The Effects of Iron Complexing Ligands on the Long Term Ecosystem Response to Iron Enrichment of HNLC waters (Final Activity Report)
Bibliographic record
Abstract
Substantial increases in the concentrations of the stronger of two Fe(III) complexing organic ligand classes measured during the mesoscale Fe enrichment studies IronEx II and SOIREE appeared to sharply curtailed Fe availability to diatoms and thus limited the efficiency of carbon sequestration to the deep. Detailed observations during IronEx II (equatorial Pacific Ocean) and SOIREE (Southern Ocean –Pacific sector) indicate that the diatoms began re-experiencing Fe stress even though dissolved Fe concentrations remained elevated in the patch. This surprising outcome likely is related to the observed increased concentrations of strong Fe(III)-complexing ligands in seawater. Preliminary findings from other studies indicate that diatoms may not readily obtain Fe from these chemical species whereas Fe bound by strong ligands appears to support growth of cyanobacteria and nanoflagellates. The difficulty in assessing the likelihood of these changes with in-situ mesoscale experiments is the extended monitoring period needed to capture the long-term trajectory of the carbon cycle. A more detailed understanding of Fe complexing ligand effects on long-term ecosystem structure and carbon cycling is essential to ascertain not only the effect of Fe enrichment on short-term carbon sequestration in the oceans, but also the potential effect of Fe enrichment in modifying ecosystem structure and trajectory.
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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".