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Record W2952651279 · doi:10.55411/26652544.169

Eco-diseño y los sistemas de enfriamiento

2019· article· es· W2952651279 on OpenAlexaboutno aff
María Dolores Galindo Torres

Bibliographic record

VenueLetras ConCiencia TecnoLógica · 2019
Typearticle
Languagees
FieldSocial Sciences
TopicSocial Skills and Education
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

La industrialización junto con el crecimiento de la población mundial ha llevado a desarrollos tecnológicos para dar respuesta rápida a las necesidades de la sociedad. Al querer sistemas de refrigeración más seguros, con me- jor eficiencia frigorífica, se compromete la estabilidad del medio ambiente al utilizar sustancias químicas como los CFC que agotan la capa de ozono y ponen en peligro la vida en la tierra(Llombai, 2015). Luego de firmar el protocolo de Montreal y las diferentes enmiendas los países miembros están comprometidos vigilar y restringir el uso de sustancias que comprometan el medio ambiente(Sánchez Segura, 2010), los ingenieros deben seguir unas guías para diseñar, instalar y mantener los sistemas de refrigeración y aire acondicionado que no dañen el medio ambiente y hacer sostenible el desarrollo del país.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.003

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 head0.012
GPT teacher head0.306
Teacher spread0.294 · 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; both teacher heads agree on what is shown here.

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
Published2019
Admission routes1
Has abstractyes

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