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Record W2024468670 · doi:10.1139/f2011-171

Impact of the <i>Deepwater Horizon</i> well blowout on the economics of US Gulf fisheries

2012· article· en· W2024468670 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsUniversity of British Columbia
FundersUniversity of British ColumbiaPew Charitable Trusts
KeywordsDeepwater horizonFisheryFishingRevenueShoreEconomic impact analysisWellheadEnvironmental scienceRecreational fishingOil spillBusinessEnvironmental protectionEconomicsEngineeringBiologyFinance

Abstract

fetched live from OpenAlex

Marine oil spills usually harm organisms at two interfaces: near the water surface and on shore. However, because of the depth of the April 2010 Deepwater Horizon well blowout, deeper parts of the Gulf of Mexico are likely impacted. We estimate the potential negative economic effects of this blowout and oil spill on commercial and recreational fishing, as well as mariculture (marine aquaculture) in the US Gulf area, by computing potential losses throughout the fish value chain. We find that the spill could, in the next 7 years, result in (midpoint) present value losses of total revenues, total profits, wages, and economic impact of US$3.7, US$1.9, US$1.2, and US$8.7 billion, respectively. Commercial and recreational fisheries would likely suffer the most losses, with a respective estimated US$1.6 and US$1.9 billion of total revenue losses, US$0.8 and US$1.1 billion in total profit losses, and US$4.9 and US$3.5 billion of total economic losses.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.013
GPT teacher head0.196
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it