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Record W2141769977 · doi:10.1071/aseg2015ab042

Logging during diamond drilling - Autonomous logging integrated into the Bottom Hole Assembly

2015· article· en· W2141769977 on OpenAlex
Andrew Greenwood, Anton Kepic, Anna Podolska, Christian Dupuis, G. Stewart

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

VenueASEG Extended Abstracts · 2015
Typearticle
Languageen
FieldEngineering
TopicTunneling and Rock Mechanics
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsDrillingLoggingBoreholeMeasurement while drillingScientific drillingPetroleum engineeringMud loggingDiamondGeologyDrilling fluidEngineeringMechanical engineeringGeotechnical engineeringMaterials science

Abstract

fetched live from OpenAlex

Logging total count gamma data while diamond drilling an HQ borehole has been achieved using an autonomous shuttle. The shuttle is integrated into the Bottom Hole Assembly (BHA) prior to drilling. Logging is initiated at the beginning of each core run and the shuttle unit continuously logs at 1 second intervals. Continuous logging combined with the relatively slow rate of penetration of diamond drilling results in high fidelity logs at 1-5 cm intervals. The data is collected by the drilling crew, who download and email the data at the end of each core run for near real time analysis. Little to no interruption to the normal drilling process is experienced once the Shuttle has been integrated into the BHA. Autonomous logging while diamond drilling enables the collection of in-situ rock property measurements, without the risks and costs associated with later wireline logging. This value is added to the drilling process at little expense.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.279
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.014
GPT teacher head0.225
Teacher spread0.211 · 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