainties in belowground carbon stock estimates hinder their inclusion in climate mitigation strategies. Here we present soil carbon stock density data for mangrove and forested wetland ecosystems across Panama's Pacific and Caribbean regions. We established 45 permanent plots across marine and riparian mangrove typologies and 14 permanent plots in forested wetlands, collecting 544 soil cores to quantify soil carbon stocks. Soil samples were analyzed for bulk density, organic matter content, and organic carbon concentration, enabling calculation of carbon stock density at 0.3 m and 0.5 m depth profiles. Soil carbon stock density estimates differed among marine mangroves, riparian mangroves, and forested wetlands, reflecting ecosystem heterogeneity including mineral versus peat soils. These data provide essential ground-truth measurements contributing to Panama's national carbon accounting and climate commitments for the Land Use, Land-Use Change, and Forestry sector. The standardized methodology facilitates integration with regional carbon monitoring efforts across Central America and the Caribbean, supporting blue carbon database development and carbon mapping validation initiatives.
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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 | 0.001 |
| 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 | 0.000 | 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".