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ANALISIS PERENCANAAN PEMETAAN KAWASAN DAN PENERAPAN KEPEMILIKAN IJIN PENAMBANGAN PASIR YANG BERLEBIHAN DAN ILEGAL

2025· article· en· W6967121007 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2025
Typearticle
Languageen
FieldComputer Science
TopicComputational Physics and Python Applications
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsThematic mapWork (physics)Thematic analysisService (business)Data sourcePopulation

Abstract

fetched live from OpenAlex

The purpose of this study was to determine the planning of areas and the implementation of excessive and illegal sand mining permit ownership. This type of research is qualitative. Primary and secondary data were used as the data sources. The primary data were based on interview results. Secondary data uses figures and data, books and scientific journals which are used as the basis for scientific literature. The data analysis technique uses thematic analysis. Primary data are obtained from interviews with the Environmental Service and Mining Actors or workers in the Humbang Hasundutan Regency. The results of the study showed that sand mining in Purba Baringin Village, Pakkat District, and Humbang Hasundutan Regency was still found in many who did not have permits, and many met the need for sand from outside the district itself.

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.

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 categoriesMeta-epidemiology (narrow), Scholarly communication, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0040.003
Open science0.0070.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.157
GPT teacher head0.518
Teacher spread0.360 · 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