Iron enrichment stimulates toxic diatom production in high-nitrate, low-chlorophyll areas
Bibliographic record
Abstract
Oceanic high-nitrate, low-chlorophyll environments have been highlighted for potential large-scale iron fertilizations to help mitigate global climate change. Controversy surrounds these initiatives, both in the degree of carbon removal and magnitude of ecosystem impacts. Previous open ocean enrichment experiments have shown that iron additions stimulate growth of the toxigenic diatom genus Pseudonitzschia. Most Pseudonitzschia species in coastal waters produce the neurotoxin domoic acid (DA), with their blooms causing detrimental marine ecosystem impacts, but oceanic Pseudonitzschia species are considered nontoxic. Here we demonstrate that the sparse oceanic Pseudonitzschia community at the high-nitrate, low-chlorophyll Ocean Station PAPA (50 degrees N, 145 degrees W) produces approximately 200 pg DA L(-1) in response to iron addition, that DA alters phytoplankton community structure to benefit Pseudonitzschia, and that oceanic cell isolates are toxic. Given the negative effects of DA in coastal food webs, these findings raise serious concern over the net benefit and sustainability of large-scale iron fertilizations.
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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 | 0.000 | 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".