The importance of geomorphic context for estimating the carbon stock of salt marshes
Bibliographic record
Abstract
We measured total carbon stocks of three marshes: Two formed in association with a developing spit along the Gulf of St. Lawrence coast of New Brunswick, Canada, and another with a lagoon on the coast of Maine, USA. Overall, 46 cores and 157 depth recordings were collected to determine depth of the marsh deposits. Total marsh soil volume was estimated by interpolation. In all marshes soil depth varied in a predictable pattern based upon marsh developmental history. In spit marshes deposit age and thickness increased towards the oldest portion of the spit. In the lagoonal marsh, soil depth was greatest in the center and declined towards both the upland and seaward margins. This same pattern held on axes perpendicular to the primary, age axis of the spit marshes. In each marsh C density did not significantly vary with depth so that marsh depth was an acceptable estimator of C stock, and therefore driven by the geomorphic context of the marshes we studied. There were major differences in C stock estimates produced using GIS interpolation, average C contained in all marsh cores, or cores along a single transect. Our study demonstrates that assuming a soil depth of just 0.5 or 1 m can substantially under- or overestimate marsh carbon stocks and the value of that stock on a carbon market.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".