A 40-year analysis of environmental trends and their ecological impacts in the Beaufort Large Marine Ecosystem
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it