A comparative analyses of microstructures from Late Jurassic diamictic units, near Helmsdale, northeast Scotland and a Pleistocene diamicton from near Milton, southern Ontario, Canada – a differential diagnostic method of sediment typing using micromorphology
Bibliographic record
Abstract
Micromorphology is used to examine and compare a Late Jurassic diamictite from northeast Scotland with a Pleistocene diamict from southern Ontario, Canada in order to test if a statistical difference between diamicts can be recognized and used to separate differing types of diamicts/ diamictites. The diamictites from Scotland have been ascribed to various depositional agencies occurring in several distinctly differing terrestrial and marine palaeoenvironments. In contrast, the Pleistocene diamicton is regarded as a subglacial till. Both diamicts appear remarkably similar visually and contain many corresponding features such as macrostructures, and exotic and fractured subangular to subrounded clasts. Micromorphology is used to re-examine these diamicts/diamictites at the microscopic level to detect if the palaeoenvironments within which they were deposited can be ascertained. In this paper a quantitative assessment of microstructures using micromorphology is developed. Comparative statistical analyses of these diamicts, using micromorphological features, reveals that the Jurassic diamictites are non-glacigenic, non-terrestrial and most likely deposited within a marine environment as a result of subaquatic debris mass movement, while, in contrast, the Pleistocene diamicts were most likely subglacial tectomicts deposited beneath the active base of the Laurentide Ice Sheet.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".