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Simulation of Logging While Drilling Tools Based on Amorphous Si:H and LaBr3:Ce Detectors

2013· article· en· W1686426322 on OpenAlexvenueno aff
V. Esmaeili Sani, N. Akbar Ashrafi, H. Afarideh

Bibliographic record

VenueAdvances in petroleum exploration and development · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsGamma spectroscopyDetectorNeutronWell loggingMaterials scienceNeutron temperatureSpectroscopyNeutron detectionPhysicsBoronNuclear physicsOpticsGeophysics

Abstract

fetched live from OpenAlex

Bore hole logging data represent the properties of rocks, such as porosity, density and constitutiveelements of formation, as a function of depth in a well. Properties of rocks are obtained from thermal neutron counting and gamma ray radiation due to neutron activation. Transport of neutrons, from an Am-Be source situated in a bore-hole tool, through rock media to detectors, has been simulated using a GEANT4 radiation transport code. The high precision GEANT4 cross section library was used to gain better analyses about well formation. In this paper we present the results of simulation of logging tools based on boron coated amorphous silicon detector for thermal neutron detection and LaBr3:Ce detector for gamma spectroscopy. Key words: Well logging; Spectroscopy; GEANT4

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.422

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 communication0.0000.001
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 head0.020
GPT teacher head0.247
Teacher spread0.227 · 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.

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

Citations0
Published2013
Admission routes1
Has abstractyes

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