Distinguishing Depositional Setting For Sandy Deposits In Coastal Landscapes Using Grain Shape
Bibliographic record
Abstract
Abstract: Several methods exist that use sediment properties to characterize depositional setting and related mechanisms of transport, including analysis of grain-size distributions, sediment petrology, micromorphology, and grain structure. Techniques that rely on electron or optical microscopy produce results with varying degrees of success and applicability. Here, a new method is presented and used to differentiate between littoral and eolian sands that were extracted from recently formed landforms, as well as landforms that are from mid to late Holocene in age. The method utilizes a standard optical microscope with a mounted digital camera, paired with freely available software (ImageJ) to characterize grain shape parameters. The method was tested on nearly 6000 sand grains from samples with varied transport histories, and it was found that grain solidity was the most distinguishable characteristic between eolian and littoral samples, differentiating them 86% of the time for calibration samples. The method was used to correctly identify the mechanism of transport for 76% of the samples. Patterns in the results indicated that this method could be extended to link potential sediment sources to various depositional basins, and future work includes testing the method in areas with a different mineralogy and/or landscape history.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".