Structure and function of the western Baffin Bay coastal and shelf ecosystem
Bibliographic record
Abstract
Arctic marine species, from benthos to fish and mammals, are essential for food security and sovereignty of Inuit people. Inuit food security is dependent on the availability, accessibility, quality, and sustainability of country food resources. However, climate change effects are threatening Inuit food systems through changes in abundance and nutritional quality of locally harvested species, while foundational knowledge of Arctic food webs remains elusive. Here, we summarized scientific knowledge available for the western Baffin Bay coastal and shelf ecosystem by building a food web model using the Ecopath with Ecosim modeling framework. Based on this model, we calculated ecological network analysis indices to describe structure and function of the system. We used Linear Inverse Modeling and Monte Carlo analysis to assess parameter uncertainty, generating plausible parameterizations of this ecosystem from which a probability density distribution for each index was generated. Our findings suggest that the system is controlled by intermediate trophic levels, highlighting the key role of Arctic cod (Boreogadus saida) as prey fish, as well as the importance of other less studied groups like cephalopods in controlling energy flows. Most of the ecosystem biomass is retained in the system, with very little lost to subsistence harvest and commercial fisheries, indicating that these activities were within a sustainable range during the modeling period. Our model also highlights the scientific knowledge gaps that still exist (e.g., species abundances), including valued harvest species like Arctic char (Salvelinus alpinus), walrus (Odobenus rosmarus), and seals, and importantly our poor understanding of the system in winter. Moving forward, we will collaborate with Inuit partners in Qikiqtarjuaq, NU, Canada, to improve this modeling tool by including Inuit knowledge. This tool thus serves as a starting point for collaborative discussions with Inuit partners and how its use can better inform local and regional decision-making regarding food security.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".