Viajes de investigación para escuchar ballenas en la región del Pacífico Sudeste: un estudio de caso del turismo científico en Ecuador
Bibliographic record
Abstract
Scientific tourism is an emerging but poorly studied field in the Southeast Pacific region. We conducted a literature review to assess the current state of scientific tourism and included a case study of humpback whale-listening research tours off the coast of Esmeraldas in northern Ecuador. Additionally, we conducted online interviews to examine changes in people’s perception of whale-observation tours (comparing visual and auditory experiences). The literature review revealed more published articles related to whale-watching aided research than to scientific tourism. Still, we found that humpback whale-listening research tours operate in the region. Human facial expressions showing emotions such as happiness and surprise were the most frequently recorded reactions when people listened to humpback whale songs. Online respondents mostly expressed high satisfaction when listening to whale songs or seeing whales up close (< 5 m). However, after respondents read about the impact of tour boats on whales’ well-being, most respondents preferred to watch and listen to whales at a distance of 100 m. Whale-listening tours generate emotional well-being in people who participate, and we consider that the whale-watching industry could implement this activity to promote compliance with distance guidelines for observing humpback whales in their breeding grounds.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".