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Record W2792141141 · doi:10.1002/2017gl076606

Was the Deepwater Horizon Well Discharge Churn Flow? Implications on the Estimation of the Oil Discharge and Droplet Size Distribution

2018· article· en· W2792141141 on OpenAlexaff
Michel C. Boufadel, Feng Gao, Lin Zhao, Tamay M. Özgökmen, Richard S. Miller, Thomas King, Brian Robinson, Kenneth Lee, Ira Leifer

Bibliographic record

VenueGeophysical Research Letters · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
FundersGulf of Mexico Research Initiative
KeywordsWellheadFlow (mathematics)PlumePetroleum engineeringMechanicsEnvironmental scienceFluid dynamicsGeologyMeteorologyPhysics

Abstract

fetched live from OpenAlex

Abstract Improved understanding of the character of an uncontrolled pipeline flow is critical for the estimation of the oil discharge and droplet size distribution both essential for evaluating oil spill impact. Measured oil and gas properties at the wellhead of the Macondo255 and detailed numerical modeling suggested that the flow within the pipe could have been “churn,” whereby oil and gas tumble violently within the pipe and is different from the bubbly flow commonly assumed for that release. The churn flow would have produced 5 times the energy loss in the pipe compared to bubbly flow, and its plume would have entrained 35% more water than that of the bubbly flow. Both findings suggest that the oil discharge in Deepwater Horizon could have been overestimated, by up to 200%. The resulting oil droplet size distribution of churn flow is likely smaller than that of bubbly flow.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.271
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations35
Published2018
Admission routes1
Has abstractyes

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