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Record W4401276176 · doi:10.5376/ijmeb.2024.14.0009

Habitat Destruction and Biodiversity Loss Due to Sugarcane Expansion

2024· article· en· W4401276176 on OpenAlexvenueno aff
Kaiwei Liang, Jianquan Li

Bibliographic record

VenueInternational Journal of Molecular Evolution and Biodiversity · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsBiodiversityHabitatHabitat destructionAgroforestryEnvironmental scienceGeographyEcologyBiology

Abstract

fetched live from OpenAlex

This study explores the environmental and ecological impacts of sugarcane expansion in Brazil, India, and Australia. It highlights the significant effects of sugarcane cultivation on biodiversity, soil health, water resources, and local climates. In Brazil, the expansion into the Cerrado and Amazon regions has led to biodiversity losses, especially in soil fauna and altered hydrological cycles. In India, intensive cultivation practices have degraded local biodiversity and ecosystem services. In Australia, runoff from sugarcane fields threatens the Great Barrier Reef by causing algal blooms and coral bleaching. The study emphasizes the need for sustainable agriculture practices, robust environmental policies, community engagement, and international cooperation to mitigate the adverse effects of sugarcane cultivation. Recommendations include enforcing stricter environmental regulations, promoting sustainable farming practices, and investing in research for resilient crop varieties.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.007
GPT teacher head0.200
Teacher spread0.193 · 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 source (direct Gemma or distilled Codex), 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
Published2024
Admission routes1
Has abstractyes

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