Population fluctuations and distribution of staging Eared Grebes (<i>Podiceps</i> <i>nigricollis</i>) in North America
Bibliographic record
Abstract
Eared Grebes (Podiceps nigricollis Brehm, 1831) use saline ecosystems throughout much of their life cycle, and greater than 90% of the North American population stage during fall at two hypersaline lakes. At one of these lakes, Great Salt Lake (GSL), Utah, a commercial harvest of brine shrimp (Artemia franciscana Kellogg, 1906) cysts occurs during fall and may impact Eared Grebe populations. We used photo surveys on the other hypersaline lake, Mono Lake, California, and on the GSL, as well as aerial counts on the GSL, to describe population fluctuations of Eared Grebes staging on these lakes. The long-term (1997–2012) Eared Grebe population was 1.4 million on the GSL and 1.0 million on Mono Lake. Populations changed on GSL and Mono Lake in synchrony, indicating population regulation is likely occurring at wintering, not staging, areas and is influenced by El Niño effects. Location of Eared Grebes on the GSL was influenced by brine shrimp densities and did not overlap with concentrations of commercial harvest boats. Spatial segregation of commercial harvesters and Eared Grebes reduces negative impacts of anthropogenic disturbance on Eared Grebes. Knowledge of population changes within and among staging areas will help managers monitor long-term abundances and reduce negative impacts between Eared Grebes and commercial harvesters.
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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 | 0.000 |
| 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) | 0.001 | 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".