Competition between European hare and European rabbit in a lowland area, Hungary: a long-term ecological study in the period of rabbit extinction
Bibliographic record
Abstract
A b s t r a c t . Abundance of the European hare (Lepus europaeus Pallas, 1778) has been declining dramatically in Europe. In the framework of our long-term ecological studies in the juniper forest at Bugac, Hungary, we have also monitored its population abundance. At the beginning of our researches the European rabbit (Oryctolagus cuniculus Linne, 1758) had been the dominant herbivore species there, but as a result of two diseases in 1994 and 1995 they disappeared. Earlier studies had showed competition between these two species, therefore we expected a significant increase in the local hare abundance after the extinction of rabbits. Our results, however, did not comply with this supposition. Nonetheless, experimental comparison of the vegetation in grazed and ungrazed plots proved that rabbits had been significantly decreasing the vegetation cover, especially that of grasses; meanwhile hares did not. Although grasses were the main food components of both species, their moderate diet overlap throughout the year does not suggest a food competition between them. All these findings show that population size of hares was not significantly limited by rabbits due to trophic overlap. Competitive effect of rabbit on sympatric hares had been low or it was expressed by the depreciation of other non-investigated population characteristics.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".