Short visits reveal consistent patterns of interyear and intercolony variation in seabird nestling diet and performance
Bibliographic record
Abstract
To investigate the utility of short visits to seabird colonies to gauge nestling growth performance and diet, in 4 consecutive years (19951998) we measured nestling development and diet of Rhinoceros Auklets (Cerorhinca monocerata) at three colonies in British Columbia: Seabird Rocks, Triangle Island, and SGaang Gwaii (Anthony Island). In all years, nestlings of a given wing length on Triangle Island were significantly smaller than those on Seabird Rocks or SGaang Gwaii. Meals were also significantly lighter on Triangle Island than on Seabird Rocks or SGaang Gwaii; meals on the latter two islands were indistinguishable in mass. Retarded nestling development and small meal size on Triangle Island likely reflect the large contribution of rockfish (Sebastes spp.) to nestling diet on that colony alone. The intercolony differences in nestling performance appear to reflect the strong influence of local fish prey populations. Nonetheless, a united decline in performance in 1996 and subsequent increase on all colonies suggest the influence of large-scale ocean climate phenomena on our entire study region and beyond. We did not, however, observe noticeable effects of the 19971998 El Niño Southern Oscillation event on nestling growth performance or diet. We discuss the utility of our short-visit techniques for seabird-monitoring programs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".