Vulnerability of Coral Reefs to Bioerosion From Land‐Based Sources of Pollution
Bibliographic record
Abstract
Abstract Ocean acidification (OA), the gradual decline in ocean pH and [ ] caused by rising levels of atmospheric CO 2 , poses a significant threat to coral reef ecosystems, depressing rates of calcium carbonate (CaCO 3 ) production, and enhancing rates of bioerosion and dissolution. As ocean pH and [ ] decline globally, there is increasing emphasis on managing local stressors that can exacerbate the vulnerability of coral reefs to the effects of OA. We show that sustained, nutrient rich, lower pH submarine groundwater discharging onto nearshore coral reefs off west Maui lowers the pH of seawater and exposes corals to nitrate concentrations 50 times higher than ambient. Rates of coral calcification are substantially decreased, and rates of bioerosion are orders of magnitude higher than those observed in coral cores collected in the Pacific under equivalent low pH conditions but living in oligotrophic waters. Heavier coral nitrogen isotope (δ 15 N) values pinpoint not only site‐specific eutrophication, but also a sewage nitrogen source enriched in 15 N. Our results show that eutrophication of reef seawater by land‐based sources of pollution can magnify the effects of OA through nutrient driven‐bioerosion. These conditions could contribute to the collapse of coastal coral reef ecosystems sooner than current projections predict based only on ocean acidification.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".