Quantifying simulated fine sand fraction in muddy sediment using laser diffraction
Bibliographic record
Abstract
The objective of this study is to verify whether low amounts of fine sand added to a muddy sediment matrix can be detected and quantified with accuracy using a Mastersizer 3000 (Malvern Panalytical) laser diffraction particle-size analyzer equipped with a Hydro LV large volume liquid dispersion module. To achieve this goal, a postglacial sediment sample was sieved to recover naturally co-occurring sand and clay–silt fractions. Sand in the range of 1%–7% by weight was added to the clay–silt at three concentrations (88, 132, and 276 mg dry weight) and each sample was duplicated. A very strong linear relationship was found between the measured % volume of sand added and the actual weight of sand added to the mud. Sand representing as low as 1% by weight could be detected. On average, there was only a 0.7% absolute difference between the measured and actual % sand values (range 0.02%–1.65%). Sample concentration had a negligible impact on the measured % sand. A range of plausible values for the refractive and absorption indexes, essential parameters for the Mie theory calculation of the size distribution from the measured light scattering, also had very small impact on the measured % sand. The demonstrated possibility of detecting a small input of fine sand to muddy sediment provides a basis for studies using grain-size data to reconstruct past and modern detrital inputs and sediment transport variations.
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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 | 0.001 | 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".