Glacial microsedimentology—a new lens to investigate glacial sediments—a review
Bibliographic record
Abstract
Glacial sediment research using thin sections began post-1945. Microsedimentology examines sediments at the microscopic level deriving insights into the processes of glacial erosion, transport, and deposition. Two issues exist (1) the difficulty by some in recognizing microstructures in thin section and (2) the absence of quantitative data making data reproduction difficult. The latter is hard to resolve but more image capture and software methodologies are now becoming available at reasonable costs. Thin sections are two-dimensional sections of three-dimensional objects, and this must be considered when measurements, fabrics and other data are assessed. Research into the microaspects of glacial sediments followed a typical scientific trajectory: thin sections description with little uniformity or common “language” for observed microstructures: standardization allowed comparison between different sediments; with standardization, came an open-ended classification; and with cross-comparison with multiple thin sections—a quantitative means of study needs developed. It has become apparent that the basic principles of structural geology had to be applied. Thus, micromorphology has subsumed into a microsedimentological study of glacial sediments where stress parameters, structural fabrics and the mapping of deformation structures and contextual integration allows an understanding of how these sediments have been formed. Examples of the development stages of glacial micromorphology/sedimentology are presented here as well as discussion of future avenues of study. In addition, multiple thin sections are used to illustrate the many aspects of glacial micromorphology and the parameters necessary to understand glacial sedimentology and the processes of glacial sedimentation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".