Microscale Characterization and Trace Element Distribution in Bacteriogenic Ferromanganese Coatings on Sand Grains from an Intertidal Zone of the East China Sea
Bibliographic record
Abstract
An ancient wood layer dated at about 5600 yr BP by accelerator mass spectrometry (AMS) 14C was discovered in an intertidal zone of the East China Sea. Extensive and horizontally stratified sediments with black color on the top and yellowish-red at the bottom, and some nodule-cemented concretions with brown surface and black inclusions occurred in this intertidal zone. Microscale analysis methods were employed to study the microscale characterization and trace element distribution in the stratified sediments and concretions. Light microscopy, scanning electron microscopy (SEM) and backscattered electron imaging (BSE) revealed the presence of different coatings on the sand grains. The main mineral compositions of the coatings were ferrihydrite and goethite in the yellowish-red parts, and birnessite in the black parts using X-ray powder diffraction (XRD). SEM observations showed that bacteriogenic products and bacterial remnants extensively occurred in the coatings, indicating that bacteria likely played an important role in the formation of ferromanganese coatings. Post-Archean Australian Shale (PAAS)-normalized middle rare earth element (MREE) enrichment patterns of the coatings indicated that they were caused by two sub-sequential processes: (1) preferentially release of Fe-Mn from the beach rocks by fermentation of ancient woods and colloidal flocculation in the mixing water zone and (2) preferential adsorption of MREE by Fe-Mn oxyhydroxides from the seawater. The chemical results indicated that the coatings were enriched with Sc, V, Cr, Co, Ni, Cu, Zn, Ba, especially with respect to Co, Ni. The findings of the present study provide an insight in the microscale features of ferromanganese coatings and the Fe-Mn biogeochemical cycling during the degradation of buried organic matter in intertidal zones or shallow coasts.
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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 | 0.000 | 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 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".