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Record W2069323921 · doi:10.1190/1.1579574

Recent advances in airborne survey technology yield performance approaching ground-based surveys

2003· article· en· W2069323921 on OpenAlexaff
William E. Doll, T. Jeffrey Gamey, Les P. Beard, D. T. Bell, J. Scott Holladay

Bibliographic record

VenueThe Leading Edge · 2003
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsGeneral Motors (Canada)
Fundersnot available
KeywordsYield (engineering)Remote sensingEnvironmental scienceGeographyPhysics

Abstract

fetched live from OpenAlex

Airborne magnetic and electromagnetic systems have been very effective over the years for mineral prospecting and in support of petroleum exploration. More recently, these towed-bird systems, operating at sensor altitudes of 30–50 m, have supported environmental investigations. The towed-bird systems can provide regional data for site investigations, such as locating or delimiting the boundaries of waste areas, identifying geologic contacts that influence environmental issues, or mapping saline intrusion. However, these conventional systems cannot provide the resolution required in many environmental and engineering problems because the distance between sensors and target objects is too great. Ground-based surveys are often suitable for addressing these problems but, for many sites, the area can be too large to be expediently addressed with surface geophysics. Contamination of government land with unexploded ordnance (UXO) is one such large-scale problem.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.010

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 head0.044
GPT teacher head0.261
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations26
Published2003
Admission routes1
Has abstractyes

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