Insights from modern diffuse-flow hydrothermal systems into the origin of post-GOE deep-water Fe-Si precipitates
Bibliographic record
Abstract
Post-GOE deep-water Fe-Si precipitates associated with volcanogenic massive sulfide deposits are an important feature of the Proterozoic rock record. Although it is clear that these enigmatic deposits formed in oxygen-deficient and hydrothermally influenced deep-water settings, the oxidation mechanism(s) resulting in their precipitation remain(s) unclear. Whilst existing genetic models typically couple direct and/or bacterially-mediated iron oxidation with abiotic silica precipitation, low temperature diffuse hydrothermal fluids offer a potential mechanistic alternative to explain the observed layering. Herein, via combination of petrographic observations with elemental and isotopic data (Fe, Si, and O), we explore the genesis of the primary mineral phases present within recent Fe-Si precipitates obtained from the Southwest Indian Ridge. Formation of ferrihydrite and opal-A in these precipitates provides insight into the genesis of widely invoked precursor minerals to post-GOE deep-water Fe-Si precipitates. Specifically, we find that the mineralogical layers that typify these Fe-Si precipitates may have originated via biologically mediated ferrihydrite precipitation and abiotic precipitation of opal-A from oversaturated fluids during diffuse flow. By analogy, we propose that diffuse hydrothermal fluid flow played an important role in the formation of post-GOE deep-water Fe-Si precipitates.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".