Bivalve-enhanced nitrogen removal from coastal estuaries
Bibliographic record
Abstract
Interest in use of bivalves to remediate estuarine eutrophication has increased in recent years. High variation among data sets, substantial focus on particle removal, and insufficient links to anthropogenic nitrogen (N) sources encouraged this empirical examination of N removal by bivalves from estuaries receiving different N loads. We determined the capacity of the oyster Crassostrea virginica to remove N by comparing N assimilated into tissues with anthropogenic N from land or available in phytoplankton. Oyster growth yielded 0.2–0.4 g N in tissues and depended on estuary-specific conditions. δ 15 N values confirmed that N in oyster tissues derived from local anthropogenic sources. At representative restoration and aquaculture conditions (≤400 oysters·m –2 at 0.5%–1.0% of estuary area), estimated N removal was ≤15% of land-derived loads and <1% of phytoplankton N. N removal via biogeochemical processes was negligible during grow-out, but became important after oysters attained harvestable size. This study explicitly demonstrates that oysters assimilated land-derived N, but suggests that bivalve bioremediation consider trade-offs between intensity of planting, ecological effects, and available space.
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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 | 0.000 | 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) | 0.000 | 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 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".