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Stuart H. Perry's contributions to meteorite collection and research, 1927–1957

2009· article· en· W1967686923 on OpenAlexaff
Howard Plotkin, Roy S. Clarke

Bibliographic record

VenueMeteoritics and Planetary Science · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsWestern University
FundersAustralian GovernmentSmithsonian Institution
KeywordsMeteoriteAstrobiologyMetallographyGeologyMetallurgyMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Abstract— Stuart H. Perry (1874–1957), an influential Michigan newspaper editor and publisher and a vice president of the Associated Press, developed a passionate interest in collecting and studying meteorites in the 1920s and 1930s. Firmly believing that meteorites belong in great museums where they can be properly investigated, he generously donated his meteorites to various museums after he finished his own study of them. He had a sincere interest in the National Collection of Meteorites, and donated 192 specimens–‐mostly irons–‐to the U.S. National Museum; these constituted some of the most important meteorites in its collection, and moved iron meteorites to center stage, a position still occupied. By applying current metallographic methods to the study of iron meteorites, Perry directed scientists to a powerful new research tool, which led to major advances in our understanding of meteoritic irons and helped give rise to a new field within planetary sciences. His groundbreaking monograph The metallography of meteoric iron served as a standard reference collection of metallographic photomicrographs of iron meteorites for more than 30 years. It remained an insightful and useful work on the structure of meteoritic iron until improved binary and ternary phase diagrams in the Fe‐Ni(‐P) system allowed a more detailed treatment of the formation of iron meteorites. Perry received many honors for his work, and held office in the Meteoritical Society, serving as a councilor from 1941–1950, and as a vice president from 1950–1957.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.017
GPT teacher head0.309
Teacher spread0.292 · 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.

Study designObservational
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
Published2009
Admission routes1
Has abstractyes

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