The effectiveness and cost efficiency of different predator exclosure designs to increase piping plover ( <scp> <i>Charadrius melodus</i> </scp> ) nest success and fledging rate in Alberta, Canada
Bibliographic record
Abstract
Abstract An estimated 30% of the North American piping plover ( Charadrius melodus) breeding population occurs in Canada, where it is Endangered . Predator exclosures are a common management tool across the species' range to increase nest success. Using data from 1998 to 2010 on 820 nests, collected as part of an ongoing management program in Alberta, we compare daily nest survival (DNS), numbers of chicks hatched and fledglings produced, and cost/chick among three treatments (large, medium, small exclosures) and natural nests. During the early period (1998–2001), when all three types of exclosures were applied, there were no significant differences in DNS between exclosed nests and natural nests. During the late period (2002–2010), when only small exclosures were applied, nests with small exclosures had a significantly higher DNS rate ( = 0.994, n = 594) compared with natural nests ( = 0.984, n = 88). Nests with small exclosures also hatched more chicks ( = 3.21, n = 598) and produced more fledglings ( = 1.17, n = 337) than natural nests ( = 1.73 chicks, n = 31; = 0.59 fledglings, n = 21) during the late period. However, considering only successful nests, the differences were not significant for either period, indicating no added benefit of exclosures beyond protecting the nest. The cost/chick rate was lowest using small exclosures (cylindrical, 40 by 60 cm), and this portable design is well‐suited for widespread application in the field. We demonstrate increased DNS for nests with small exclosures and a mean fledging rate close to the goal of 1.25 chicks/nest/year, although this did not increase Alberta's piping plover population beyond the timeframe of systematic exclosure application. Long‐term, cost‐efficient management tools that increase nest success and fledging rates should continue.
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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 | 0.001 | 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".