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Record W2566169473

Экологический туризм в России и странах Скандинавии

2016· article· ru· W2566169473 on OpenAlexaboutno aff

Bibliographic record

VenueСервис в России и за рубежом · 2016
Typearticle
Languageru
FieldEnvironmental Science
TopicDiverse Scientific Research in Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsEcotourismTourismPopularityRecreationDestinationsPopulationGeographyState (computer science)Alternative tourismBusinessEconomyEconomic growthEnvironmental protectionPolitical scienceEconomicsSociology
DOInot available

Abstract

fetched live from OpenAlex

In recent years, in our country there is a significant growth of interest in such unusual trend in tourism as ecological tourism. Ecotourism is a kind of nature tourism, which brings together people who want to be as close as possible to nature. The very concept of for our country is a relatively new, but in Western Europe, USA, Canada, Australia and other countries such kind of rest has already gained a lot of popularity. In Russia, despite the opportunities for development of this trend, ecotourism is underdeveloped. This article defines the concept of ecotourism, describes the peculiarities of this type of tourism, and lists the main requirements for eco-tours. The authors give the main reasons hindering the development of eco-tourism in our country. The authors consider in detail the Nordic countries as an example of the highest level of eco-tourism organizing. Currently, it is one of the most popular destinations for eco-tourists. The Nordic countries, namely Norway, Sweden and Denmark, provide great opportunities for the ecotourism development. It is promoted by the magnificent nature, picturesque landscapes, good level of state support and protection of national parks and reserves, the interest of the local population in maintaining the ecotourism facilities in proper state and creation of comfortable conditions for recreation of foreign and domestic tourists, who prefer to spend their holidays or a weekend surrounded by nature. There are solutions for active rest and relaxing vacation, which allows the Nordic countries to attract more and more eco-tourists year by year.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.386
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.004
Scholarly communication0.0010.002
Open science0.0040.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1540.180

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 head0.024
GPT teacher head0.264
Teacher spread0.240 · 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
Published2016
Admission routes1
Has abstractyes

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