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A series of confusing measurements in the search for water

2019· article· en· W2984164604 on OpenAlexaff
Chris Wijns

Bibliographic record

VenueASEG Extended Abstracts · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsFirst Quantum Minerals (Canada)
Fundersnot available
KeywordsGeologyBoreholeBedrockWater tableElectrical resistivity and conductivityBasementDrillingStructural basinWell loggingMining engineeringGroundwaterGeomorphologyGeophysicsPaleontologyGeotechnical engineeringArchaeology

Abstract

fetched live from OpenAlex

SummaryA mine development in the high Andes of north-western Argentina requires a supply of fresh water for various uses, the largest being mineral processing. Complementary survey techniques in a gravel basin have provided vastly different and sometimes contradictory results. Mapping of the basin geometry proceeded via downhole resistivity logging, moving loop ground EM, and galvanic resistivity. The expected layering of low resistivity gravel fill over high resistivity basement rock, which was demonstrated by prior downhole logging, was turned on its head by surface electrical and EM surveys. Furthermore, subsequent borehole resistivity logs, which do not reach bedrock, support the increase of conductivity with depth, while water sampling indicates this is not due to greater salinity. Surface nuclear magnetic resonance was trialled for the direct detection of the upper part of the water table. The majority of these data were contaminated by mysterious noise sources in an area hundreds of kilometres from the nearest atmospheric activity, and fifty kilometres from the closest settlement. False negative readings appear to be common. The most recent drilling, demonstrating much deeper gravel than previously drilled or imagined, may explain an inability to image the bottom, but the reason for the deep conductivity remains a mystery.

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 score0.962
Threshold uncertainty score0.319

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 head0.053
GPT teacher head0.279
Teacher spread0.225 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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