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Record W6990799801

El desarrollo de capacidades y su influencia en el desarrollo de las operaciones de desminado humanitario en la Cordillera del Cóndor.

2017· other· es· W6990799801 on OpenAlexaboutno aff

Bibliographic record

VenueIke Skelton Combined Arms Research Library (CARL) Digital Library (US Army Combined Arms Center) · 2017
Typeother
Languagees
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Local DevelopmentRegional development
DOInot available

Abstract

fetched live from OpenAlex

El Conflicto del Cenepa entre los países de Ecuador y Perú durante 1995 trajo como resultado la contaminación de áreas de terreno con minas antipersonales en la Cordillera del Cóndor los cuales continúa siendo un problema para su erradicación y poder cumplir con los compromisos que el Estado peruano contrajo desde la firma del Tratado de Ottawa, muchos de ellos debido a factores externos como el terreno, clima y condiciones meteorológicas que vienen afectando el desarrollo de las Operaciones de Desminado Humanitario (ODH) de los cuales no se pueden modificar o predecir ya que como es sabido estos afectan en cualquier momento y circunstancia, sin embargo, existe factores internos como la doctrina, organización, entrenamiento, material, liderazgo, personal e infraestructura que pueden ser concebidos por la propia organización con la finalidad de no afectar el desarrollo y conducción de las ODH. \nPor lo que, cabe preguntar entonces ¿Qué modelo de desarrollo de capacidades debe adoptar el Ejército del Perú que permita alcanzar con los objetivos de modernización y eficiencia de las ODH en la Cordillera del Cóndor? Las conclusiones establecen la necesidad de tomar conciencia y establecer las brechas que se identifican para poder desarrollar las capacidades en materia de desminado humanitario de forma tal que dicha organización pueda cumplir con las tareas y misiones encomendadas y pueda ser capaz de operar en el ámbito internacional acorde a sus necesidades, amenazas y posibilidad económica de la nación.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient 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 score0.557
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0080.008
Meta-epidemiology (broad)0.0070.004
Bibliometrics0.0090.004
Science and technology studies0.0040.007
Scholarly communication0.0300.035
Open science0.0200.014
Research integrity0.0050.014
Insufficient payload (model declined to judge)0.0080.009

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 head0.020
GPT teacher head0.293
Teacher spread0.273 · 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
Published2017
Admission routes1
Has abstractyes

Explore more

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