Migration patterns and wintering distribution of common loons breeding in the Upper Midwest
Bibliographic record
Abstract
Identification of geographic linkages among breeding, migratory and wintering common loon Gavia immer populations is needed to inform regional and national conservation planning efforts and compensation of loons lost during marine oil spill events. Satellite telemetry and archival geolocator tags were used to determine the migration patterns and wintering locations of breeding adult and young of the year juvenile common loons captured and marked on lakes in Minnesota, Wisconsin and Michigan. Adult loons typically traveled from breeding lakes, often via larger staging lakes, to the Great Lakes (primarily Lake Michigan) and then on to wintering areas. Most radiomarked juvenile common loons utilized natal lakes or local lakes through mid‐November. Subsequently, the first fall migration of juvenile loons was generally initiated later, and more direct and quicker to wintering areas relative to adults. Among adult (n = 103) and juvenile (n = 23) loons that completed fall migration, most wintered in the Gulf of Mexico (GOM), with smaller proportions wintering off the southern Atlantic Coast or impoundments in the southeastern United States. Spring migration of adults to breeding lakes was less prolonged than fall migration, with adult male loons tending to depart wintering areas earlier than adult females. Juvenile common loons migrated during their first spring from wintering sites in the GOM to summer in the Gulf of St Lawrence/Nova Scotia Coastal region. Juvenile mortality was largely linked to parasitic infection and emaciation; spring appeared to be a survival bottleneck among juvenile loons monitored in our study. Our results identify several areas where common loon conservation efforts could be directed to protect key habitats and minimize stressors during the non‐breeding period.
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 | 0.000 |
| 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".