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Record W4295917005 · doi:10.11159/ijepr.2022.003

Impact Of a Low-Income Informal Settlement in a Headwater Area at High-Risk Of Erosion in Brazil

2022· article· en· W4295917005 on OpenAlexvenueno aff
Maria Thereza Fonseca, Renan Lima, Samuel L. Silva, Hugo Luiz Martins de Paula, Jonatas Ferreira da Cruz, Arthur Antão, Juni Cordeiro, Luiz Alberto I. Saenz, Maria Rita Scotti

Bibliographic record

VenueInternational Journal of Environmental Pollution and Remediation · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil erosion and sediment transport
Canadian institutionsnot available
FundersUniversidade Federal de Minas Gerais
KeywordsSettlement (finance)ErosionLow incomeGeographySocioeconomicsWater resource managementEnvironmental scienceBusinessGeologyEconomicsGeomorphologyFinance

Abstract

fetched live from OpenAlex

The largest remaining area of preserved Atlantic Forest in the Belo Horizonte City (Minas Gerais, Brazil) protects many headwaters and watercourses. Among them, the Macacos stream is considered the most preserved with high water quality. However, since 2013 this region has suffered an intense and disorderly of informal settlement process by low-income communities, which resulted in a progressive loss of the original vegetation. Due to the lack of vegetation, this area became prone to erosional process as demonstrated by the topographic analysis. The Normalized Difference Vegetation Index (NDVI) demonstrated the great impact of the settlement process over the vegetation, ranging from 1 to 0. Besides, this eroded area has faced the loss of biodiversity as estimated by the richness and Shenon's index as well as the invasion by exotic species as Brachiaria sp and Typha domingensis. Also, the erosional process in this hilly site resulted in the accumulation of high level of sediments in the Macacos stream. Therefore, the restoration of headwaters and riparian sites aiming at soil stabilization and vegetation restoration is highly recommended.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score0.812

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.213
Teacher spread0.207 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Environmental Pollution and RemediationSame topicSoil erosion and sediment transportFrench-language works237,207