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El turismo de aventura como impacto socioeconomico en el canton La Mana

2018· article· es· W3000177290 on OpenAlexvenueno aff
Yolanda Tatiana Carrasco Ruano

Bibliographic record

VenueConcienciaDigital · 2018
Typearticle
Languagees
FieldSocial Sciences
TopicGeography and Environmental Studies in Latin America
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesGeographyArt

Abstract

fetched live from OpenAlex

El Ecuador es reconocido por practicar el turismo de aventura ya que es un pequeño paraíso por su biodiversidad y su riqueza de climas y paisajes. Famoso por sus volcanes atractivos como los Illinizas, el Cotopaxi, Cayambe y Chimborazo, y por sus caminatas entre los picos nevados de los Andes ecuatorianos, el Ecuador cuenta también con una maravillosa cultura llena de historias y leyendas. De todas las montañas de los Andes ecuatorianos, las mejor conocidas son Cayambe (5.789m), Cotopaxi (5,897m) y Chimborazo (6,310m). Para alcanzar estas grandes alturas una buena aclimatación es indispensable a fin de disfrutar de las ascensiones plenamente. Por esta razón ofrecemos una escuela de glaciar y caminatas por los picos más bajos, y en los días de descanso, paseos a las aguas termales de las tierras altas. En todos estos viajes será acompañado por guías profesionales que se distinguen por su constante atención a la seguridad. Para aquellos que quieran tener una experiencia en los fascinantes paisajes de los Andes, ofrecemos una selección de excursiones de senderismo a través de las regiones más bellas de nuestro país. Si gusta de la naturaleza virgen y disfrutar de camping, considere nuestros programas de senderismo. Durante nuestras excursiones incluimos experimentados guías, transporte todo-terreno, entradas, alimentación y equipo de campamento.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.007
GPT teacher head0.295
Teacher spread0.288 · 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 designObservational
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

Citations3
Published2018
Admission routes1
Has abstractyes

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