Tourist Visits Prediction with Fully Recurrent Neural Network
Bibliographic record
Abstract
Tourism is a key factor in export income, job creation and local development. The tourism sector is the third largest GDB contributor after oil and mining in Indonesia. Natural beauty and cultural wealth make Indonesia one of the destinations of foreign tourists. The growth tourist visit to Indonesia increases quite significantly (2017BPS data). Bali province is one the biggest contributors of tourist visit to Indonesia. Bali, which is a parameter of tourism in Indonesia, received around 4.001.835 tourists in 2015, or a quarter of tourist visits to Indonesia. Tourism has sustainable expansion and diversification, being one the largest economic sectors with the fastest growth in Indonesia, particularly Bali Province, and increases every year. The number of tourists visiting Bali fluctuates all the time, as it’s influenced by several factors which may affect tourist arrival every year. Visiting tourists spread to various areas and tourists’ objects in Bali. One of the popular tourist destinations is Tanah Lot, which annual average visit of up to one million.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".