MétaCan
Menu
Back to cohort
Record W4386605021 · doi:10.1051/e3sconf/202342402005

Research on the Ecological and Economic Impacts of Rising Sea Levels on Mangroves in Southern Florida in the Context of Climate Change

2023· article· en· W4386605021 on OpenAlexaff
Xiaoyan Ding

Bibliographic record

VenueE3S Web of Conferences · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal wetland ecosystem dynamics
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsMangroveContext (archaeology)TourismGeographyClimate changeEcologyBiodiversityEnvironmental resource managementEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

The decline in mangrove ecosystems is a major contributor to the loss of biodiversity, extra carbon release, and local economic instability, particularly in tropical coastal regions that are constantly influenced by climate changes. Despite the importance of the mangroves, there is limited research investigating the consequences. Under the context, this research examines the correlation between mangrove density, sea level rise, and economic impacts in Southern Florida. The work aims to address three main questions: (i) what are the ecological impacts of sea level rising on mangroves density; (ii) what are the economic implications of mangrove loss; (iii) what extent of government intervention should be imposed on the environmental problem. The research presents a two-variable graph that examines the relationship between mangrove density in Southern Florida and sea level rise, analyzing its overall correlation in the Caribbean region. Another correlation study of the impacts of mangrove loss on local tourism is created and set as the focus. The conventional hedonic method is applied as the tool of evaluating mangroves value. The results demonstrate a close negative correlation between sea level and mangrove density, and a positive correlation of mangroves density and local tourism.

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)0.002
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 score0.050
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.103
GPT teacher head0.320
Teacher spread0.218 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueE3S Web of ConferencesSame topicCoastal wetland ecosystem dynamicsFrench-language works237,207