Climate Change in Chiloé (Chile): Trends, Coastal Zones and Socio-Ecological Conflicts
Bibliographic record
Abstract
The archipelagic geography of the province of Chiloé (Chile) makes it one of the most vulnerable places in the country to climate change. In this paper, the consequences of climate change in Chiloé are analyzed using two methodological approaches in an effort to improve our understanding of this threat. Firstly, the Low Elevation Coastal Zones (LECZ) have been identified using the 16 m isohypse as a reference. This threshold aligns with the maximum run-up of tsunamis recorded in Chile. Consequently, it has been determined that the LECZ contain a quarter of Chiloé’s urban infrastructure, as well as 665 specific elements of significant socio-economic value. Secondly, the potential impact of climate change on the most pressing socio-ecological conflicts, particularly those related to water stress and salmon farming, has been examined. Issues such as the occurrence of red tides, the proliferation of invasive species, or the reliance on water trucks for summer water supply are likely to become more frequent in the future. In conclusion, it is evident that climate change not only introduces new challenges to the region but also exacerbates existing coastal-marine problems. The development of a specific instrument to address this phenomenon in the province of Chiloé is therefore crucial to reduce the effects of climate change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".