Potential Impact of the <i>Deepwater Horizon</i> Oil Spill on Commercial Fisheries in the Gulf of Mexico
Bibliographic record
Abstract
Abstract Given the economic and social importance of fisheries in the Gulf of Mexico large marine ecosystem (LME), it is imperative to quantify the potential impacts of the Deepwater Horizon oil spill. To provide a preliminary perspective of the consequences of this disaster, spatial databases of annual reported commercial catch and landed value prior to the spill were investigated relative to the location of the fisheries closures during July 2010. Recent trends illustrated by this study suggest that more than 20% of the average annual U.S. commercial catch in the Gulf has been affected by postspill fisheries closures, indicating a potential minimum loss in annual landed value of US$247 million. Lucrative shrimp, blue crab, menhaden, and oyster fisheries may be at greatest risk of economic losses. Overall, it is evident that the oil spill has impacted a highly productive area of crucial economic significance within the Gulf of Mexico LME. This study draws attention to the need for ongoing and thorough investigations into the economic impacts of the oil spill on Gulf fisheries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".