MétaCan
Menu
Back to cohort
Record W4378760133 · doi:10.18280/ijsdp.180530

Environmentally Friendly Post-Mining Land Reclamation Policy for Manganese in Sabu Raijua, East Nusa Tenggara, Indonesia

2023· article· en· W4378760133 on OpenAlexvenueno aff
Iga Gangga Santi Dewi, Yuli Prasetyo Adhi, Agung Basuki Prasetyo, Made Wiryani

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2023
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsLand reclamationEnvironmentally friendlyBusinessManganeseEnvironmental scienceEnvironmental protectionGeographyChemistryEcology

Abstract

fetched live from OpenAlex

Every mining business that is carried out will greatly affect the environment, either directly or gradually.Changes in natural ecology are also determined by human attitudes and protection of the environment.Restoration in accordance with the initial conditions is critical in mining industry to balance of the ecosystem, ecology, and avoid damage to environment.This study aims to investigate the post-mining reclamation of manganese in Sabu Raijua Regency, East Nusa Tenggara, Indonesia.The method used is empirical with some supporting data from respective agencies used as a basis for investigation.The results showed that the important position of Sabu Raijua in manganese production, and it has among the best quality of manganese in the world.However, as to avoid the detrimental effect of mining activities to cause environmental damage, strict supervision and the involvement of local institutions in environmentally friendly reclamation are highly needed.The findings underlined the core principle of reclamation of manganese mining with sustainability principle.

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 score0.303
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.009
GPT teacher head0.228
Teacher spread0.219 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Sustainable Development and PlanningSame topicMining and Resource ManagementFrench-language works237,207