Non-parametric analysis of the spatio-temporal variability in the fatty-acid profiles among Greenland sharks
Bibliographic record
Abstract
Shifting prey distributions due to global warming are expected to generate dramatic ecosystem-wide changes in trophic structure within Arctic marine ecosystems. Yet a relatively poor understanding of contemporary Arctic food webs makes it difficult to predict the consequences of such changes for Arctic predators. Doing so requires quantitative approaches that can track contemporary changes in predator diets through time, using accurate, well-defined methods. Here we use fatty acids (FA) to quantify differences in consumer diet using permutational multivariate analysis of variance tests that characterize spatial and temporal changes in consumer FA signatures. Specifically we explore differences in Greenland shark (Somniosus microcephalus) FA to differentiate their potential trophic role between Svalbard, Norway and Cumberland Sound, Canada. Greenland shark FA signatures revealed significant inter-annual differences, probably driven by varying seal and Greenland halibut responses to environmental conditions such as the NAO, bottom temperature, and annual sea-ice extent. Uncommon FA were also found to play an important role in driving spatial and temporal differences in Greenland shark FA profiles. Our statistical approach should facilitate quantification of changing consumer diets across a range of marine ecosystems.
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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 | 0.002 |
| 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".