Zinc stimulation of phytoplankton in a low-carbon-dioxide, coastal Antarctic environment: evidence for the Zn hypothesis
Bibliographic record
Abstract
The ocean acts as a carbon sink, absorbing carbon from the atmosphere and resulting in substantial uptake of anthropogenic CO 2 emissions. As biological processes in the oceans such as net primary production (NPP) contribute significantly to this sink, understanding how they will shift in response to increasing atmospheric CO 2 is necessary to project future ocean carbon storage capacity. Macronutrient and micronutrient resource limitation within the oceans regulates NPP, and while some micronutrients such as zinc (Zn) are present at very low concentrations, their ability to limit NPP has remained unclear. Zn is a key micronutrient used by phytoplankton for a multitude of metabolic functions, yet there have been few observations of its influence on natural oceanic phytoplankton populations. In this study, we observed Zn limitation of growth in the natural phytoplankton community of Terra Nova Bay, Antarctica, in addition to primary iron (Fe) limitation. Shipboard incubation experiments amended with Zn and Fe resulted in significantly higher chlorophyll a content and dissolved inorganic carbon drawdown compared to Fe addition alone. Zn- and Fe-stress-response proteins detected in incubation and environmental biomass provided independent verification of algal co-stress for these micronutrients. We consider total biomass and low surface ocean p CO 2 as potential drivers of environmental Zn stress. This study establishes that Zn limitation can occur in the modern oceans, opening up new possibility space in our understanding of nutrient regulation of NPP through geologic time and supporting the need to consider the future of oceanic Zn limitation in the face of climate change.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".