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Record W2318569502 · doi:10.1071/aseg2012ab172

Joint 3D of muon tomography and gravity data to recover density

2012· article· en· W2318569502 on OpenAlex

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueASEG Extended Abstracts · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMuonGeodesyGeologyFlux (metallurgy)Inversion (geology)Field (mathematics)PhysicsData setJoint (building)GeophysicsNuclear physicsSeismologyComputer scienceEngineeringMathematicsCivil engineering

Abstract

fetched live from OpenAlex

SummaryCosmic rays producing muons shower the Earth daily. These natural, high-energy particles decay as they pass through matter and are directly affected by density. Recently, sensors have been placed in existing tunnels and mine shafts that observe muon flux in a brown-field mining scenario. We have developed an algorithm to invert these data individually, or jointly with gravity data, to recover a 3D distribution of density. Muon and gravity data are both linear functionals of density but the associated sensitivity functions are substantially different. These differences in physics between muon ray paths and gravity data provide a unique insight into the subsurface. This is illustrated through synthetic examples. Inversion of a set of field data, obtained at a mine site in south-west British Columbia, Canada, illustrates the potential benefits and challenges for the technique to be used in field surveys.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.982
Threshold uncertainty score0.381

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 integrity0.0000.000
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 head0.051
GPT teacher head0.272
Teacher spread0.221 · 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