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 (t0) and in the control remained at ~0.1–0.2 nmol L−1. In the control, chlorophyll (=0.3 mg m−3), 14C 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 Pm >10‐fold and Pmchl 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 14C 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 Pm and α around 100‐fold and Pmchl and αchl by 5–10‐fold compared to the control. The +Fe treatment showed a slightly higher αchl and slightly lower Pmchl 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 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) | 0.000 | 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 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".