Using Environmental Variables to Predict the Structure of Deep-sea Arctic Fish Communities: Implications for Food Web Construction
Bibliographic record
Abstract
Understanding trophic interaction within deep-sea marine communities is critically important as commercial exploitation increases in the Arctic. Undoubtedly, the Arctic marine environment will be subject to the same environmental and human stressors as its southern counterparts and, as a result, community stability is vulnerable. The objectives of this study were to test the predictive power of environmental variables on fish community composition using redundancy analysis and to determine if this information could be used to differentiate distinct trophic communities throughout the Davis Strait/Baffin Bay region. This analysis used data collected in benthic surveys from 2000 to 2004, and results indicate that water temperature, latitude, and depth strongly influence the distribution of fish species in this region. Latitude and water temperature were strongly negatively correlated, and the majority of species were distributed along the low latitude/high temperature to high latitude/low temperature gradient. Some fish species were more highly correlated with depth. Proportions of most by-catch species differed between northern and southern regions at each depth range. Since community structure changes gradually along environmental gradients, perhaps it is more appropriate to describe the communities as shifting their trophic status along environmental gradients rather than as distinct communities with distinct food webs in a specific region of the Arctic.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".