Accelerated marsh erosion following the Deepwater Horizon oil spill confirmed, ameliorated by planting
Bibliographic record
Abstract
Multiple studies have examined the effects of the Deepwater Horizon oil spill on coastal marsh shoreline erosion. Most studies have concluded that the spill increased shoreline erosion (linear retreat) in oiled marshes by ~ 100-200% for at least 2-3 years. However, two studies have called much of this prior research into question, due to potential study design flaws and confounding factors, primarily tropical cyclone influences and differential wave exposure between oiled (impact) and unoiled (reference) sites. Here we confirm that marsh erosion in our field experiment was substantially increased (112-233%) for 2 years in heavily oiled marsh after the spill, likely due to vegetation impacts and reduced soil shear strength attributed to the spill, rather than the influences of hurricanes or wave exposure variation. We discuss how our findings reinforce prior studies, including a wider-scale remote sensing analysis with similar study approach. We also show differences in the degree of erosion among oil spill cleanup treatments. Most importantly, we show that marsh restoration planting can drastically reduce oiled marsh erosion, and that the positive influences of planting can extend beyond the immediate impact of the spill.
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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 | 0.001 |
| 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".