Combining sediment analysis with geospatial mapping to quantify carbon sequestration by an eelgrass bed on the Nova Scotian coast, eastern Canada. 
Bibliographic record
Abstract
In recent years, seagrass has been presented as a solution to sequester excess carbon emissions from the atmosphere, with studies reporting that seagrass meadows are responsible for burying as much as 10% of anthropogenic carbon per year (Fourqurean et al. 2012). However, this estimate has started to seem improbable as more recent research, specifically from North American study sites, are reporting carbon stock estimates much lower than the global average. Here, we present estimates of organic carbon (OC) stock in an eelgrass meadow on the Eastern Shore of Nova Scotia, Canada. To quantify sediment OC stock, we combined sediment geochemical analysis with geospatial mapping based on high-resolution optical aerial imagery collected by drone flights. Three sediment cores, plus a control, were extracted from the meadow in regions with differing levels of vegetative cover. The control core was used to establish a background signal for sediment OC, which we assume to be representative of nearshore unvegetated sediments in the region.Despite the health and anecdotally reported longevity of this eelgrass meadow measuring 4.7 Ha in size, the carbon stock is estimated to be less than 10 Mg OC/Ha. This is significantly lower than the global average estimates of ~163.3-660 Mg/Ha (Fourqurean et al. 2012) but is comparable to other reports emerging from the North American east coast (eg. 3.7 Mg/Ha from coastal Virginia, US: Greiner et al. 2013). From the individual core slices, the maximum sediment OC did not exceed 2.5 weight % even in the densest, healthiest part of the meadow. There was also a notable correlation between presence of coarse biomass and higher sediment OC in the bulk sample, suggesting that the carbon is mostly associated with living biomass rather than being buried and stored in sediments. Further, radiocarbon ages of the bulk OC of up to 1140 years in the topmost sediment layer imply a significant admixture of pre-aged, likely terrestrial, OC to the bulk OC, rendering the stock estimates absolute maximum estimates. Overall, this study adds to the growing body of evidence that suggests that global estimates of OC storage in eelgrass beds need to be carefully reevaluated.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 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".