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Record W6907702132 · doi:10.25316/ir-382

Estimating pipeline oil spill volumes using environmental site assessment information

2018· other· en· W6907702132 on OpenAlexfundno aff

Bibliographic record

VenueVIURRSpace (Vancouver Island University) · 2018
Typeother
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsnot available
FundersAlberta Environment and ParksNew Jersey Department of Environmental Protection
KeywordsOil spillEnvironmental remediationPetroleumPipeline (software)Volume (thermodynamics)Environmental impact assessmentPipeline transport

Abstract

fetched live from OpenAlex

When a reportable oil spill occurs, stakeholders expect the responsible party to issue a release volume estimate as part of the mitigation accountability. One method of estimating a spill volume is to use environmental assessment data to enumerate the total volume of petroleum hydrocarbons detected in the environment. This method was applied to two terrestrial based pipeline spill case studies. The assessment data was used to create a conceptual site model which, along with the liquid petroleum hydrocarbon density and soil bulk density, was synthesized to estimate the spill volumes. The case study data was collected at the time of each release for assessment, delineation, and remediation purposes. Using the data to estimate spill volumes was ancillary and complementary to its original purpose. The practical implication is that environmental assessment and remediation practitioners can readily adopt and implement this method at terrestrial based spills. Keywords: oil spill, mass balance, environmental remediation, pipeline release, spill volume estimate.

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)0.000
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.036
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.002

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 head0.005
GPT teacher head0.197
Teacher spread0.192 · 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
GenreOther

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

Citations0
Published2018
Admission routes1
Has abstractyes

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