Long-tailed Jaeger - GLS - Canadian Arctic-tracks
Bibliographic record
Abstract
Long-distance migratory seabirds need to adjust their migration strategy according to internal (breeding, molting) and external factors (seasonality, resource availability). Time-minimizing strategies are common during spring migration to arrive at the optimal time to breed. We studied the annual movements and migration strategy of the long-tailed jaeger Stercorarius longicaudus, a small arctic-nesting seabird. First, we documented year-round movements (routes, wintering sites) of male and female jaegers breeding in the Canadian Arctic. We then compared their migration strategies between seasons (phenology, stopover use, travel distance, speed) to determine whether they adopt a time-minimizing strategy in spring. Over 6 yr, we collected 43 tracks from geolocators deployed on Bylot and Igloolik Islands. Jaegers departed the breeding site over a 5 wk period and traveled on average 32375 km (round trip) before returning to breed, one of the longest documented migrations on Earth. Birds used a major stopover area east of the Grand Banks of Newfoundland in spring and fall, and wintered in high marine productivity areas of the South Atlantic. Unexpectedly, the spring migration was 40% longer and 32% slower than in fall, and birds increased their time spent on water (foraging and/or resting) by 61%. A time-minimizing strategy in fall may help to reach the wintering site rapidly and start molting early. In spring, a fly-and-forage strategy seems to be adopted to increase foraging effort, probably for the accumulation of body reserves before breeding and in anticipation of unfavorable conditions that may prevail at arrival on their arctic breeding site.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".