Intensive oyster farming enhances carbon storage in sediments over decades
Bibliographic record
Abstract
Clarifying the oyster’s carbon budget of their farming ecosystem defines this industry’s future. Through biodeposition, oyster enhances the vertical flux of organic carbon. However, the cycling of sedimented carbon before being separated from the biosphere remains unclear. Here, we constructed the chronologic profiles of the sediment cores from a typical oyster farm with approximately 50 years of farming history. The profiles corresponded with the farming development, environment, associated biogenic elements, and microbial communities. Our results showed the organic carbon burial flux after the onset of intensive farming increased 2.6-fold, reaching 106 g C·m−2·yr−1. Farming development drove the accumulation of microbial necromass, while the percentage of recalcitrant organic carbon in sediment organic carbon also increased from 42.52% to 60.19%. This study highlights the enhancement of carbon storage in response to the development of oyster farming, contributing to the understanding of the ecosystem-based carbon budget for oyster farming. Oyster farming can enhance organic carbon storage in sediments by increasing sediment organic carbon concentration up to 2.6 times, according to an analysis of 50 years of farming data from a typical oyster farm in Sanggou Bay, China.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".