Bibliographic record
Abstract
Studying and identifying the constraints and threatening risks of atmosphere as well as awareness of the gravities and the potential in geographical during climate features in different seasons is very important in tourism planning.Tourism Climate Index (TCI) as a useful indicator in the field of tourism integrates the different parameters of climate and presents them in the form of an index that easily is interpretable by tourists.The goal of this study is offering tourism monthly calendar and also accordance of time-local of attracting the tourist using the TCI.In order to study climate Index in Kerman, first, the monthly recorded data of seven required climate parameters was gathered from synoptic stations of Kerman in the period of 60 years (1951( -2010 AD) AD) and after analyzing and processing and preparing the database, obtain the rank of each components of the CID, CIA and ultimately TCI's value is calculated separately for months.The results of this study indicate that during a year May has the highest Tourism Climate priority in Kerman with the ideal descriptive features and after September and October with the same described features.The lowest priority is related to months of December and then January and February.In other words, spring and fall in Kerman, Tourism Climate have high priority in spite of summer and winter which have low priority of tourism climate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.914 | 0.923 |
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".