Effects of biosolids amendments on American Kestrel (<i>Falco sparverius</i>) nest-site selection and diet
Bibliographic record
Abstract
Fertilization is a common practice, as it improves plant yields. In many areas, biosolids (remains from treated wastewater) are used instead of chemical fertilizers owing to multi-year nutrient release and positive changes in soils and water retention. Little is known about whether biosolids affect animal consumers. Here, we examine diets and nesting sites of American Kestrels (Falco sparverius Linnaeus, 1758) in British Columbia, Canada. Our major study area was a large cattle ranch that has been grazed for over a century and where biosolids were applied in the previous 1–3 years. Kestrels selected nests in areas with higher Northern Flicker (Colaptes auratus (Linnaeus, 1758)) nest density and with a higher proportion of pastures with biosolids applications, but with less forest. Kestrels were observed more often on biosolids-amended pastures than in untreated areas. Kestrels primarily ate voles (genus Microtus Schrank, 1798), but also consumed grasshoppers (Orthoptera) late in the breeding season when grasshoppers were abundant. Kestrel diets were slightly more varied on a nearby control area than on the ranch. Our results suggest that biosolids applications improve prey availability for Kestrels, thus potentially acting as a restoration tool in areas with degraded habitats or where Kestrels have declined.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".