Preliminary results on the applicability of neutron activation analysis (NAA) to identify cherts from the Munsungun Lake Formation, Maine, USA
Bibliographic record
Abstract
Abstract Red chert attributed to the Munsungun Lake Formation, Maine, USA is common in late Pleistocene fluted‐point‐period archaeological sites located throughout the New England states and Quebec, appearing more frequently than any other material type in the region. Despite the assumed association between red Munsungun chert and fluted‐point‐period sites, until recently, it was not possible to link red chert artifacts from these sites to a specific source area within the Munsungun Lake Formation because outcrops of this material associated with direct evidence of past use were not documented. Here, we report the first results of a neutron activation analysis (NAA) study of red Munsungun chert from two quarry areas within the Munsungun Lake Formation. These results suggest that NAA can distinguish between chert source areas within the Munsungun Lake Formation and lookalike materials from the wider region. Additional analyses are required to include more comparative samples and evaluate the efficacy of less destructive geochemical techniques in characterizing cherts from the region. Despite the need for additional research, these results suggest that NAA will be useful for re‐evaluating past identifications of chert from the Munsungun Lake Formation, providing an important foundation for additional geochemical research in the region.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".