Identifying variable sea ice carbon contributions to the Arctic ecosystem: A case study using highly branched isoprenoid lipid biomarkers in Cumberland Sound ringed seals
Bibliographic record
Abstract
We analyzed liver samples from 322 ringed seals (Pusa hispida) collected from Cumberland Sound (southeast Baffin Island) to test our ability to differentiate between carbon sources in near apex predators. Highly branched isoprenoids (HBIs) were present in all samples, and their distributions were consistent with recognized seal habitat use. HBI distributions in mature seals (≥ 5 yr) confirmed a less variable carbon source during winter, consistent with geographically restricted sexually mediated territorialism. In contrast, HBI distributions were more variable for immature seals (< 5 yr old), consistent with increased movements and body growth—mediated habitat selection. The ubiquitous presence of sea ice—derived HBIs (e.g., the ‘Ice Proxy with 25 carbons’) in every seal collected throughout January—December indicates that springtime sea ice primary production remains important for ringed seals throughout the year. HBI distributions remain largely unaltered by trophic transfer, enabling them to document short‐term (< 4 weeks) and seasonal changes in carbon. This important characteristic of HBIs facilitated interpretation of sea ice—derived carbon use by seals over annual and interannual timeframes and identified strong associations between sea ice carbon use and insolation as well as sea ice extent. Analysis of HBI distributions could be used to monitor and predict the response of Arctic organisms to reducing sea ice extent and the associated decline in future sea ice primary production over a range of temporal scales.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".