Changes in subarctic Pacific phytoplankton communities over the last two decades
Bibliographic record
Abstract
Ongoing climate change is expected to transform ecosystems worldwide. Time series of remotely sensed data are now of sufficient length to begin to assess change in the ocean at large spatial and temporal scales. This study focused on changes in the phytoplankton phenology and composition in the subarctic Pacific Ocean, winter residence region for Pacific salmonids. A time series of satellite phytoplankton phenology metrics and phytoplankton functional groups between 2002 and 2022 were analyzed. Additionally, potential drivers of change were determined among the essential environmental factors and climate indices. Using changepoint analysis, a decrease in the total bloom length was revealed in recent years in all bioregions except for the waters surrounding the Kamchatka Peninsula. Moreover, a decreasing trend in the diatom-to-dinoflagellate Chl-a and the diatom-to-small algae Chl-a, consisting of haptophytes, pelagophytes, green algae, and cyanobacteria, was observed in the Gulf of Alaska. A sharp decline was particularly pronounced after 2018, which probably stemmed from a combination of the weaker currents forming the North Pacific Gyre Oscillation (NPGO) and recurring marine heat waves after 2014. It is uncertain yet whether the decline of the diatom group is temporary or marks the beginning of a long-term shift in the phytoplankton community structure in the subarctic Pacific. The following years will likely bring the answers.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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".