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Record W2034251394 · doi:10.1190/tle33020130.1

Introduction to this special section: 4D

2014· article· en· W2034251394 on OpenAlexaff
Bill Goodway

Bibliographic record

VenueThe Leading Edge · 2014
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsCalgary Construction Association
Fundersnot available
KeywordsPaceSection (typography)Special sectionGeologyPetroleum industryEnvironmental scienceComputer scienceGeodesyEngineeringPaleontologyEngineering physics

Abstract

fetched live from OpenAlex

Time lapse is defined as a technique in which exposures are taken at fewer intervals than usual so that on playback, a naturally slow process may be viewed at an accelerated pace. In our seismic parlance, 4D time lapse monitoring is a series of snapshots of changes induced in the earth, taken over periods of days to years. So what is the potential impact of seismic 4D time lapse in the oil and gas (O&G) industry? Following the doomsday prediction of having passed various “peak oil” peaks, Figure 1 clearly demonstrates the O&G industry's motivation to recover from 25% to 60% more hydrocarbons beyond primary 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 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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.219
Threshold uncertainty score0.733

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0050.004
Open science0.0020.003
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.2190.154

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 head0.257
Teacher spread0.241 · 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 designNot applicable
Domainnot available
GenreEditorial

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
Published2014
Admission routes1
Has abstractyes

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