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Record W2622064087 · doi:10.1139/as-2016-0056

Some Thoughts on Estimating Change to Arctic Cod Populations from Hypothetical Oil Spills in the Eastern Alaska Beaufort Sea

2017· article· en· W2622064087 on OpenAlexvenueno aff
Benny J. Gallaway, Wolfgang J. Konkel, Brenda L. Norcross

Bibliographic record

VenueArctic Science · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsnot available
FundersNew York State Department of Environmental ConservationConocoPhillips
KeywordsArcticBeaufort seaEnvironmental scienceOil spillOceanographyPopulationFisheryEnvironmental protectionBiologyGeologyDemography

Abstract

fetched live from OpenAlex

We describe a fecundity-hindcast model that incorporates Arctic cod (Boreogadus saida) acute toxicity data, field studies of Arctic cod larval distribution and abundance, natural mortality estimates for Arctic cod eggs and larvae, and an oil spill fate model in Alaska Beaufort Sea. Three orders of magnitude of spill events (1000, 10 000, and 100 000 tons) were evaluated for both physically and chemically dispersed oil. Using worst-case assumptions in our model, a 100 000 ton spill of crude oil treated with dispersants resulted in 266 million m3 of water that exceeded our acute toxicity threshold, compared to a volume of 71 million m3 for a 100 000 ton spill not treated with dispersants, and resulted in exposure of about 2 million Arctic cod larvae remaining from an initial 87 million eggs. This represents the reproductive output of about 7300 adult females. Adult Arctic cod populations in the Alaska Beaufort number in the tens to hundreds of millions. The results show that even with an order of magnitude variation in exposure, the effect of dispersing a large oil spill on the regional cod population is expected to be insignificant (∼0.7%). The recent hiatus in Arctic oil and gas development affords an opportunity to acquire additional data to further strengthen this conclusion.

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 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)0.001
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 score0.096
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.070
GPT teacher head0.312
Teacher spread0.242 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2017
Admission routes1
Has abstractyes

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