Differential effects of iron additions on organic and inorganic carbon production byphytoplankton
Bibliographic record
Abstract
Bottle and mesoscale experiments have demonstrated that iron additions enhance phytoplankton growth and reduce surface pCO2 in highߚnutrient, low‐chlorophyll (HNLC) regions of the world oceans. Here we show that iron additions specifically stimulate organic but not inorganic carbon production in the HNLC Subarctic Pacific. Fiveߚhour 14C labeling experiments performed during incubation of surface water samples demonstrated a large increase in the rate of organic carbon produced but no change in the rate of inorganic carbon production. The same result was obtained on two different dates: one when coccolithophores formed a relatively large proportion of total autotrophic biomass; the other when coccolithophores were less abundant. Together with previous taxonomic observations, our results imply that iron fertilization may be particularly effective in drawing down CO2 in surface waters by stimulating primary production but not calcium carbonate precipitation, which augments CO2.
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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".