Analyzing the role of Bavan Valley in Mamasani as tourists attraction
Bibliographic record
Abstract
Tourism industry plays an important role on developing economy especially in regions where there are different historical, landscape and other natural attractions.Bavan Valley located in Nur Abad Mamasani city in Iran is one of the well-known places among tourists.The region has outstanding natural landscapes, moderate weather especially in spring and summer, low distance from the major road locating between different local regions such as Fars, Bushehr, Khuzestan, and Kohkiluye Boyer Ahmad Province.These regions provide appropriate accessibility for the citizens of highly populated cities of this province and it plays essential role of a major attractive pole in southern part of the country.The primary objective of this research is to recognize the present barriers for attracting tourists and to analyze the tourists' satisfactions associated with the facilities and tourist services.The statistical population of this research includes all the tourists of Bavan Valley in which 381 individuals were chosen as the sample of this research, using Cochran's formula.The results indicate that there is a significant relationship between the absence of advertisement about Bavan Valley and the number of tourists in this zone (P<0.05).The findings also show that there is a significant relationship between lack of infrastructural equipments and un-development in tourism industry (P<0.05).Moreover, the findings of SWOT analysis indicates that 9 internal strength versus 10 internal weaknesses and 7 external chance versus 8 external threat were recognized and analyzed with regard to ecotourism in this zone.Thus, generally 16 strength and chances as the advantages and 18 weaknesses and threats as the obstacles about the Bavan Valley's tourism were recognized in order to develop 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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".