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Record W7009415507

Diseño de los pavimentos de la nueva carretera Panamericana Norte en el tramo de Huacho a Pativilca (KM 188 a 189)

2015· dissertation· es· W7009415507 on OpenAlexaboutno aff

Bibliographic record

Venuerenati · 2015
Typedissertation
Languagees
FieldMaterials Science
TopicEngineering and Material Science Research
Canadian institutionsnot available
Fundersnot available
KeywordsBlock (permutation group theory)Quarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

La nueva carretera Panamericana Norte se encuentra al norte de Lima. Actualmente el tramo de Ancón – Huacho – Pativilca se encuentra en concesión a Norvial S.A. En esta tesis se realiza el diseño del pavimento de un kilómetro de esta carretera en el tramo de Huacho a Pativilca. Específicamente, según el temario del tema de tesis el kilómetro designado por el asesor fue del 188 al 189. La carretera Huacho – Pativilca tiene 57 kilómetros de longitud y conecta las ciudades de Huacho, Huaura, Medio Mundo, Supe, Barranca y Pativilca. En general, la Panamericana Norte es una carretera interprovincial que conecta todos los departamentos de la Costa. El tramo de estudio de esta tesis une a las provincias de Barranca y Huaura. Cabe resaltar que entre las particularidades de la zona se incluye el tránsito de gran porcentaje de vehículos pesados. Además presenta un clima templado y con pocas precipitaciones. Se procede con el diseño del pavimento tanto flexible como rígido. Para el tipo flexible se utiliza la metodología de la American Association of State Highway and Transportation Officials (AASHTO) y la del Instituto del Asfalto (IA), mientras que para el rígido se utiliza también la de la AASHTO y la de la Portland Cement Association (PCA). Por último, una vez obtenidos los diseños definitivos para los dos tipos de pavimento se procede a una comparación económica del costo inicial de construcción de esta estructura.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.451
Threshold uncertainty score0.898

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.011
GPT teacher head0.358
Teacher spread0.348 · 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
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
Published2015
Admission routes1
Has abstractyes

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