Identification of Macrofossils within Stone Tools: a possibility for tracing the source of artifacts?
Bibliographic record
Abstract
This paper discusses the application of malacological identification of macrofossils in stone tools. A macroscopically distinct toolstone utilized by prehistoric peoples, reported widely in archaeological consulting literature across central and southern Alberta (Meyer et al. 2007; de Mille 2009; Bohach 2010; Porter 2014), features fossilized root traces and occasional large fossil shells. These fossils can be identified, and correlated with temporal and geologic formations indicative of the environments within which the taxa occurred. Artifacts with fossils morphologically coherent with Hydrobia, Lioplacodes, and Viviparus spp. are identified in stone artifacts analyzed in this paper. These taxa are consistent with depositional environments of Paleocene period Paskapoo Formation sedimentary rocks, particularly, as identified at the Blindman-Red Deer River confluence and Joffre roadcut paleontological localities (Hoffman and Stockey 2011). In this paper we explore how the identification of these fossils offer clues to the procurement areas which were sought out by prehistoric toolmakers. We do not suggest that all Red Deer Mudstone is from these localities, though the fossil molluscs presented so far do not refute this conclusion, but we do suggest that identifying large fossil shells can be a critical diagnostic tool for identifying the geologic origin of artifacts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| 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".