Water-Level Fluctuations and Ungulate Community Dynamics in Central Uganda
Bibliographic record
Abstract
Climate change has altered precipitation regimes with concomitant influences in hydrology. For a complex assemblage of ungulates, these water-level fluctuations might alter habitat partitioning thought to be crucial for coexistence in response to livestock introduction. We placed camera traps in three habitat types along the Mayanja River in central Uganda to evaluate space use by native and domestic ungulates. For each species, we assessed the difference in the proportion of days with camera-trap detections during three water-level conditions (low in 2017, normal in 2015, and high in 2016). Sitatunga was the only species regularly using wetlands; their use of remote wetlands remained consistent regardless of water-level conditions, and their use of forest habitats decreased during the study. In the forest, warthogs showed no change in use, while proportion of days with detections increased over time for all other ungulates. Our results indicate that ungulate community space use appears to be independent of hydrologic condition, and that risk for competitive exclusion between native and domestic ungulates is tempered by dietary and habitat use differences. Adaptations to dynamic hydrology appears to buffer consequences for ungulate communities; more serious are habitat losses to agriculture and development.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".