Estimation of Potential Impacts from Offshore Liquefied Natural Gas Terminals on Red Snapper and Red Drum Fisheries in the Gulf of Mexico: An Alternative Approach
Bibliographic record
Abstract
Abstract As of October 1, 2005, seven offshore liquefied natural gas (LNG) terminals have been proposed to be sited in offshore waters of the Gulf of Mexico. Six of these facilities have opted to use open‐rack vaporizers (ORVs) to heat and regasify the LNG and one plans to use a combination open‐closed‐loop system. Each of the terminals would require on the order of 100‐200 million gallons (380‐760 × 106 L) of seawater per day to vaporize the LNG. The potential impact on fishery stocks resulting from the entrainment of fish eggs and larvae has emerged as the foremost issue associated with the LNG terminals. The U.S. Coast Guard (USCG) and the Maritime Administration (MARAD) have used forward‐projecting equivalent adult models (EAMs) to evaluate the expected levels of impacts from entrainment and have concluded that the effects, while adverse, are minor. The results of these analyses have, however, predicted losses of a magnitude that have been interpreted by resource managers to represent significant reductions in important, overfished stocks of red drum Sciaenops ocellatus and red snapper Lutjanus campechanus. In this paper we show that the forward‐projecting EAMs are probably inappropriate and describe a fecundity hindcasting approach that is used in conjunction with the existing stock assessment models to estimate the impacts of entrainment on stocks and yields. The results of these analyses suggest that the effects on red drum yields are 387‐fold lower than the impacts on red drum yield estimated by the USCG and MARAD using the forward‐projecting EAM. For red snapper the impacts on yield using the fecundity hindcasting approach are 19‐fold lower than the effects estimated by the USCG and MARAD using the forward‐projecting approach. If our estimates are correct, the proposed LNG terminals will have minor adverse impacts on the subject stocks.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".