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Record W4389222237 · doi:10.59763/mam.aeq.v5i.55

Viajes de investigación para escuchar ballenas en la región del Pacífico Sudeste: un estudio de caso del turismo científico en Ecuador

2023· article· es· W4389222237 on OpenAlexaff
Javier Onã, Ana Eguiguren, Paola Moscoso, Judith Denkinger

Bibliographic record

VenueMammalia aequatorialis · 2023
Typearticle
Languagees
FieldSocial Sciences
TopicCruise Tourism Development and Management
Canadian institutionsDalhousie University
Fundersnot available
KeywordsHumanitiesPersonaArtPolitical science

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.022
GPT teacher head0.295
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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