Sea Surface Kinetic Energy as a Proxy for Phytoplankton Light Limitation in the Summer Pelagic Southern Ocean
Bibliographic record
Abstract
Abstract The pelagic Southern Ocean is a high‐nutrient, low‐chlorophyll ecosystem. Here, phytoplankton growth is colimited by iron supply and light availability. This creates a general expectation that when light is available in the austral summer (shallow mixing depths), phytoplankton concentrations may be high or low depending on the delivery of iron to the surface layer. When light is not adequate (deep mixing depths), phytoplankton concentrations will likely be low, even if iron is available. Here we show that low surface kinetic energy behaves like a necessary but not sufficient condition for high chlorophyll concentrations. In high kinetic energy conditions, high chlorophyll concentrations are rare. Conversely, under low kinetic energy conditions, both high and low chlorophyll concentrations were observed. We show that higher kinetic energy conditions are related to deeper mixed layers, which is likely a proxy for local light conditions. Probabilistic models of chlorophyll based on surface kinetic energy were able to describe 30% of the spatial variability in monthly chlorophyll climatologies. This means that local light availability, proxied by mixing through kinetic energy, significantly shapes the spatial distribution of chlorophyll in the Southern Ocean. We suggest that regions with consistently higher kinetic energy may not be as sensitive to iron inputs compared to historic iron addition experiments, which were conducted in low surface kinetic energy conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".