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Record W6908884391 · doi:10.34989/swp-2022-36

Weather the Storms? Hurricanes, Technology and Oil Production

2022· article· en· W6908884391 on OpenAlexaff

Bibliographic record

VenueEconstor (Econstor) · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsUniversity of TorontoBank of Canada
Fundersnot available
KeywordsNucleofectionGestational periodTSG101DiafiltrationHyporeflexiaProteogenomicsLiquationFusible alloyHemopericardium

Abstract

fetched live from OpenAlex

Do technological improvements mitigate the potential damages from extreme weather events? We address this question using oil production and hurricane data from the Gulf of Mexico. We show that hurricane activity lowers well production and that stronger storms have larger impacts that persist for months after impact. Hurricanes also significantly increase the probability that oil assets are stranded, particularly when the hurricanes pass within 50km of an oil rig’s location. Regulations enacted in 1980 that required improved construction standards for rigs in the Gulf only modestly mitigated the short-run production losses caused by hurricanes. The 1980 regulatory reforms also modestly decreased the probability that leases permanently exited production.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.217
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.000

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 head0.011
GPT teacher head0.212
Teacher spread0.201 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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