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Record W7055337843

Centro de alto rendimiento deportivo en el distrito de San Juan de Miraflores

2022· dissertation· es· W7055337843 on OpenAlexaboutno aff

Bibliographic record

Venuerenati · 2022
Typedissertation
Languagees
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPopulationWork (physics)Quarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

La presente tesis tiene como finalidad el diseño de un CARD en el distrito de San Juan de Miraflores. El proyecto se enfoca en el desarrollo de los deportes de Basketball, Natación, Box y Vóley. \nSe propone cuatro unidades: Complementario, Medico, Residencial y Deportivo y está sustentado bajo el Reglamento Nacional de Edificaciones \nLos referentes Nacionales e Internacionales presentados y la información del IPD ayudo a saber las necesidades que los deportistas tienen. Además, la información recaudada del distrito de San Juan de Miraflores nos ayudó a ver las necesidades que la población tenía y así se pudo implementar espacios semi públicos para la ciudadanía \nPara concluir los CARD son edificaciones para el desarrollo profesional del deportista, en donde ellos pueden sentir que es su hogar, por ende, se brinda la mejor infraestructura y maquinaria de última generación.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.206
Threshold uncertainty score0.409

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0120.002

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 head0.006
GPT teacher head0.281
Teacher spread0.275 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2022
Admission routes1
Has abstractyes

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Same venuerenatiSame topicMagnetic Field Sensors TechniquesFrench-language works237,207