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Record W3139528389 · doi:10.1007/s10646-021-02359-9

Review of petroleum toxicity in marine reptiles

2021· review· en· W3139528389 on OpenAlexafffund
Elizabeth J. Ruberg, Tony D. Williams, John E. Elliott

Bibliographic record

VenueEcotoxicology · 2021
Typereview
Languageen
FieldEnvironmental Science
TopicEnvironmental Toxicology and Ecotoxicology
Canadian institutionsEnvironment and Climate Change CanadaSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaEnvironment and Climate Change Canada
KeywordsSpillagePetroleumFaunaBiologyEcology

Abstract

fetched live from OpenAlex

Worldwide petroleum exploration and transportation continue to impact the health of the marine environment through both catastrophic and chronic spillage. Of the impacted fauna, marine reptiles are often overlooked. While marine reptiles are sensitive to xenobiotics, there is a paucity of petroleum toxicity data for these specialized fauna in peer reviewed literature. Here we review the known impacts of petroleum spillage to marine reptiles, specifically to marine turtles and iguanas with an emphasis on physiology and fitness related toxicological effects. Secondly, we recommend standardized toxicity testing on surrogate species to elucidate the mechanisms by which petroleum related mortalities occur in the field following catastrophic spillage and to better link physiological and fitness related endpoints. Finally, we propose that marine reptiles could serve as sentinel species for marine ecosystem monitoring in the case of petroleum spillage. Comprehensive petroleum toxicity data on marine reptiles is needed in order to serve as a foundation for future research with newer, unconventional crude oils of unknown toxicity such as diluted bitumen.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.922
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0760.001

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 head0.026
GPT teacher head0.305
Teacher spread0.279 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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

Citations20
Published2021
Admission routes2
Has abstractyes

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