Analysis of common pre-treatments in grain-size analysis (using a grain-size standard)
Bibliographic record
Abstract
ABSTRACT This study investigates the impacts of sample preparation procedures on grain-size measurements to determine comparability of data collected using differing methodologies. Grain-size distributions of marine and terrestrial sediments contain important information about the depositional environment. For example, the “sortable-silt index” (or mean grain size between 10 and 63 μm in marine sediments, is used as an indicator of flow speed and has been applied to the reconstruction of ocean current strength before the instrumental period. Similarly, the mean grain size of a sediment is used to classify it (e.g., silt versus sand). Accurate measurements of grain-size distributions often require chemical pre-treatments in order to remove sedimentary components of biogenic origin (e.g., shells), and multiple ways to perform these pre-treatments exist. This study tests whether the choice of pre-treatment introduces variability into grain-size distributions. We simulate multiple commonly used pre-treatments on a well-characterized internal standard (“Sillikers”) and compare the resulting mean size and sortable-silt index in each treatment group to untreated samples using ANOVA. Two instruments, a Coulter Counter Multisizer III and a Coulter LS 230 Laser Diffraction Analyzer are used. Results from the Multisizer III suggest that the choice of pre-treatment method does not significantly impact the final grain-size distributions but underlines the importance of replicates. Results from the laser sizer suggest that oven-drying leads to a small but statistically significant difference of ∼ 0.3 μm in the sortable-silt index, and drying samples via hot plate leads to another small but statistically significant difference of ∼ 0.29 μm. While it is unclear what causes these differences in the laser sizer data, they are smaller than the observed variations in sortable-silt index used to infer changes in current speed in a typical paleoclimate study. In conclusion, grain-size measurements are a robust tool for sediment analysis and are resistant to changes from differing pre-treatment methods tested here.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".