To Measure or not to Measure: Geochemical Analysis of Siliceous Materials in the Interior Plateau of British Columbia
Bibliographic record
Abstract
Lithic sourcing studies remain one of the most effective ways to establish past trade networks and cross cultural connections within a region. The nature of trade within the British Columbian Interior Plateau may be assessed by analysing chert débitage excavated from Structure 109 at Keatley Creek and its archaeological and geological origins/quarries. This paper discusses both the potential and limitations of a current lithic sourcing study that is attempting to utilize petrographic and geochemical methods to characterize and correlate chert and chalcedony artefacts to their geologic sources. s a multidisciplinary field archaeology draws from a wide range of methods and techniques. Although this is beneficial for the majority of archaeologists, it also has the potential to lead to the misuse of technology. There are limits to the usefulness of techniques used by natural sciences such as geology and chemistry to understand archaeological problems. The focus of this paper is to explore the capabilities and limitations of geochemical analysis. To do so, I will discuss geoarchaeology and its relevance to archaeological inquiry, the importance of geochemical characterization studies, the potentiality and constraints of geochemical analysis, and conclude with an in-progress case study from the Keatley Creek site, British Columbia, Canada. Archaeology and Earth Sciences
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".