Factors Influencing Utilization of Artificial Nesting Cylinders by Mallards and Wood Ducks in Northwest Pennsylvania and Southern Ontario
Bibliographic record
Abstract
Factors influencing utilization of elevated nesting structures by waterfowl were examined in southern Ontario and northwest Pennsylvania, 2006-2008. In the final-year, Mallard occupancy rates were 18 % in Pennsylvania and 16 % in Ontario. Mean nest success was 77 ± 20 % for combined sites (2006-2008). Final-year Wood Duck occupancy rates were 12 % in Pennsylvania and 2 % in Ontario; mean nest success was 70 ± 29 %. Mallards tended to select structures in areas with high wetland densities and adjacent to grasslands or hayfields. In Pennsylvania, Wood Ducks had similar preferences for structures as did Mallards. In Ontario, Wood Ducks were more likely to use structures with a high proportion of adjacent forest cover and a high abundance of invertebrates. Cost per duckling fledged was $20.00 with a paid technician and $5.24 if structures are maintained by volunteers. Relative to other management strategies, artificial nesting cylinders may be cost effective for increasing Mallard populations in Ontario and Pennsylvania.
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 | 0.000 | 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 | 0.000 |
| 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".