INTERACTIVE EFFECTS OF VEGETATION AND PREDATORS ON THE SUCCESS OF NATURAL AND SIMULATED NESTS OF GRASSLAND SONGBIRDS
Why this work is in the frame
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Bibliographic record
Abstract
Abstract We examined the influence of vegetation and predator community on nesting success of songbirds in the grasslands of eastern North Dakota, USA. Each year, eight sites were chosen: four were subject to predator removal, and four were non-removal sites. On each site, nests of grassland songbirds were monitored, and simulated nests were used to examine how vegetation characteristics at nests affect nest success. Vegetative characteristics at simulated nests did not differ from those at natural nests, but successful natural nests had greater forb and lesser grass cover than unsuccessful nests, whereas no differences in vegetation were detected between successful and depredated simulated nests. On non-removal sites, small mammals and ground squirrels (Spermophilus sp.) depredated nests in taller and denser cover when compared to nests destroyed by medium-sized mammals and birds. On removal sites, we found no difference in vegetation characteristics of nests depredated by different predator types. However, each group of mammalian predators depredated simulated nests with different vegetation characteristics on removal versus non-removal sites. On sites where predators were removed, small mammals and ground squirrels preyed on simulated nests in shorter vegetation containing fewer forbs, ground squirrels preyed on nests with higher grass cover and lower vertical density, and medium-sized carnivores preyed on nests in taller vegetation. These results support the hypothesis that high predator diversity may reduce the chance of “safe” nest sites, and suggest that the behavior of low-level predators may change when top-level predators are removed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it