MétaCan
Menu
Back to cohort
Record W4317401665 · doi:10.18280/mmep.090608

Assessment of Geo-Environmental Consequences of Oil and Gas Complex Enterprises’ Extraction Activities on the Shelf

2022· article· en· W4317401665 on OpenAlexvenueno aff
M.V. Zaretskaya

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological Studies and Exploration
Canadian institutionsnot available
FundersRussian Foundation for Basic Research
KeywordsSubmarine pipelineProduction (economics)Petroleum engineeringWork (physics)Fossil fuelExtraction (chemistry)Environmental scienceProcess (computing)GeologyContinental shelfMode (computer interface)Petroleum industryEarth scienceComputer scienceOceanographyEngineeringEnvironmental engineeringWaste managementEconomicsMechanical engineering

Abstract

fetched live from OpenAlex

One of the causes of rising seismic tension in a territory is the production activity of mining enterprises, including those in the oil and gas industry. The paper reports the results of modeling and analysis of the tensions of geological structures, which include the contact areas of the continental and oceanic crust, making a slow horizontal movement. The technogenic impact of the production process of an offshore oil and gas production platform and the pressure of the ocean water column are taken into consideration. The differential factorization method is used to investigate the posed boundary value problems. The study assesses the occurring contact stresses and draws conclusions about their dependence on the distance between the plates, the thickness of the water layer, and the frequency of the external load, modeling the intensity of the production process. The results obtained can be used by oil and gas companies to work out scenarios of the production process in a risk-free mode.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.210
Teacher spread0.172 · 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 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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueMathematical Modelling and Engineering ProblemsSame topicGeological Studies and ExplorationFrench-language works237,207