Nesting Ecology of White-Winged Scoters (Melanitta Fusca Deglandi) at Redberry Lake, Saskatchewan
Bibliographic record
Abstract
Abstract Population surveys indicate a trend of declining abundance of scoters (Melanitta spp.) in North America. Little is known about changes in life-history traits that may be responsible for the recent population decline of White-winged Scoters (Melanitta fusca deglandi). Therefore, we studied nesting ecology of White-winged Scoters at Redberry Lake, Saskatchewan, during the summers of 2000–2001. We found 198 nests and examined nest-site selection by comparing habitat features of successful nests, depredated nests, and random sites. Discriminant function analysis differentiated habitat features—measured at hatch—of successful nests, depredated nests, and random sites; lateral (r = 0.66) and overhead (r = 0.35) concealment were microhabitat variables most correlated with canonical discriminant functions. We also modeled daily survival rate of nests as a function of year, linear and quadratic trends with nest age, nest initiation date, and seven microhabitat variables. Nest survival from a time-constant model (i.e. Mayfield nest-success estimate) was 0.35 (95% CL: 0.27, 0.43). Estimates of nest success were lower than those measured at Redberry Lake in the 1970s and 1980s. Nest survival increased throughout the laying period and stabilized during incubation, and showed positive relationships with nest concealment and distance to water and a negative relationship with distance to edge. Considering those factors, a model-averaged estimate of nest survival was 0.24 (95% CL: 0.09, 0.42). We concluded that White-winged Scoters selected nesting habitat adaptively, because (1) successful sites were more concealed than depredated sites; (2) nest sites (both successful and depredated) had higher concealment than random sites; and (3) nest sites were on islands, where success is greater than on the mainland.
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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 | 0.001 | 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".