Influence of fire, topography, and consumer abundance on seed predation in tallgrass prairie
Bibliographic record
Abstract
We assessed seed predation by vertebrates and invertebrates in three fire-frequency treatments (<1 year, 1–4 years, and >4 years since fire) and in three topographic positions (upland, limestone breaks, and lowland) in tallgrass prairie. Two types of seed trays, one for vertebrates and one for invertebrates, were placed in each treatment during each nocturnal and diurnal period. Vertebrates removed significantly more seeds than did invertebrates. Fire frequency and topographic position affected seed removal by both vertebrates and invertebrates. Seed removal by invertebrates was influenced negatively by fire; the greatest seed removal occurred in uplands and lowlands in unburned prairie. Vertebrates removed the most seeds in burned prairie and in lowlands and limestone breaks. Time of day also influenced seed removal by vertebrates, as nocturnal vertebrates (assumed to be rodents) removed more seeds than diurnal vertebrates. Abundance of rodents, however, did not predict accurately seed removal in fire treatments or topographic positions, as rodents removed fewer seeds than expected in prairie that had not been burned in >4 years and in lowlands. This pattern likely was due to the presence of a well-developed plant litter layer in both unburned and lowland habitats, which reduces the likelihood of a rodent locating seeds.
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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 | 0.000 | 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".