Gold in eskers and gyttja overlying auriferous till, Regnault deposit, Quebec
Bibliographic record
Abstract
The Regnault gold deposit in Quebec, Canada, was discovered by Kenorland Minerals North America Ltd in 2020 following identification of a gold-in-till dispersal train. In 2022, two non-traditional media – eskers and organic lake-bottom mud (gyttja) – were sampled to determine if they likewise contained gold anomalies that vectored the mineral deposit. Striking, coherent (mappable) anomalies exist in both media. The gold-in-esker dispersal train has a similar pathfinder element association as the gold-in-till dispersal train (e.g. Te, W) but contains approximately twice the gold (average 73 ppb Au) in the <63 µm fraction. It is hypothesized to be a meltwater-sorted version of the gold-in-till train, sourced from the erosional esker corridor some 3 km upflow. By contrast, the gold-in-gyttja dispersal train is substantially different to both the gold-in-esker and gold-in-till dispersal trains. It has lost significant association with pathfinder elements, contains an order of magnitude less gold (average 4.2 ppb Au), and is tentatively hypothesized to have a hydromorphic origin, sourced primarily from the gold-in-till dispersal train. The main takeaway from the study is that the sampling of any of these three media – eskers, gyttja or till – could have conceivably led to the discovery.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".