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Record W2133087621 · doi:10.1190/1.2000287

The MIT Geophysical Analysis Group (GAG) from inception to 1954

2005· article· en· W2133087621 on OpenAlexaboutno aff
Enders A. Robinson

Bibliographic record

VenueGeophysics · 2005
Typearticle
Languageen
FieldComputer Science
TopicComputability, Logic, AI Algorithms
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceOperations researchService (business)George (robot)Library scienceEngineeringManagementArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract The beginning of digital signal processing took place in the years 1950 to 1954. Using an econometric model, E. A. Robinson in 1951 came up with the method of deconvolution, which he tested on 32 seismic traces. Norbert Wiener, George Wadsworth, Paul Samuelson, and Robert Solow were his advisors. On the basis of this work, the MIT president's office in 1952 set up and sponsored the Geophysical Analysis Group (GAG) in the Department of Geology and Geophysics. GAG was made up of graduate students doing research in digital signal processing. In 1953, a consortium of oil and geophysical companies took over the sponsorship. At first, GAG used the MIT Whirlwind digital computer. In order to do the larger amount of computing required by the consortium, the Computer Service Section of Raytheon Manufacturing Company was enlisted in 1953. The Raytheon people who played key roles were Richard Clippinger, Bernard Dimsdale, and Joseph H. Levin, all of whom had worked on ENIAC, the world's first electronic digital computer. As originally built, ENIAC did not use programs stored in memory as does a modern computer; instead, the programming was done by rewiring the physical components for each new problem. In 1948, Clippinger was responsible for converting ENIAC into the world's first operational stored-program computer. ENIAC had 20 accumulators but no other random access memory (RAM). The programs were stored in the function tables, which acted as programmable read-only memory(PROM). For GAG work in 1953, Raytheon used the British Ferranti Mark 1 computer (which was the commercial version of the Manchester Mark 1 computer, for which Alan Turing played a key role). This computer was installed at the University of Toronto to help in the design of the St. Lawrence Seaway. Raytheon was plagued by frequent breakdowns of the computer but still produced several hundred seismic deconvolutions for the summer GAG meeting in 1953. The consortium was pleased with the geophysical results but was disheartened by the unreliability of the current state of digital technology. As a result, GAG was directed to find analog ways to do deconvolution. Instead, GAG found that all of the analog methods, and in particular, electric frequency filtering, could be done by digital signal processing. In fact, the digital way provided greater accuracy than the analog way. At the spring meeting in 1954, GAG proposed that all analog processing be thrown out and replaced by digital signal processing. Raytheon was at the meeting and offered to obtain or build all the elements required for digital signal processing, from input to output. The conversion to digital was not done at the time. However, that step did happen in the early 1960s, and exploration geophysics has the distinction of being the first science to experience a total digital revolution. Digital processing today provides seismic images of the interior of the Earth so startling that they compare to images of the stars made by the Hubble telescope. (In fact, the digital method of deconvolution first developed in geophysics made possible the digital correction of the lens of the Hubble telescope.)

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.999
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.007

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 head0.008
GPT teacher head0.227
Teacher spread0.219 · 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.

Study designQualitative
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

Citations11
Published2005
Admission routes1
Has abstractyes

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