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Astrolabes from Medieval Europe

2018· article· en· W210731190 on OpenAlexvenueno aff
David A. King, Sara J. Schechner

Bibliographic record

VenueAestimatio Sources and Studies in the History of Science · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicHistorical Astronomy and Related Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAstrolabeAncient historyMamlukHistoryClassicsLiteratureGeographyArtAstronomyPhysics

Abstract

fetched live from OpenAlex

Contents: Preface Part I General: Astronomical instruments between East and West. Part II The Earliest European Astrolabe: The earliest known European astrolabe in the light of other early astrolabes. Part III An Astrolabe Featuring a Remarkable Number Notation: Rewriting history through instruments: the secrets of a medieval astrolabe from Picardy. Part IV More Individual European Astrolabes: The medieval Catalan astrolabe of the Society of Antiquaries, London (co-authored with Kurt Maier) A remarkable Italian astrolabe from ca. 1300 a witness to an ingenious tradition of non-standard astrolabes An astrolabe from Einbeck datable ca. 1330. Part V Astrolabe Stars: The star-names on three 14th-century astrolabes from Spain, France and Italy. Part VI Universal Horary Devices: A vetustissimus Arabic text on the quadrans vetus 14th-century England or 9th-century Baghdad? New insights on the elusive astronomical instrument called the Navicula de Venetiis. Part VII Two Renaissance Astrolabes: The astrolabe depicted in the intarsia of the studiolo of Archduke Frederico in Urbino The astrolabe presented by Regiomontanus to Cardinal Bessarion in 1462 (co-authored with Gerard L'E. Turner). Part VIII An Aid to Future Research: An ordered list of European astrolabes up to ca. 1500 Index.

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)0.001
metaresearch head score (Gemma)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.997
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.005
Science and technology studies0.0030.002
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0390.006

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 head0.034
GPT teacher head0.276
Teacher spread0.242 · 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 designNot applicable
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

Citations4
Published2018
Admission routes1
Has abstractyes

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