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Record W2163904561 · doi:10.1071/aseg2015ab104

Airborne Inductive Induced Polarization Chargeability Mapping of VTEM Data

2015· article· en· W2163904561 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 · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsPetro Geotech (Canada)
FundersCommonwealth Scientific and Industrial Research Organisation
KeywordsInduced polarizationTime domainElectrical resistivity and conductivityGeologyPolarization (electrochemistry)Frequency domainData setMineralogyRemote sensingGeophysicsElectrical engineeringMathematicsComputer scienceEngineeringChemistryStatisticsMathematical analysis

Abstract

fetched live from OpenAlex

Airborne inductive induced polarization (AIIP) effect has been widely recognized in airborne time domain EM system data. AIIP chargeability mapping opens new and exciting areas in mineral exploration for airborne time domain EM systems in the search for sulphides and clay minerals. An AIIP chargeability mapping tool based on CSIRO/AMIRA Airbeo is created for VTEM data, with examples from Mt Milligan, British Columbia, Canada and Tullah, Tasmania. Using the Cole-Cole frequency dependent resistivity, the tool examines the VTEM decay data spectrally and selects the decay associated with the lowest RMS error from a set of decays generated by varying chargeability m and time constant T within specific ranges, giving a constant frequency factor c, while the background resistivity is inverted. The parameter mo used to generate the decay is the AIIP apparent chargeability.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.994
Threshold uncertainty score0.586

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.112
GPT teacher head0.296
Teacher spread0.184 · 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