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Record W2313482346 · doi:10.1071/aseg2003ab054

Exploration for deep Paleozoic sediments in Uzbekistan using MT: Project “Paleorift”

2003· article· en· W2313482346 on OpenAlexaff
Lydia K. Fox, A. Ingerov, A. A. Abidov, F.G. Dovgopolov, T. L. Babajanov, M.D. Basov, A.B. Kocherov, I.S. Feldman, Yann Avram, C. Finateu

Bibliographic record

VenueASEG Extended Abstracts · 2003
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsPhoenix Technologies (Canada)
Fundersnot available
KeywordsPaleozoicGeologyProspectingGrabenMesozoicNatural gas fieldPaleontologySource rockFossil fuelMining engineeringStructural basinNatural gas

Abstract

fetched live from OpenAlex

To maintain self-sufficiency in oil, Uzbekistan is systematically exploring all oil-prospective basins in the country, including the Bukharo-Khivinsky paleorift (BKP) in central Uzbekistan. The BKP has a prospective area of 14 100 km2 and contains an estimated 63 000 km3 of prospective Paleozoic sediments below gas-producing Mesozoic sediments. The “Paleorift” project (begun in November 2001) is an integrated project comprising ten regional profiles 80-90 km long that will include seismic (using new 3D equipment), MT, gravity, magnetics, and thermometry. The field work began with MT because MT costs less than seismic, is easy and quick to use and interpret, causes minimal disturbance to agricultural land, and is definitive in this application. Uzbekneftegaz has long experience with MT and other electrical methods and routinely uses them before seismic. Approx. 350 MT stations were measured on three of the regional profiles (1 km station spacing) and on two “prospecting” profiles (500 m station spacing) over known Mesozoic gas deposits. The profiles mapped the boundaries of the BKP central graben, a crustal conductor in the north part of the BKP, a gas-producing Jurassic structural high, and a resistivity low associated with the gas deposits.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.983
Threshold uncertainty score0.620

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 head0.305
Teacher spread0.254 · 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 designOther design
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

Citations1
Published2003
Admission routes1
Has abstractyes

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