Bibliographic record
Abstract
Nowadays, many of the policy-makers and development planners recount tourism as one of the main elements of sustainable development.The extant realities indicate that traveling in nature has incurred irreparable damages to the natural resources.The study model has been formed by making use of factors like favorable tourism resorts, sustainable development of tourism, metropolitan's economical growth as the dependent variable and bioenvironmental, socio-cultural, economical and infrastructural factors as the independent variable.The present study aims at the calculation of the extent to which the bioenvironmental standards, socio-cultural, economical as well as infrastructure factors influence the selection of tourism appropriate zones and also designing plans for sustainable exploitation of the environment and keeping the nature intact as much as possible.To do so, a library research method, interviews with the specialists and Delphi technique have been applied on a sample of 20 individuals including the urban planning experts and environment conservation specialists to gather the required information.The required statistics and information have been made available to the experts in the format of questions posited within questionnaires.The scales obtained though running Delphi method have been assessed and weighted comparatively through taking advantage of analytic hierarchy process (AHP).then, each of the scales underwent a valuation process by making use of geographical information system (GIS) software within the regional area of concern to the present study.The results of the study, after the obtained layers overlapping analysis, indicate that the bioenvironmental scale takes the first position with the highest effectiveness after which socio-cultural, economical and infrastructure scales are placed in the second priority.Next, after the main scales' overlapping was evaluated, the zones prioritization was offered in line with the development of the facilities and the conveniences.The present study intends to provide guidelines by way of which natural resources sustainable utilization, economical booming and residential stability could be facilitated in such a manner that it will finally positively influence the region's development and prosperity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.948 | 0.929 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".