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Record W2352869856

Research on Tourism Resources and Development Strategies of Baicheng City

2014· article· en· W2352869856 on OpenAlexaff
LU Yan-l

Bibliographic record

VenueResource Development & Market · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicCruise Tourism Development and Management
Canadian institutionsScience North
Fundersnot available
KeywordsTourismBusinessEnvironmental planningNatural (archaeology)Natural resourceEcotourismEnvironmental resource managementGeographyEnvironmental scienceEcology
DOInot available

Abstract

fetched live from OpenAlex

Wetland ecosystem,wind-power landscape,folk customs and grassland ecosystem were the main four types tourism resources in Baicheng City in Jilin Province.The rich and the high-quality tourism resources and the convenient location,all these made the Baicheng City competitive in developing the tourism.However,because of the combined effect of natural climate change and human activities,the ecosystems of this city was severely damaged and was becoming sensitive to environmental changes.Furthermore,the seasonal characteristic of tourismwas obvious,so there were a lot of problems to promote the tourism development in Baicheng City.Based on the analysis of local tourism resources and the background,this paper focused on the outstanding problems,and putforward some own views.Finally,according to the current actual situation of the Baicheng City,this paper put forward some suggestions and strategies to promote the scientific development of tourism.

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 score0.069
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.051
GPT teacher head0.335
Teacher spread0.284 · 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
Published2014
Admission routes1
Has abstractyes

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