Detecting population heterogeneity in effects of North Atlantic Oscillations on seabird body condition: get into the rhythm
Bibliographic record
Abstract
Climatic influences on animal populations, mediated by changes in condition‐dependent survival or reproduction, have long intrigued ecologists. We analyzed links between winter North Atlantic Oscillations (NAO), a large scale climatic phenomenon affecting weather conditions over the North Atlantic and the Arctic, and average pre‐laying body mass in common eiders. Body mass is a good proxy for condition‐dependent reproductive output in this species. Time series links were assessed for two eider populations breeding at high latitudes, over a 10‐ and a 21‐year time series. Winter NAO affected body mass in both populations and these effects were easier to detect when changes in the series rhythm were assessed using a novel method based on data discretization and information theory, rather than detection based on changes in amplitude, assessed using traditional linear models. Winter conditions affected body condition of eiders in both populations. Different mechanisms, however, are likely to be involved in the two populations, one being presumably affected by direct effects of climate and the other by effects through the food chain. Therefore, the same species can respond along different pathways to the same large scale climatic pattern, an important consideration when seeking to understand or manage the response of species to present and future climate change.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".