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Record W3121378046 · doi:10.24036/jmiap.v2i3.141

KENDALA PEMERINTAH KOTA PADANG DALAM MENANGGULANGI BENCANA ABRASI SEPANJANG KAWASAN PANTAI PURUS DI KOTA PADANG

2020· article· en· W3121378046 on OpenAlexaboutno aff
Desi Marlina, Zikri Alhadi

Bibliographic record

VenueJurnal Manajemen dan Ilmu Administrasi Publik (JMIAP) · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geophysical Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAbrasion (mechanical)Indonesian governmentGeographyGovernment (linguistics)IndonesianSocioeconomicsEngineeringSociology

Abstract

fetched live from OpenAlex

Indonesian is a country that has very many islands and has the longest coastline after Canada. Of the many islands located in the territory of Indonesia, not all of the beaches are well preserved, even many of the beaches have suffered damage caused by coastal abrasion, one of which is the purus beach in the city of Padang. Abrasion is an erosion that often occurs in coastal areas caused by ocean waves over a long period of time and can also be caused by human activity itself. The purpose of this research is to determine the obstacles faced by the Padang city government in overcoming abrasion disasters along the purus beach area in the city of Padang. The results show that there are several obstacles that are being faced by the Padang city government in overcoming the abrasion disaster, namely the funds owned by the Padang city government are insufficient in carrying out abrasion disasters and the equipment used is also very limited so that the Padang city government in dealing with abrasion disasters on the coast purus according to the existing equipment.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.026
GPT teacher head0.222
Teacher spread0.196 · 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.

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

Citations0
Published2020
Admission routes1
Has abstractyes

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