Grain-Size Analysis of Ancient Deep-Marine Sediments Using Laser Diffraction
Bibliographic record
Abstract
Grain-size analysis of siliciclastic sedimentary rocks provides critical information for interpreting flow dynamics and depositional environments in sedimentary systems and for analysing reservoir quality of sandstone. Methods such as sieving and thin-section analysis are time consuming and unsuited for large sample numbers. Laser diffraction particle analysis is quick and reliable for analysing 100s of samples, assuming successful disaggregation. Here, we evaluate this method utilizing samples from three siliciclastic formations in Northern Italy: the Miocene Castagnola and Marnoso-Arenacea Formations, and the Cretaceous to Palaeocene Gottero Formation, which vary in degree of lithification. We focus on: 1) methods of whole-rock disaggregation; 2) methods of subsampling sediment for laser diffraction analysis; and 3) comparison of thin-section analysis with laser-diffraction particle size analysis. Using an ultrasonic bath and a SELFRAG (high voltage selective fragmentation) as disaggregation tools, this study evaluates separation of whole, undamaged grains subsequently measured by laser diffraction analysis. We show that it is possible to disaggregate ancient, well cemented rocks using an ultrasonic bath. When disaggregating samples with the SELFRAG method, grain-size measurements become less accurate and less precise with increasing sample lithification and increased presence of cement. This is likely a combination of incomplete grain disaggregation in the SELFRAG and heterogeneity within samples. Following disaggregation, we compare sub-sampling methods using a stirrer plate versus a pipette. Both produce accurate analyses, but the stirrer method is the most reliable and replicable. A comparative small subsample method, run as one whole sample with no need for subdivision into aliquots, is found to be reliable and replicable but is more susceptible to heterogeneity within field samples. When comparing laser diffraction results to grain-size volume methods estimated from thin-section analysis, thin-section sand grains are overestimated, and clay/silt grains are inaccurate. These results provide a framework for understanding potential biases introduced through various sample preparation and measurement methods.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".