Variability in the phytoplankton response to upwelling across an iron limitation mosaic within the <scp>California</scp> current system
Bibliographic record
Abstract
Abstract Coastal upwelling currents such as the California Current System (CCS) comprise some of the most productive biological systems on the planet. Diatoms dominate these upwelling events in part due to their rapid response to nutrient entrainment. In this region, they may also be limited by the micronutrient iron (Fe), an important trace element primarily involved in photosynthesis and nitrogen assimilation. The mechanisms behind how diatoms physiologically acclimate to the different stages of the upwelling conveyor belt cycle remain largely uncharacterized. Here, we explore their physiological and metatranscriptomic response to the upwelling cycle with respect to the Fe limitation mosaic that exists in the CCS. Subsurface, natural plankton assemblages that would potentially seed surface blooms were examined over wide and narrow shelf regions. The initial biomass and physiological state of the phytoplankton community had a large impact on the overall response to simulated upwelling. Following on‐deck incubations under varying Fe physiological states, our results suggest that diatoms quickly dominated the blooms by “frontloading” nitrogen assimilation genes prior to upwelling. However, diatoms subjected to induced Fe limitation exhibited reductions in carbon and nitrogen uptake and decreasing biomass accumulation. Simultaneously, they exhibited a distinct gene expression response which included increased expression of Fe‐starvation induced proteins and decreased expression of nitrogen assimilation and photosynthesis genes. These findings may have significant implications for upwelling events in future oceans, where changes in ocean conditions are projected to amplify the gradient of Fe limitation in coastal upwelling regions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".