Assessing Winter Phytoplankton Community Composition Dynamics and Their Response to Environmental Drivers in the Subarctic Northeast Pacific
Bibliographic record
Abstract
Abstract The subarctic northeast Pacific (SNEP) is a high‐nutrient, low‐chlorophyll region where primary productivity is limited by bioavailable iron during the spring through autumn, and by light limitation during winter. Here, we investigate the spatio‐temporal distribution and drivers of SNEP surface phytoplankton biomass and community composition in the winters of 2019 and 2020 using in situ environmental data, chemotaxonomic analysis of phytoplankton pigment samples, and Sentinel‐3A Ocean Land Color Instrument imagery. The utilized satellite‐based algorithm showed promise replicating the expected trends of: (a) homogenous phytoplankton communities dominated by haptophytes, green algae, and pelagophytes in highly mixed light‐limited oceanic waters and; (b) increased diatoms in coastal Haida Gwaii waters with reduced mixed‐layer depth (MLD) and salinity. Unexpectedly, increases in cryptophytes were observed in the northern extents of the SNEP, which coincided with winter marine heatwave driven reductions in MLDs and also the presence of a mesoscale eddy. This finding highlights a deviation from expected homogeneous phytoplankton conditions, which may be systematically missed by spatially and temporally constrained in situ sampling. The further advancement and deployment of the satellite‐based algorithm could significantly expand the understanding of winter phytoplankton dynamics in the SNEP, a critical period for Pacific salmon survival, improving the understanding of trophic linkages and match/mismatch dynamics, and contributing to improve the forecasting of salmon returns.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".