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Record W4410946412 · doi:10.24043/001c.138657

Climate Change in Chiloé (Chile): Trends, Coastal Zones and Socio-Ecological Conflicts

2025· article· en· W4410946412 on OpenAlexvenueno aff
Francisco José Vázquez Pinillos, J. Adolfo Chica Ruiz, Juan Manuel Barragán-Muñoz

Bibliographic record

VenueIsland Studies Journal · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeGeographyEcologyEnvironmental resource managementPhysical geographyEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
Threshold uncertainty score0.898

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.160
GPT teacher head0.393
Teacher spread0.233 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2025
Admission routes1
Has abstractyes

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