Review and synthesis: iron input, biogeochemistry, and ecological approaches in seawater
Bibliographic record
Abstract
The processes involved in the biogeochemical cycle of Fe in the oceans have been intensely discussed in recent decades because this element is limiting to primary productivity in most oceanic regions. From biogeochemical and ecological perspectives, inputs from anthropogenic sources, especially mining activities, may be more representative than natural inputs in coastal areas affected by metal loads from tailings. Here we provide a review of all the stages related to Fe behaviour in marine ecosystems, including Fe input sources, which may be of natural and (or) anthropogenic origin; input rates; chemical speciation; bioavailability; and changes in the phytoplankton community structure. To allow conceptualization of the anthropogenic processes, the collapse of the Fundão tailings dam (southeast Brazil) was used as a case study of one of the worst environmental disasters of the mining industry. From this perspective, the interrelations among the chemical, biological, and ecological components were discussed. Regarding the chemical component, Fe speciation must be determined by the input of several other materials, mainly organic compounds that can be complexed to this element and increase its solubility. From a biological perspective, the biochemical and physiological processes used for the assimilation of this element, such as the reduction in cell membranes and the production of chelating substances (such as siderophores), will also determine the forms of this element present in the water column. On the other hand, the groups that obtain a competitive advantage due to these assimilation strategies must be dominant in the system. Synergistic effects are also expected with other materials such as the inorganic nutrients, organic compounds, and metals that are carried to the coastal region together with Fe. In the specific case of mine tailings, the accumulation of this material in the river banks and bed should cause an increase in Fe input from other sources, such as atmospheric transport and submarine groundwater discharge, as well as river discharge by erosion and transport under increased river flow conditions. The iron fluxes from mining areas to coastal oceans and the effects of these loads to phytoplankton ecological aspects should be investigated.
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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 | 0.001 | 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 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".