MétaCan
Menu
Back to cohort
Record W4321490588 · doi:10.1111/aje.13133

The effects of the invasive species, <i>Lantana camara</i>, on regeneration of an African rainforest

2023· article· en· W4321490588 on OpenAlexfundno aff
Anke Barahukwa, Colin A. Chapman, Mary Namaganda, Gerald Eilu, Patrick A. Omeja, Michael J. Lawes

Bibliographic record

VenueAfrican Journal of Ecology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsLantana camaraLantanaShrubInvasive speciesBiologyClearanceVerbenaceaeSpecies diversityForest restorationEcologyAgroforestryForest ecologyEcosystem

Abstract

fetched live from OpenAlex

Abstract Invasive plants adversely affect native communities by altering ecosystem function and disrupting natural regeneration. We investigate the effect of invasive Lantana camara L. (Verbenaceae) on forest regeneration in Kibale National Park, Uganda. We appraise the efficacy of cutting and uprooting Lantana for promoting native tree recruitment. Sample plots comprised three types: (i) currently invaded by Lantana ; (ii) cleared of Lantana and now managed; and (iii) forest reference plots uninvaded by Lantana . Tree species numbering 51, 19 shrubs, and 17 herb species were identified. Lantana reduced tree, shrub, and herb cover and diversity, and suppressed tree regeneration. The short‐term management of Lantana did not promote tree establishment. The tree community in cleared areas was not converging on uninvaded adjacent forest. Lantana is known to allelopathically suppress tree seedling establishment, but even at sites cleared of Lantana , tree species recruitment was poor. While insufficient time may have passed for tree recruitment, we argue that an increase in shrub and herb cover and diversity arrested forest tree regeneration. Sustained follow‐up clearing of dense secondary shrubs and herbs and resprouted L. camara in cleared areas is key to ensuring long‐term recovery of the forest tree community.

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)0.001
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 score0.117
Threshold uncertainty score0.281

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.007
GPT teacher head0.206
Teacher spread0.199 · 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

Citations16
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueAfrican Journal of EcologySame topicEcology and Vegetation Dynamics StudiesFrench-language works237,207