Using sedimentary pyrite chemistry to inform regional exploration for sediment-hosted gold deposits - a gold fertility case study from the Selwyn Basin Area, Yukon
Bibliographic record
Abstract
Regional-scale exploration for sediment-hosted gold deposits is hampered by the challenge of evaluating the fertility of ancient sedimentary sequences that host these deposits. Recent developments in laser ablation inductively coupled plasma mass spectrometry allow for the evaluation of the gold fertility of sedimentary sequences using chemistry of early-formed sedimentary pyrite. We present a case study from the Selwyn basin area of Yukon, Canada, where significant sediment-hosted gold exploration has been ongoing for the last five years. Data from sedimentary pyrite in some Neoproterozoic shales are anomalous in Au and As compared to a global data set of unmineralised sedimentary pyrite. If at least some of the gold and associated trace elements in sediment-hosted gold deposits are derived from recrystallisation of early sedimentary pyrite, it follows that strata containing anomalous sedimentary pyrite should be more prospective. Based on this premise, our data clearly show that Yusezyu Formation Neoproterozoic shales in the Selwyn basin area of Yukon are a fertile source rock for sediment-hosted gold deposits, while pyrite from Nadaleen formation shales are strongly anomalous in Hg and Tl, but not Au. We suggest that the area warrants continued exploration for sediment-hosted gold.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".