An evaluation of the success of dredging as remediation at a DDT-contaminated site in San Francisco Bay, California, USA
Bibliographic record
Abstract
Lauritzen Canal, a portion of San Francisco Bay near Richmond, California, USA, was heavily contaminated with dichlorodiphenyltrichloroethane (DDT) and dieldrin as a result of releases from a pesticide-formulating firm. In 1996 and 1997, 82,000 m3 of contaminated sediment was removed from the canal by dredging. This study evaluated the success of the dredging based largely on body burdens of DDT and its metabolites (sigmaDDT) in resident biota, with some data on sediment- and water-contaminant levels and sediment toxicity testing. Sediment disturbance during dredging introduced a pulse of sigmaDDT into the Lauritzen Canal ecosystem, and body burdens of fish and invertebrates increased 2- to 76-fold, depending on the species. Approximately 1 1/2 years after remediation, 11 of 14 indicators showed contamination comparable with or worse than the contamination that existed prior to dredging. Monitoring of mussels up to four years postdredging suggests some modest improvement, although the sigmaDDT body burden of canal mussels remained far above the norm for San Francisco Bay. The elevated sigmaDDT body burdens in biota that persisted for years after remediation reflect recent exposure and are not merely a result of slow metabolic elimination of the sigmaDDT pulse associated with dredging. Sediment sigmaDDT concentrations were low immediately after dredging, but within months, the canal bottom became covered with a veneer of fine sediment as contaminated as that that had been removed. The source of this material has not been conclusively established, but we suspect it came from slumping and erosion from the flanks of the canal beneath docks and around pilings where dredging was not done. In retrospect, either capping in place or more thorough dredging may have been more successful in reducing pesticide exposure of the biota, although there were difficulties associated with both alternatives.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".