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Record W4313577373 · doi:10.1016/j.marpol.2022.105466

Oil spill response planning in Pacific Canada: A tool for identifying vulnerable marine biota

2023· article· en· W4313577373 on OpenAlexaffabout
Sharon Jeffery, Lucie Hannah, Leif‐Matthias Herborg, Candice St. Germain

Bibliographic record

VenueMarine Policy · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsVulnerability (computing)Oil spillBiotaEmergency responseHarmEnvironmental resource managementVulnerability assessmentEnvironmental scienceEnvironmental planningFisheryGeographyEnvironmental protectionEcologyComputer scienceBiologyComputer securityMedical emergency

Abstract

fetched live from OpenAlex

Oil spill response planning is crucial for a fast and effective response. During an oil spill, focused biological resources at risk information is required to inform and guide oil spill response. A significant challenge for the initial emergency response phase (0–24 h) is having a consistent and transparent way to quickly identify the marine species that are most vulnerable to the oil spill, while not being reliant on regional experts who may not be available at the time. To address this challenge, biologists at Fisheries and Oceans Canada developed an oil vulnerability framework that has been used to assess vulnerability to oil for species groups using a suite of criteria. In the framework, vulnerability is defined as the degree to which a species group is susceptible to, and unable to cope with, injury, damage, or harm, and is a function of exposure to oil; sensitivity to oil, and recovery potential. Species groups were scored against criteria under these categories to generate a total vulnerability score that has been used to rank the species groups from most to least vulnerable. The framework has been adapted for use in different regions of Canada, but here we describe an application for the Pacific region of Canada, and how environmental response experts are using the results for oil spill planning and response.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.467
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.035
GPT teacher head0.308
Teacher spread0.273 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

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