Recreational uses and motivations of visitors in seaside wetlands of costa brava (Spain)
Bibliographic record
Abstract
Seaside wetlands bring together a huge quantity of services and functions, especially ecological and social.All of them depend on the ecological quality of ecosystems and on the equipment and service that allow them to be enjoyed.Tourism is an economic segment, carrying an important weight in most coastal regions around the world.Hence a complete environmental management of seaside wetlands should include the touristic perspective.In this communication, we analyse usage and motivation in three seaside wetlands of the Costa Brava (Spain) through a survey realized in the months of the highest occurrence of visitors (from June to September 2015).From the results, we highlight the high number of visitors who use the sites for recreational purposes (49%), such as running or cycling, in comparison with visitors who stated aesthetic motivations (16%).Many visitors also stated no motivation for visiting the sites (31%); they use them as car parks to go to the beach or as a byway to other sites.On the other hand, most visitors stressed the landscape (30%) or the degree of naturalness (29%) as a positive element of seaside wetlands, while the majority of negative elements are linked to bad management of the site (36%).When we requested a landscape valuation in a five-point scale, a significant number of high values were shown.Furthermore, we found a link between evaluation and tourism typology (local, national or foreign, lodged or excursionist) and motivation for visit (recreational, aesthetic or without).The principal conclusion is that, despite the fact that the main uses of the seaside wetlands are recreational, tourists appreciate landscape quality and degree of naturalness in sites where they develop their activities.This assigns to tourism, especially seaside tourism, an active role in conservation of seaside wetlands.
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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 | 0.000 |
| 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 | 0.001 | 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".