Effects of Plant Cover Improvements for Nesting Ducks on Grassland Songbirds
Bibliographic record
Abstract
Several islands located along the St. Lawrence River in southern Quebec have been used as natural pastureland by cattle for decades. Recently, a rest-rotation grazing system and dense nesting cover were established on four islands near Varennes to improve duck nesting conditions. The effects of these two plant cover improvements on the abundance of grassland songbirds were assessed through four treatments: (1) idle fields with no vegetation improvement but exclusion of cattle (IDLE), (2) improved pastures with seeding of forage plants for cattle (IMPP), (3) dense seeded nesting cover fields improved for ducks and where cattle were excluded (DNC), and (4) natural or unimproved pastures grazed by cattle after the duck nesting season (UIPP). The overall abundance of birds was similar among treatments before cover improvements as well as two years after. The abundance of Bobolinks (Dolichonyx oryzivorus) was significantly greater in DNC and UIPP two years after treatments while Red-winged Blackbirds (Agelaius phoeniceus) were more abundant in DNC and IDLE. Plant cover improvements had little impact on Savannah Sparrow (Passerculus sandwichensis) abundance. Furthermore, few annual or treatment-related changes were observed for less abundant species. On the short-term, duck nesting cover improvements in natural pastures did not have any major effect on grassland songbirds on Varennes islands.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".