Current state and trends in Canadian Arctic marine ecosystems: II. Heterotrophic food web, pelagic-benthic coupling, and biodiversity
Bibliographic record
Abstract
As part of the Canadian contribution to the International Polar Year (IPY), several major international research programs have focused on offshore arctic marine ecosystems. The general goal of these projects was to improve our understanding of how the response of arctic marine ecosystems to climate warming will alter food web structure and ecosystem services provided to Northerners. At least four key findings from these projects relating to arctic heterotrophic food web, pelagic-benthic coupling and biodiversity have emerged: (1) Contrary to a long-standing paradigm of dormant ecosystems during the long arctic winter, major food web components showed relatively high level of winter activity, well before the spring release of ice algae and subsequent phytoplankton bloom. Such phenological plasticity among key secondary producers like zooplankton may thus narrow the risks of extreme mismatch between primary production and secondary production in an increasingly variable arctic environment. (2) Tight pelagic-benthic coupling and consequent recycling of nutrients at the seafloor characterize specific regions of the Canadian Arctic, such as the North Water polynya and Lancaster Sound. The latter constitute hot spots of benthic ecosystem functioning compared to regions where zooplankton-mediated processes weaken the pelagic-benthic coupling. (3) In contrast with another widely shared assumption of lower biodiversity, arctic marine biodiversity is comparable to that reported off Atlantic and Pacific coasts of Canada, albeit threatened by the potential colonization of subarctic species. (4) The rapid decrease of summer sea-ice cover allows increasing numbers of killer whales to use the Canadian High Arctic as a hunting ground. The stronger presence of this species, bound to become a new apex predator of arctic seas, will likely affect populations of endemic arctic marine mammals such as the narwhal, bowhead, and beluga whales.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".