Guidelines to the Management of Firefly Watching Tour in Thailand
Bibliographic record
Abstract
This article presents findings from the study on “Guidelines to the Management of Firefly Watching Tour in Thailand”. The study utilized the Delphi technique and 18 experts in 6 fields, namely entomology, mangrove forest, tourism, environment, economy and social science. It reveals the first three priorities for management that include: 1) Campaigning and working on public relations for the conceptual change from the mainstream tour to ‘eco-friendly tour’ through conservation or restoration-related activities and participation from tourists; 2) Managing the tourism based on understanding in nature and; 3) Monitoring environmental changes, both physically and biologically, on a constant basis with collaboration from concerned parties such as Sub-district Administration Organization (SAO), local tour companies and community leaders to create the direction and appropriate patterns of firefly watching tour. Among findings is the management that highly requires collaboration from local people and participation from concerned parties including tour companies, local agencies such as Sub-district Administration Organization (SAO) and municipalities, state agencies and tourists. Furthermore, the finding highlights the use of youth power in order to strengthen the awareness in environment and nature preservation in local communities. By using this, youth will be instilled with knowledge through curriculum at kindergarten, elementary and secondary level. At the end, the nature preservation will exist in them since young ages, along with the sustainability of preservation in their communities.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".