Constructed Borrow-Pit Wetlands as Habitat for Aquatic Birds in the Peace Parkland, Canada
Bibliographic record
Abstract
The Peace Parkland, Alberta, Canada is part of a continentally important region for breeding and migrating aquatic birds. As a result of resource development and agricultural conversion, many wetlands have been lost. Road construction in the area results in the creation of borrow pits, <3 ha ponds created when soil is removed to form the road bed. We surveyed 200 borrow pits for aquatic birds in May through August 2007. We examined patterns of occurrence and richness, categorizing ponds based on surrounding landscape type: agriculture (0–33.3% forest within 500 m), mixed habitat (33.4–66.6% forest), and forested (66.7–100% forest). Principal Component Analysis indicated that pond environments differed based on local and landscape features. Twenty-seven species of aquatic birds used borrow pits, with 13 nesting. Nonmetric Multidimensional Scaling and Indicator Species Analysis of birds observed in each month revealed assemblages characteristic of agricultural ponds, including horned grebe, lesser scaup, American coot, and mallard, and of ponds with >33.3% forest, including bufflehead, ring-necked duck, green-winged teal, and American wigeon. Because borrow pits were used by a variety of dabbling and diving aquatic birds in repeatable assemblages across the breeding season, we propose that these wetlands be integrated into avian conservation strategies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".