A simple method to classify diamicts by scanning electron microscope from surface microtextures
Bibliographic record
Abstract
Abstract Interpretation of quartz sand grain surface microtextures with scanning electron microscopy has been riddled with inconsistencies, invalid assumptions and much subjectivity. Therefore, a novel classification for analysing grain surface microtextures is presented based on the origin of complete grain surfaces. This novel method has solved most of the earlier problems of interpretation of surface microtextures, and it is easy to use and to quickly find evident genetic interpretations of diamicts. The data are plotted graphically in ‘2‐History Diagrams’ or ‘3‐History Diagrams’ for quick visual inspection and statistical evaluation. Source rocks and Quaternary glacial deposits from Scandinavia and Southern Ontario, representing different ice‐substrate dynamics, are analysed to define surface microtextures from typical glacigenic grains, bedrock and fluvially transported grains. Typical glacially crushed grains display large‐scale fractures and abrasion. Shield bedrock grains display large or small‐scale fractures and solution/precipitation microtextures. Fluvially transported grains exhibit abrasion and solution/precipitation microtextures.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".