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Record W4378631772

Visual impact produced by mining activity in the Punta Gorda ore body, Moa

2016· article· en· W4378631772 on OpenAlexaff
Idania Aguilera-Fernández, Yordanis E. Batista-Legrá, Subash Bastola, Luis Rojas-Purón

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2016
Typearticle
Languageen
FieldEnergy
TopicEnvironmental and Ecological Studies
Canadian institutionsCentre for Excellence in Mining Innovation
Fundersnot available
KeywordsMining engineeringGeographyGeology
DOInot available

Abstract

fetched live from OpenAlex

Open-pit mining has adverse impacts on the landscape of the mining area. This article describes the visual impact resulting from surface mining activities being conducted in the Punta Gorda laterite ore body located in Moa. The implementation of the indirect method Bureau of Land Management by means of computer tools, such as Surfer 8.0, Didger 3.02, Gemcom 4.11, Autocad Civil 3d allowed the determination of visual landscape units and the main visual basins according to the method of selection used. This investigation also includes an analysis on the visual basin soil, color, texture and luminosity. Observation locations were selected based on their topographical characteristics or because of their being located in the highest altitudes. It was concluded that the visual impact of mining activities on the landscape investigated covers more than 50% of the visual basin. This research paves the way to a new field in the visual impact assessment of open-pit mining in Cuba. It constitutes a practical contribution providing information of interest on landscapes for the decision making associated with ore body management and planning and it stands out for being useful to ensure a successful mining and environmental planning of a region.

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 categoriesInsufficient 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 score0.319
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.000

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 head0.193
GPT teacher head0.523
Teacher spread0.330 · 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
Published2016
Admission routes1
Has abstractyes

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