Shelf‐basin gradients shape ecological phytoplankton niches and community composition in the coastal Arctic Ocean (Beaufort Sea)
Bibliographic record
Abstract
Abstract The contiguous Arctic shelf is the green belt of the Arctic Ocean. Phytoplankton dynamics in this environment are driven by extreme physical gradients and by rapid climate change, which influence light and nutrient availability as well as the growth and ecological characteristics of phytoplankton. A large dataset collected across the Canadian Beaufort Shelf during summer 2009 was analyzed to assess how the interplay of physical and biogeochemical conditions dictates phytoplankton niches and trophic regimes. Nonmetric multidimensional scaling and cluster analysis demonstrated marked partitioning of phytoplankton diversity. Elevated phytoplankton biomass (∼ 2.41 μg Chl a L−1) was observed in association with the surface mixed layer near the coast, close to the mouth of the Mackenzie River, and at the shelf‐break as a result of nutrient‐rich Pacific water intrusions. The coastal communities were supported by high levels of nitrogen nutrients and were taxonomically uniform, with diatoms accounting for 95% of total cell numbers. By contrast, adjacent oceanic waters were characterized by low autotrophic biomass near the surface (∼ 0.09 μg Chl a L−1) and below the mixed layer (∼ 0.23 μg Chl a L−1) due to mainly nutrient limitation. However, the oceanic community was more diverse with a mixed assemblage of diatoms and small mixotrophs/heterotrophs near the surface and a predominance of autotrophic nanoflagellates at depth. We conclude that as climate change intensifies freshening and stratification in the Western Arctic Ocean, coastal hotspots of high autotrophic productivity may play an even greater role in supporting Arctic marine ecosystems while offshore environments become increasingly oligotrophic.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".