The Canadian Beaufort Shelf trophic structure: evaluating an ecosystem modelling approach by comparison with observed stable isotopic structure
Bibliographic record
Abstract
Climate-driven impacts on marine trophic pathways worldwide are compounded by sea-ice loss at northern latitudes. For the Arctic, current information describing food-web linkages is fragmented, and there is a need for tools that can describe overarching trophic structure despite limited species-specific data. Here, we tested the ability of a mass-balanced ecosystem model (Ecopath with Ecosim, EwE) to reconstruct the trophic hierarchy of 31 groups, from primary producers to polar bears, in the Canadian Beaufort Sea continental shelf. Trophic level (TL) estimates from EwE were compared with those derived from two nitrogen stable isotope (SI) modelling approaches (SI linear and scaled) to assess EwE accuracy, using a data set of 642 δ 15 N observations across 282 taxa. TLs from EwE were strongly, positively related to those from both SI models (R 2 > 0.80). EwE performed well (within 0.2 TL) for groups with relatively well-known diets or for taxa characterized by fewer trophic connections (e.g., primary consumers). Performance was worse (>0.5 TL) for species groups aggregated at coarse taxonomic levels, those with poorly documented diets, and for anadromous fishes. Comparisons with SI models suggested that the scaled approach can overestimate the TL of top predators if ecosystem-specific information is not considered.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".