The effects of the invasive species, <i>Lantana camara</i>, on regeneration of an African rainforest
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".