Evaluation of the Tourism Climate in M’Sila Province Using the Tourism Climate Index (TCI)
Bibliographic record
Abstract
Climatic factors constitute one of the most important elements of tourist attraction and a vital natural resource for any tourist destination. Many studies in applied climatology link the degree of influence of climatic characteristics with tourism, highlighting the term “climatic comfort” as one of the most essential conditions for attracting tourists. This study aims to evaluate the tourism climate of M’Sila Province using the Tourism Climate Index (TCI) developed by Mieczkowski in 1985, which is considered one of the significant indicators applied to many regions worldwide to assess the impact of climate on tourism attraction. The index relies on various climatic variables, including daytime comfort index (CID), daily comfort index (CIA), precipitation index (R), sunshine duration index (S), and the wind speed index (W), to formulate the mathematical equation with specific standardized weights and proportions. The equation of this index was applied to all months of the year to identify the most climatically suitable tourism months in M’Sila Province, based on climatic data from the two meteorological stations (M’Sila and Bousaada) for the period from 1991 to 2023. The results of the Tourism Climate Index (TCI) evaluation for M’Sila Province indicate a range from acceptable to excellent, with evaluation scores ranging between 56.2 and 82.4. These results are considered favorable and contribute to attracting tourists throughout most of the year. For the M’Sila station, the results showed an excellent tourism climate in April and October, a very good tourism climate in March, May, September, and November, and a good tourism climate in January, February, June, July, August, and December. The acceptable tourism climate was recorded in August. For the Bousaada station, the results were similar to those of the M’Sila station but consisted of only three categories of tourism climate. A very good tourism climate was observed in March, April, May, September, October, and November, a good tourism climate in January, February, June, and December, and an acceptable tourism climate in July and August. The study concluded that M’Sila Province enjoys an attractive tourism climate during most months of the year, making it a significant tourism hub that combines various types of tourism due to its diverse and abundant tourism potential. Consequently, the study recommended enhancing and rehabilitating all tourism resources in the province, with greater focus on maintaining and expanding green spaces.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".