Heavy mineral partitioning in sedimentary facies: Lac Baby Esker, Lac Timiskaming region, Canada
Bibliographic record
Abstract
Sampling protocols for heavy minerals (HM) and kimberlite indicator minerals (KIM) in glacifluvial sediments generally follows those established for sampling in fluvial environments. These protocols emphasize sampling of gravely facies. The transfer of this protocol to glacifluvial sediments (especially eskers) assumes that fluvial and glacifluvial systems operate in similar manners. Though they share process similarities, they also have significant differences. Consequently the existing protocol used to sample eskers is largely untested and is not constrained by understanding of glacifluvial processes. In the Lac Baby glacifluvial complex in the Lac Timiskaming region, various esker facies were sampled for KIM in order to assess how these minerals may be partitioned and concentrated in an eskerine depositional environment. The main finding of this study is that gravely esker facies are not necessarily the prime sampling medium in an esker, and that medium to coarse sand facies may be a better choice. However, this does not mean that sampling gravely facies should be abandoned. It is suggested that examining the matrix characteristics of coarser boulder beds is critical, as they may yield KIM. Near Notre Dame du Nord, heavy mineral (HM) grain counts are elevated within an esker segment where a cobble-boulder bedform occurs. In this bedform, HM grain counts are only slightly lower than the highest HM concentration reported from Lac Baby. Many esker gravely facies record mass flow deposits and poor sorting. These are poor sampling media for KIM due to a lack of density sorting. Nevertheless, other gravel facies have similar KIM concentrations to those reported in medium to coarse sand facies. Consequently, to select the optimum sample medium requires more than simple identification of sedimentary facies, but also the identification of the depositional controls on the facies to optimize sampling strategies based on facies interpretations.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".