IMPRINTS IN SILICA GRAINS INDUCED DURING AN OPEN-CHANNEL FLOW EXPERIMENT: DETERMINATION OF MICROTEXTURAL SIGNATURES DURING AQUEOUS TRANSPORT
Bibliographic record
Abstract
Abstract The aim of this work is to identify and characterize microtextural signatures in silica glass grains (used as analogous to quartz) that are produced during aqueous transport at different flow velocities, with variable sediment concentrations, transport distances, and time intervals. To achieve this, an open-channel flow experiment was conducted with a mixture of sand and silica glass microspheres in varying conditions—velocity (from 0.67 to 1.4 m/s), duration (1 or 10 minutes), distance (0 to 2.5 m) and sediment concentration (60 or 80%). Experimental conditions were used to replicate natural phenomena such as river superficial velocity or coastal swash processes. Before the experiment the microsphere surfaces were imaged and clear of any microtextural imprint. Increasing velocity, distance, and sediment concentration exhibited a strong correlation with higher numbers of surfaces abundantly covered with microtextures of mechanical origin (i.e., craters, abrasion marks, and v-shaped percussion marks). SEM microphotographs of silica were analyzed and classified to provide examples of the specific microtextures produced during the open-channel flow experiment. The purpose of the experiment was to characterize surface microscopic signatures in quartz grains replicating hydrodynamic conditions of coastal and fluvial environments. The results demonstrated a strong correlation between higher velocities (and higher sediment concentrations) and a larger presence of microtextural mechanical imprints in the grains analyzed, thus demonstrating a clear relation between microtextural imprints and water flow modes. These results have important implications for future microtextural works analyzing grain imprints and their relation to sediment transport types. An example demonstrated here is that the higher presence of v-marks could be used as an indicator of supercritical flow conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".