<sup>57</sup>Fe as a Tracer of Bacterially Mediated Iron Mobilization in Lake Sediments
Bibliographic record
Abstract
In simulation experiments the stable isotope, 57 Fe, was used as a novel tracer of iron transport to investigate the role of microbial activity on the distribution/redistribution of metals in lake sediments. A series of microcosm sediment columns was set up, containing a layer of 57 Fe-labelled ferrihydrite to track iron mobility temporally. Three series of columns were prepared, one set with unamended homogenized lake sediments, one set amended with 57 Fe-labelled ferrihydrite in the bottom layer of the column and one abiotic control set amended with 57 Fe-labelled ferrihydrite which was sterilized. During the experiments the iron concentrations increased in the upper sediment layers, pore water and overlying lake water and decreased in the lower amended sediment layer. The distribution of 57 Fe in the abiotic cores did not change during the experiment, implying that physical disturbance during sampling and chemical iron reduction and remobilization can be excluded as causes for the changes in iron distribution in the biotic cores. Thus, the remobilization and deposition of iron in the columns was initiated by microbial redox reactions. The implications of this work are significant for the interpretation of metal profiles in fresh water sediments as historical records of metal loadings, particularly because redox-sensitive amorphous Fe and Mn oxides and oxyhydroxides can serve as sites for metal adsorption or co-precipitation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".