Combining visual and geochemical analyses to source chert on Southern Baffin Island, Arctic Canada
Bibliographic record
Abstract
Abstract A combined methodological approach using visual and geochemical methods is introduced and preliminary results of a study illustrating its effectiveness to determine chert source provenance are presented. This study focuses on lithic debitage and raw chert samples collected from the interior of southern Baffin Island, Arctic Canada. Chert is abundant throughout this region yet it occurs as small, scattered surface nodules that are highly variable in color. Prior to this study, little was known about the provenance of this local toolstone and whether it derived from local outcrops, glacial till sheets, or both. Given the pronounced variability exhibited by this chert, we use individual attribute analysis and petrography to impose some kind of analytical order upon an otherwise random aggregation of rocks. Thereafter, inductively coupled plasma mass spectrometry and secondary ion mass spectrometry are used to test the validity of these color categories to determine from how many geochemically distinct sources they derive. Using a standard that measures Al to a ratio of Ga/Zr, our results indicate that all of the raw chert samples derive from a single local source, while the debitage derives from four different sources, including the one that is local. We are confident that this combined methodological approach can be applied in other regions where chert variability is pronounced and source provenance is unknown. © 2009 Wiley Periodicals, Inc.
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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 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".