Influence of zinc and iron enrichments on phytoplankton growth in the northeastern subarctic Pacific
Bibliographic record
Abstract
Near‐surface seawater from the northeastern subarctic Pacific was incubated on deck for 8 d, supplemented with (1) control, no additions (2) +Zn (3) +Fe (4) +Zn+Fe. Concentrations of total Zn and Fe at time zero (t 0 ) and in the control remained at ~0.1–0.2 nmol L −1 . In the control, chlorophyll (=0.3 mg m −3 ), 14 C uptake into POC and PIC, and inorganic nutrients all remained relatively constant. Addition of Zn slightly but significantly increased chlorophyll ( p = 0.05), decreased phosphate (p = 0.01) and nitrate (p = 0.05), and in P versus E experiments, increased P m >10‐fold and P m chl 2–3‐fold. The abundance of small diatoms and coccolithophores was higher in the +Zn treatment compared to the control. The +Fe and +Zn+Fe treatments, compared to the control, both showed >10‐fold increases in chlorophyll and 14 C uptake into POC and PIC and complete removal of nitrate (≤0.2 mmol m −3 ). However, differences were observed in size‐fractionated data; the +Zn+Fe treatment had significantly lower percent chlorophyll in the >20‐ µm fraction (p = 0.01) and a higher percentage in the 0.2–5‐ µm fraction (p = 0.01) than the +Fe treatment. In P versus E experiments, both +Fe treatments increased P m and α around 100‐fold and P m chl and α chl by 5–10‐fold compared to the control. The +Fe treatment showed a slightly higher α chl and slightly lower P m chl than the +Zn+Fe treatment. Abundance of large diatoms, small diatoms, small flagellates, and coccol‐ chl m ithophores all increased substantially (~7–1,000‐fold) in response to Fe addition, whereas dinoflagellate abundance only doubled. The +Zn+Fe treatment had higher abundances of small diatoms and small flagellates than the +Fe treatment. We conclude that Zn additions had limited influence on conventional indices of phytoplankton growth compared to Fe, but that there might be subtle influences of Zn that require further attention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".