Sustainability impacts of sediments on the estuary, ports, and fishing communities of Cartagena Bay, Colombian Caribbean
Bibliographic record
Abstract
Abstract This article reviews research on sediment flux impacts on the receiving estuary, ports, and society in Cartagena, Colombia. The city hosts both the country's largest touristic and coastal industrial sectors and is home to vulnerable coastal communities whose health and livelihoods are impacted by pollution. These marginalized artisanal fishing communities lack basic water and health services but have finally been recognized in a new intersectoral committee for Cartagena Bay's environmental management. To support the governance of these complex socioenvironmental challenges, the Cartagena Bay Observatory has been developed as a scientific tool to monitor the bay's conditions and forecast the effects of future sediment remediation plans. Cartagena Bay receives large freshwater discharges from the Dique Canal, draining from the 260,000 km 2 Magdalena River watershed where 80% of the national population resides. This runoff transports sediment loads of 2.3 Mt/year, dispersing large plumes that affect the marine ecosystems and tourism and make the bay one of the Caribbean's largest sediment‐receiving estuaries. Following decades of watershed deforestation and erosion, the upward trending sediment inputs and accretion of the Dique delta have resulted in deposition rates of 1.8 cm/year, and the need for frequent dredging. Mercury dumped by a chemical industry in the 1970s can be found in concentrations as high as 18.8 μg/g buried below the bay's bottom. Mercury has also been found in the bay's biota and human populations, and so the dredging needed for the port's sustainability thereby presents a health risk by allowing this trapped mercury to surface. This article is categorized under: Science of Water > Hydrological Processes Science of Water > Water Quality Science of Water > Water and Environmental Change
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".