How does weathering influence geochemical proxies in Paleoproterozoic banded iron formations? A case study from outcrop samples of 2.46 Ga banded iron formation, Hamersley Basin, Western Australia
Bibliographic record
Abstract
Abstract Trace metal and rare earth element (REE) abundances in banded iron formations are critical for assessing the chemical composition of ancient seawater and the long-term evolution of the ocean-atmosphere system. Recent studies, however, have highlighted the potential effects of outcrop weathering, raising concerns about whether banded iron formation samples are suitable proxies for ancient redox conditions or if exposure to surficial weathering regimes may have altered key geochemical signals. Here, we present a detailed, high-resolution study of several banded iron formation outcrop samples from the Hamersley Basin, Western Australia, to investigate microscale differences in composition between banded iron formation and weathered surfaces (i.e., weathered crusts). Elemental mapping and bulk-rock geochemical analyses reveal that weathered crust is more enriched in most elements than the banded iron formation, except for silica, which is significantly depleted. There is also a significant loss of redox-sensitive elements (RSEs) in the weathered surface, which suggests that outcrop samples have been affected by higher degrees of chemical leaching than physical erosion. These results are significant, because we clearly show that the geochemical characteristics of the weathered surface—irrespective of how it formed—are distinct from those of the remainder of the sample. This means that with sufficient screening of samples for obvious signs of alteration, banded iron formation outcrop samples may indeed be used as a reliable proxy for the evolution of Earth’s coupled ocean-atmosphere system. This increases the volume of easily accessible Precambrian sample material, so that researchers no longer solely need to rely on core recovered through costly drilling programs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".