Temporal niche partitioning between Korean water deer and Siberian roe deer in temperate forests
Bibliographic record
Abstract
We conducted a study to clarify the daily activity patterns of Korean water deer (Hydropotes inermis (Swinhoe, 1870)) and Siberian roe deer (Capreolus pygargus (Pallas, 1771)) to assess the potential temporal niche partitioning between these two sympatric species. We hypothesized that the two species would show different diel activity patterns in the temperate forest. Camera traps were employed in Japanese larch plantations (Larix kaempferi (Lamb.) Carrière) and natural deciduous forests on Mt. Gariwang, Pyeongchang, Gangwon Province, South Korea, during the summer of 2023. Kernel density estimation and overlap coefficients were analyzed using video data. The relative abundance index of the Korean water deer was 3.40 in plantations and 14.13 in deciduous forests, while that of the Siberian roe deer was 17.93 in plantations and 35.87 in deciduous forests. The overlap coefficients between the two species were 0.72 in both plantations and deciduous forests, suggesting temporal niche partitioning. Korean water deer exhibited nocturnal activity in plantations but showed no distinct pattern in deciduous forests; however, Siberian roe deer consistently displayed a crepuscular pattern in both habitats. We provide insights into the ecological interactions between these cervid species and highlights the need for further research on seasonal variations in their activity patterns.
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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 | 0.000 |
| 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".