Spatial Patterning of Artefacts Using Variable Hierarchical Clustering and Bivariate Spatial Autocorrelation: A Case Study in Williston Reservoir, British Columbia
Bibliographic record
Abstract
with parameters set a priori, despite that contexts may not be always available.The spatial arrangement of the artefacts found or excavated at a site is likely to have spatial dependency among them, and the scale of activities (e.g. an individual engaging in a house task, or a community taking part in a social activity) will likely vary between the types of activities and the participants involved which are difficult to calibrate without stratigraphic narratives or known features that offer the context.The objective of this study is to identify spatial associations between different types of archaeological artefacts found across an excavation site and gain knowledge on the spatial configuration and the lifestyle of the ancient community that lived in this area.To overcome the challenges stated above, this study uses two types of spatial analytical methods that can extract the hierarchical spatial structure: (1) a local variant of a spatial autocorrelation method called Multivariate Local Indicators of Spatial Association (LISA) [4], and (2) a hierarchical cluster detection method called Variable Clumping Method (VCM) [5,6].Clustering helps to simplify a large archaeological dataset to make spatial patterns easier to discern; but they need to be arranged at suitable scales with the recognition of the multi-scale across different levels of activities.Finding clusters in the distribution of the artefacts across multiple scales would provide more natural groupings for use in the subsequent analysis.Exploration of the spatial relationships between different types of artefacts through LISA could provide us with a clue to infer activities that took place AbstractArtefacts in the Williston Reservoir, British Columbia were collected and recorded by an archaeological firm over several years.While a large and extensive dataset of the locations of these artefacts was built up, several challenges to management and interpretation of the use of the landscape are presented.This study used the Variable Clumping Method (VCM) hierarchical clustering technique to detect clusters of artefacts for each object type.These detected clusters were then used for an object-type spatial correlation analysis, using Multivariate Local Indicators of Spatial Association (LISA).The results from LISA analysis included detection of significant global and local associations between several object-type pairs.For instance, strong spatial correlation was found between Scrapers and Points, which may suggest significant use areas, such as regularly used butchering sites or campsites.While it is not possible to draw a definitive conclusion of exactly what these relationships mean in terms of landscape use, they suggest a number of interesting hypotheses of possible uses of the area in the ancient time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".