Lithic artifact assemblage transport and microwear modification in a fluvial setting: A radio frequency identification tag experiment
Bibliographic record
Abstract
Abstract River processes are widely assumed to have impacted the integrity of lithic assemblages when artifacts are found in fluvial sediments, but the specifics of these influences remain largely unknown. We conducted a real‐world experiment to determine how the initial stages of fluvial entrainment affected lithic artifact assemblages. We inserted replica artifacts with radio frequency identification tags into a gravel‐bedded river in Wales (UK) for seven months and related their transport distances to their morphology and the recorded streamflow. In addition, nine artifacts were recovered at the end of the experiment and analyzed for microwear traces. In sum, our results show that in a gravel‐bedded river with a mean discharge of 5.1 m3/s, artifact length and width were the main variables influencing artifact transport distances. The experiment also resulted in characteristic microwear traces developing on the artifacts over distances of 485 m or less. These results emphasize the multifaceted nature of alluvial site formation processes in a repeatable experiment and highlight new ways to identify the transport of replica Paleolithic material.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".