Using Debitage Analysis to Investigate an Alberta Archaeological Site
Bibliographic record
Abstract
Ahai Mneh (FiPp-33) is a significant pre-contact archaeological site in Alberta. Located west of Edmonton on Lake Wabamun, this site contains material from the Early Prehistoric right up until Late Prehistoric pre-contact times. Ninety-five percent of the lithic artifacts collected are pieces of debitage. Aggregate analysis is a method of examining the whole of the debitage collection, rather than analysing singular pieces. This method is more time efficient, less subject to bias, replicable, and is used often, and successfully, at archaeological sites with immense quantities of debitage. Here I use aggregate analysis to examine the debitage assemblage from two field schools at Ahai Mneh. I investigate various characteristics such as size, raw material type, cortex amount, and number of dorsal scars. I argue that this method is successful, as it provided new information on where people were acquiring raw materials, as well as what types of flintknapping occurred at this site. These analyses resulted in the determination of a focus on local raw material, yet this material was being brought to the site as prepared cores or blanks, rather than complete unaltered cores. Tool production was the focus at this site, and this trend continued throughout time.
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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 | 0.007 | 0.008 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".