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Record W4392803236 · doi:10.51598/tab.980

La descripción de mapas y dibujos de arquitectura en AtoM

2024· article· es· W4392803236 on OpenAlexaboutno aff
Javier Fernández

Bibliographic record

VenueTábula · 2024
Typearticle
Languagees
FieldSocial Sciences
TopicUrbanism, Landscape, and Tourism Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArtPhilosophy

Abstract

fetched live from OpenAlex

El presente artículo tiene como objetivo principal dar a conocer a la comunidad de usuarios del software AtoM (Access to Memory) la plantilla RAD, correspondiente a la norma canadiense Rules for Archival Description. Las reglas canadienses cuentan con capítulos específicos para la descripción de documentos textuales, documentos en distintos tipos de soportes, materiales gráficos, materiales cartográficos, dibujos técnicos y de arquitectura, imágenes en movimiento, grabaciones sonoras, documentos electrónicos, microformas, objetos, documentos filatélicos y documentos aislados que no forman parte de un fondo o colección. Asimismo, se explica la aplicación que se hace de la plantilla en el Archivo General de Palacio en la descripción de planos de arquitectura y mapas. Y, por último, repasaremos su etiquetado EAD y el buscador de documentos. The main purpose of this article is to inform the community of users of the AtoM software about the RAD template, derived from the canadian standard Rules for Archival Description. These rules have several specific and usable chapters for the description of textual records, units consisting of multiple media, graphic materials, cartographic materials, architectural and technical drawings, moving images, sound recordings, records in electronic form, records on microform, objects, philatelic records and discrete items. Furthermore, the application of the template in the Archivo General de Palacio in the description of architectural drawings and maps is explained. And finally, we will review its EAD tagging and its search functionality.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.787
Threshold uncertainty score0.983

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.011
GPT teacher head0.301
Teacher spread0.291 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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