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IMPACT OF ABRATION ON LAND USE IN COASTAL AREA BULELENG REGENCY

2022· article· en· W4280637242 on OpenAlexaff
Luh Putu Gita Ari Parwati, Ngakan Ketut Acwin Dwijendra, Ni Ketut Agusintadewi

Bibliographic record

VenueASTONJADRO · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal Management and Development
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsCoastal erosionMangroveGeographyAbrasion (mechanical)Environmental scienceLand useVulnerability (computing)Environmental resource managementErosionWater resource managementAgroforestryFisheryCivil engineeringGeologyEngineering

Abstract

fetched live from OpenAlex

Coastal damage due to abrasion and wave erosion in Buleleng Regency is very potential, even information from the Buleleng Regency Marine Service released information that the length of the coast in Buleleng Regency which has been damaged is not less than 5 km. Abrasion and wave erosion resulting in erosion of the coastline and will continue to affect land use. In this study using quantitative methods with analysis stages consisting of, among others, land use and analysis of the impact of abrasion on land use. The analysis carried out is a Geographic Information System (GIS) based analysis in accordance with the needs of the research in spatial terms. The conclusions obtained from this study are the types of land use that exist in the Coastal District of Buleleng Regency are in the form of airports, lakes / reservoirs, forests, mangroves, stretches of beach sand, ports, plantations / gardens, paddy fields, rivers, moor and shrubs. The abrasion that occurs in the Coastal Zone of Buleleng Regency every year with vulnerability during 2013 - 2019 has an impact on land use changes. This can be seen through the results of overlapping overlay analysis and seeing the area of land use that has changed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.077
Threshold uncertainty score0.153

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.231
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2022
Admission routes1
Has abstractyes

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Same venueASTONJADROSame topicCoastal Management and DevelopmentFrench-language works237,207