Dynamics and Anthropisation of Edible Caterpillar Habitats in the Landscape of the Luki Biosphere Reserve, Democratic Republic of the Congo
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".