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Record W2812993029 · doi:10.5539/jas.v10n8p426

Impact Assessment From Coal Mining Area in Southern Brazil

2018· article· en· W2812993029 on OpenAlexvenueno aff
Monique Bohora Schlickmann, Jaqueline Beatriz Brixner Dreyer, Fabio R. Spiazzi, Francielle S. Vieira, Bruno Tayar Marinho do Nascimento, Edilane Rocha Nicoleite, Maria Raquel Kanieski, Edilaine Duarte, Chaiane Rodrigues Schneider, Jéssica Thalheimer de Aguiar

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsnot available
FundersUniversidade do Estado de Santa CatarinaCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsCoal miningCoalEnvironmental scienceEnvironmental impact assessmentPollutionExcavationMining engineeringStructural basinCoal basinEnvironmental planningWater resource managementEnvironmental resource managementGeographyGeologyArchaeologyLawEcology

Abstract

fetched live from OpenAlex

The mining activity is highly environmental impacting, being the excavation process and waste sterile rejects crowding the main pollution sources. The Santa Catarina Coal Basin has great importance, considering that contains 4.3 billions tons of coal (13% of the Brazilian reserve) and 6.400 hectares of degraded area. That way, the study objective was to characterize and identify the ambient impacts derivated from coal mining activities, at the Sideropolis city, SC. The base method used to evaluate was The Leopold matrix which adaptations were made according to a qualitative attribute weighting matrix to verify the significance of impacts. The interaction between two actions of the enterprise and 11 generated environmental impacts was analysed according to the attributes of frequency, extension, duration, direction and degree, both in the physical and anthropic environments. All impacts had a negative direction, where the most striking activity was the opening of the cava, which, when forming the acidic lagoon, changes the surface water quality, being described as of great importance. Therefore, recognizing the principal environment problems could help on strategies to accomplish the recovery requirements of degraded areas on this area.

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 score0.174
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.013
GPT teacher head0.301
Teacher spread0.288 · 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

Citations10
Published2018
Admission routes1
Has abstractyes

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