Spinning the “Ferrous Wheel”: The importance of the microbial community in an iron budget during the FeCycle experiment
Bibliographic record
Abstract
Several studies have shown the importance of the microbial community in specific aspects of the biogeochemical iron (Fe) cycle such as uptake or regeneration. During FeCycle, a 10‐day study of Fe biogeochemistry within an unperturbed mesoscale in situ SF 6 labeled patch of HNLC waters, we investigated the role of both microzooplankton (herbivores and bacterivores) and viruses in regenerating Fe in the upper ocean. In summer 2003 we measured grazer‐mediated Fe regeneration rates. The proportion of bacterial Fe released via grazing was severalfold greater than that mobilized from phytoplankton during herbivory. However, as the algal Fe pool (mainly Synechococcus ) was severalfold larger than the bacterial pool, the absolute Fe regeneration rates were similar for both herbivores (17 pmol Fe L −1 d −1 ) and bacterivores (20 pmol Fe L −1 d −1 ). In all grazing experiments we observed that 90% (bacterivory) and 25% (herbivory) of the labeled Fe resided in the dissolved fraction after 24 hours. This trend has previously been reported in similar laboratory culture studies, which invoked the formation of dissolved, and/or colloidal metal ligands, associated with digestion, to make the released Fe less bioavailable. This explanation may not be valid for our study as another FeCycle experiment (Maldonado et al., 2005) demonstrated that resident phytoplankton could obtain Fe bound to a wide range of strong‐binding ligands. In situ estimates of virally mediated Fe regeneration during FeCycle ranged from 0.4 to 28 pmol L −1 d −1 . It is not known why such a wide range of virally mediated regeneration rates was observed. Such variability prevented a direct comparison on the relative roles of grazers and viruses in Fe recycling. The rates of grazer‐mediated regeneration accounted for 30% to >100% of the bacterial and phytoplankton Fe demand measured during FeCycle, indicating the key role of the microbial food web in Fe recycling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".