Blind test evaluation of consistency in macroscopic lithic raw material sorting
Bibliographic record
Abstract
Abstract Most archaeological lithic raw material studies depend upon a macroscopic classification. However, since the human eye is a limited tool, some inconsistencies in classification may arise. Thus, a process for evaluating and increasing the reliability of macroscopic classification is needed. We present the results of a blind test designed to evaluate consistency in macroscopic lithic materials analysis, based on archaeological material taken from the Acheulo‐Yabrudian site Qesem Cave (Israel), focusing on interobserver error, aimed at identifying consistencies and weaknesses within our own study scheme. Twelve students, with various degrees of experience and familiarity with the Qesem material, sorted 100 randomly selected flint pieces into flint types, based on a previously established database, after a brief tutorial process. In addition, the authors, LW and AA, performed the same test. We then compared the results, using LW's results as an anchor. Our results show that experience affects the consistency in classification, demonstrating that it is an acquired skill. Furthermore, the blind test allowed us to identify weaknesses within the classification scheme. We suggest that blind tests should be regularly used to check accuracy and reproducibility of results and to assess the definitions set by the analyst, allowing fine‐tuning and calibration of the classification process.
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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 | 0.031 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".