Waterfowl and Wetland Birds
Bibliographic record
Abstract
Abstract The future of wetland bird habitat and populations is intrinsically connected with the conservation of rangelands in North America. Many rangeland watersheds are source drainage for some of the highest functioning extant wetlands. The Central and Pacific Flyways have significant overlap with available rangelands in western North America. Within these flyways, the importance of rangeland management has become increasingly recognized by those involved in wetland bird conservation. Within the array of wetland bird species, seasonal habitat needs are highly variable. During the breeding period, nest survival is one of the most important drivers of population growth for many wetland bird species and rangelands often provide quality nesting cover. Throughout spring and fall, rangeland wetlands provide key forage resources that support energetic demands needed for migration. In some areas, stock ponds developed for livestock water provide migration stopover and wintering habitat, especially in times of water scarcity. In the Intermountain West, drought combined with water demands from agriculture and human population growth are likely headed to an ecological tipping point for wetland birds and their habitat in the region. In the Prairie Pothole Region, conversion of rangeland and draining of wetlands for increased crop production remains a significant conservation issue for wetland birds and other wildlife. In landscapes dominated by agricultural production, rangelands provide some of the highest value ecosystem services, including water quality and wetland function. Recent research has shown livestock grazing, if managed properly, is compatible and at times beneficial to wetland bird habitat needs. Either directly, or indirectly, wetland bird populations and their habitat needs are supported by healthy rangelands. In the future, rangeland and wetland bird managers will benefit from increased collaboration to aid in meeting ultimate conservation objectives.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".