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Record W2090891767 · doi:10.1029/2012eo160001

Polarimetric synthetic aperture radar utilized to track oil spills

2012· article· en· W2090891767 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEos · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsEnvironment and Climate Change Canada
FundersJapan Aerospace Exploration Agency
KeywordsPetroleumSubmarine pipelinePetroleum engineeringOil spillDrillingOffshore drillingEnvironmental scienceTrack (disk drive)Fossil fuelGeologyOceanographyEngineeringWaste management

Abstract

fetched live from OpenAlex

The continued demand for crude oil and related petroleum products along with the resulting upward spiral of the market price of oil have forced oil exploration and production companies to seek out new reserves farther offshore and in deeper waters. The United States is among the top five nations globally in terms of estimated offshore oil reserves and petroleum production. Yet deepwater drilling to extract these reserves is a major engineering challenge for oil companies. Moreover, such drilling activity also comes with a significant environmental risk, and the extremely high pressures associated with deepwater oil wells mean that the mitigation of accidental releases from a deepwater spill is truly a challenging endeavor.

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.

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 categoriesInsufficient 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.735
Threshold uncertainty score0.996

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.008

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 head0.009
GPT teacher head0.224
Teacher spread0.215 · 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