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Record W4411878778 · doi:10.3390/land14071384

Dynamics and Anthropisation of Edible Caterpillar Habitats in the Landscape of the Luki Biosphere Reserve, Democratic Republic of the Congo

2025· article· en· W4411878778 on OpenAlexafffund
Ernestine Lonpi Tipi, Médard Mpanda Mukenza, Yannick Useni Sikuzani, Jean-Pierre Messina Ndzomo, Kouagou Raoul Sambiéni, François Malaisse, Joseph Lumande Kasali, Damase P. Khasa, Jan Bogaert

Bibliographic record

VenueLand · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaEuropean Commission
KeywordsBiosphereNature reserveDemocracyGeographyHabitatEcologyAgroforestryEnvironmental resource managementPolitical sciencePoliticsEnvironmental scienceBiologyArchaeology

Abstract

fetched live from OpenAlex

The Luki Biosphere Reserve landscape is located in the southwest of the Democratic Republic of Congo. Illicit anthropogenic activities in this landscape have contributed to the degradation of forest massifs, which are habitats for edible caterpillars. Accordingly, based on five Landsat images covering 2004–2024 period, we analysed the dynamics of edible caterpillar habitats in the Luki Biosphere Reserve, its periphery, and the landscape. The study was complemented by the calculation of class area, number of class patches, dominance, and the disturbance index. The results show that fragmentation and attrition have caused forest areas to decline by 46.13%, 21.17%, and 23.54% in the Reserve, its periphery, and at the landscape level, respectively. The dynamics of caterpillar habitats are reflected in the replacement of forest and fallow land by savannah. The level of disturbance has thus risen from 0.3 to 1.6 in the Reserve, from 2.5 to 13.9 in the periphery, and from 2.0 to 9.2 on a landscape scale. These results are mainly attributed to the expansion of agricultural land. Our observations imply an extent of disturbance in caterpillar habitats that might cause their scarcity, and strongly indicate the need for promoting effective strategies for preserving and restoring forest ecosystems in this landscape.

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 score0.051
Threshold uncertainty score0.965

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)0.0000.000

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 head0.006
GPT teacher head0.225
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

Citations0
Published2025
Admission routes2
Has abstractyes

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