A 40-year analysis of environmental trends and their ecological impacts in the Beaufort Large Marine Ecosystem
Bibliographic record
Abstract
Long-term analyses of environmental trends in the Arctic Ocean show a substantial decline in sea ice area and thickness over the last 30-40 years, affecting primary productivity and marine species abundance or distribution. Though research in the Beaufort Sea clearly shows changes in sea ice and productivity, few studies have considered the Beaufort Large Marine Ecosystem (LME), an ecologically significant area designated by the Arctic Council’s Protection for the Marine Environment that includes the Beaufort Shelf and western Canadian archipelago. We examined trends in four environmental variables (sea ice area, sea surface temperature, Mackenzie River water discharge, and chlorophyll-a concentration) over a 40-year period from 1979 to 2019 and assessed whether the annual catch of cod species and Arctic char related to environmental changes. Between the 1980s and 2010s, annual minimum sea ice coverage in the Beaufort Sea contracted by about 60%. In contrast to other studies on subregions of the Arctic Ocean, Beaufort Sea primary productivity did not increase as a function of longer open water season or larger open water areas from contracting sea ice area. Instead, open ocean chlorophyll-a varied interannually, with large spikes in 2006 and 2013. Despite one statistically significant relationship, reconstructed annual fish catch did not reflect trends in environmental variables. Coastal sea surface temperatures and chlorophyll-a concentrations exhibited slower rates of change than in the open ocean, demonstrating a need for more integrated studies across the LME. In future studies, fisheries-independent data are necessary to assess how Arctic Char and cod species respond to environmental changes in the Beaufort LME.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".