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Record W2557505635 · doi:10.5539/jgg.v8n4p23

Some Adaptations to Sea Level Rise in the Coastal City of Limbe, Cameroon

2016· article· en· W2557505635 on OpenAlexvenueno aff
Sunday Shende Kometa, Cornelius Mbifung Lambi, Tata Emmanuel Sunjo

Bibliographic record

VenueJournal of Geography and Geology · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsnot available
Fundersnot available
KeywordsSea level riseGeographySea levelIndigenousTourismClimate changeAdaptation (eye)Environmental planningOceanographyPhysical geographyEcology

Abstract

fetched live from OpenAlex

Abundant scientific evidence at our disposal clearly demonstrates that the world’s climates have been changing particularly since the advent of the Industrial Revolution. One of these evidences has been the rise in sea level. While inland cities might be confronted with other evidences and impacts of climate change, adapting to sea level rise remains a daunting task for most coastal cities especially those of developing countries. This paper therefore examines the extent of sea level rise in the Cameroonian coastal city of Limbe and the various indigenous adaptation strategies which are being put in place to combat this sea level rise. Using secondary data relating to sea level rise in tropical coastal areas and primary data relating to the various adaptation options to sea level rise, the study establishes that sea level rise will continue to be a problem to this location if adequate and lasting measures are not put in place. While the city has recorded successes especially in real estate development adaptations, other infrastructural facilities which largely support the tourism sector especially along the city’s coast line have remained unsustainable. In the wake of the growing sea level rise, perhaps, it is incumbent on the city’s authorities to have a holistic approach in the development and management of its coastal infrastructures in order to combat the sea level rise problem which has become a cruel reality in this active tectonic and mobile region of Cameroon.

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 score0.018
Threshold uncertainty score0.485

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 studies0.0000.000
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 head0.033
GPT teacher head0.231
Teacher spread0.198 · 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
Published2016
Admission routes1
Has abstractyes

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