Approximation of the benefits of socioeconomic activities in Cocos Island National Park and the effects of climate change
Bibliographic record
Abstract
The objective of this study was to quantify the economic benefits provided by the cluster of activities developed thanks to the existence of the Cocos Island National Park (CINP) and the ecosystem services it offers. The methodology consisted of employing cluster analysis complemented by a value chain approach identifying productive and non-productive activities related to the CINP and their interrelationships at the local, regional, national and international levels. The results determined that in 2019 the CINP allowed the generation of an income of US$19,682,466, of which the most significant contribution was made at the national level (88% of the total). The findings indicate the importance of protecting natural resources and marine species such as sharks (especially the hammerhead shark -Sphyrna lewini) from two potential threats, fishing and the effects of climate change, since the disappearance or decrease of such species could affect economic benefits.
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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 | 0.000 | 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".