The Middle Paleolithic Ground Stones Tools of Nesher Ramla Unit V (Southern Levant): A multi-scale use-wear approach for assessing the assemblage functional variability
Bibliographic record
Abstract
In the archaeological record, Ground Stone Tools (hereafter GST) represent an important tool group that provides invaluable data for exploring technological development and changes in resource exploitation over time. Despite its importance, Lower and Middle Paleolithic (MP) GST technology remains poorly known and understudied. The MP record of the Levant constitutes a compelling case study for exploring the nature and character of GST technology. Especially the site of Nesher Ramla (Israel, end of Marine Isotope Stage 6/beginning of 5) has provided one of the world’s largest GST assemblages from MP contexts. Aiming at evaluating the variability of tool types at the site from a technological and functional perspective, this study follows an analytical approach which integrates different scales of analysis. Our workflow seeks to generate and combine qualitative and quantitative data allowing: 1) the identification of damage areas, and 2) functional analysis, based on the location, distribution, and characterization of use-wear traces. This study shows a substantial level of diversification in resource exploitation (e.g., mineral, hard animal material and likely perishable components). Results show the presence of several tool types on which diagnostic use-wear can be associated with different activities. Importantly, our analysis indicates the presence of various hammerstone types showing distinct wear characteristics. The variability observed within the hammerstones likely reflects different functions, including in some cases the processing of distinct worked materials. Ultimately, this study contributes to our understanding of the significance of GST technology for the ecological dynamics of MP populations.
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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 | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".