American White Pelicans and Recreational Boaters on Lakes of the North American Great Plains: Habitat Use Overlap
Bibliographic record
Abstract
Shoreline development and boating on lakes of the northern Great Plains of North America have increased due to recent economic prosperity. Few studies have examined the general characteristics of habitats used by foraging waterbirds and boats to determine levels of overlap. To address this issue, we conducted point count surveys of American White Pelicans (Pelecanus erythrorhynchos) and boats on two important recreational lakes in southern Saskatchewan, Canada. The majority of pelicans and boats detected used near-shore areas of the lakes, identifying the importance of shallow water habitats and providing evidence of significant overlap. The location of pelicans relative to the shore did not change in the presence of boats, and there was no significant relationship between boat numbers and pelican numbers. These analyses suggest that pelicans did not make major changes to their habitat use on the lakes as a result of boating activity. When pelicans and boats were present simultaneously at point count locations, pelicans appeared to avoid boats on one lake, but showed no detectable avoidance behavior on the other lake. The importance of interactions between recreational boating and foraging pelicans is currently unclear. Set-back distances to protect foraging pelicans from boating activity do not appear necessary based on our analyses.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".