Soil carbon may be maintained under grazing in a St Lawrence Estuary tidal marsh
Bibliographic record
Abstract
SUMMARY Production of belowground organic matter is critical to sustainability of salt marshes. It plays a role in vertical soil accretion, a process essential for salt marshes to maintain their relative elevation and persist as sea levels rise. This paper examines belowground production and soil carbon of a high-latitude saltmarsh in the St Lawrence Estuary (Québec, Canada), which had been subjected to six years of sheep grazing. In the seventh year, without sheep, organic matter production in grazed and ungrazed sections was assessed by examining harvests of plant litter, end-of-season standing crop, and the roots and rhizomes present in in-growth cores. Excepting salinity, porewater chemistry varied little. The grazed marsh had higher soil carbon density and belowground production, yet lower aboveground biomass. Grazing reduces plant litter and increases solar exposure, soil temperature (at this latitude, soil remained frozen until April) and evapotranspiration, thus raising soil salinity and nitrogen demand, the latter a driver of root production. Grazing may not be detrimental to soil carbon storage. Permitting certain types of grazing on restored salt marshes previously drained for agriculture would provide economic incentive to restore tidal flooding, because the natural carbon sink provided in the recovered marsh would make these lands eligible for carbon payments.
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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 | 0.000 | 0.000 |
| 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".