Evaluation of repeated removal of mammalian predators on waterfowl nest success and density
Bibliographic record
Abstract
Low levels of nest success in the prairie pothole region are mainly attributed to changes in predator community and abundance. Removal of predators from large sites (≥ 4144 ha) has been an effective strategy for increasing nest success; however, trapping small sites (< 301 ha) is considered ineffective. I examined the effects of removing predators from 10, 259 ha sites in northeastern North Dakota during 2001-2002. Overall nest success for both years was greater on trapped sites (53.4%) than non-trapped sites (28.7%). Furthermore, daily survival rate was greater on removal sites, and was positively correlated with total predators removed. Differences in nest density were apparent between treatments with an increased nest density on trapped sites, however year had no effect. Pair densities did not differ between treatments, but a 2-fold increase for both trapped and non-trapped sites was found in spring 2002. Cost to produce one fledged duckling, combining all species ranged from $16-20. Overall, the results of this study indicate that repeated removal of predators on small sites is an effective strategy for increasing waterfowl production; however, feasibility will ultimately depend on the user group, budget limitations, landowner objectives, and public acceptance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".