Breeding pair and reproductive estimates of a recently expanded Red-necked Grebe (<i>Podiceps grisegena</i>) population in parkland Manitoba
Bibliographic record
Abstract
Conservation of wildlife populations requires reliable information on population size, trends, and demographic processes.Such information is sparse for Red-necked Grebe (Podiceps grisegena), a species that is vulnerable to changing wetland conditions in the prairie pothole region. During 2008–2019, I collected breeding pair and reproductive estimates of a recentlyexpanded Red-necked Grebe population on 109 semi-permanent and permanent wetlands (mean ± SE: 2.92 ± 0.41 ha, range 0.01–24.2) in agriculturally-dominated habitat in southwestern Manitoba, Canada, to determine population status and reproductive success. I also looked for effects of changing wetland water levels and the presence of conspecifics and/or wetland size on productivity. Red-necked Grebe breeding densities were the highest reported for solitary-nesting pairs in North America and the breeding population currently appears to be stable. I found that chicks/breeding pair are mostly lower but chicks/successful pair are similar or greater than values reported from other studies. Pairs breeding with conspecifics appeared to be as productive as those on single-pair wetlands. Productivity was positively associated with wetland water levels suggesting that prolonged drought or climate change leading to warmer, drier summers on the prairies could reduce Rednecked Grebe breeding populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".