Determining magnetic susceptibility of ancient and modern potsherds using a fast, cheap, and portable probe
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Archaeometry is the application of scientific techniques to archaeology. The general perception is that it consists of the use of a number of geophysical techniques that were originally developed to explore for mineral deposits in order to delineate anthropological remains in the subsurface without disturbing them. Such techniques include resistivity tomography (e.g., Saey et al., 2012; Glover, 2010; Papadopoulos et al., 2006), proton-spin magnetometry (e.g., Büyüksaraça et al., 2006), ground-penetrating radar (e.g., Dabas et al., 2000; Neubauer et al., 2002), and electromagnetic measurements (e.g., Bigman, 2012). The perception has been fed by television series such as the Time Team, and now many university-based geophysicists spend at least some of their time applying their techniques to archaeological remains. However, these techniques are only one part of archaeometry, which also covers the use of physical and chemical methods to understand how archaeological remains were made and used (e.g., Lima et al., 2012; Cagno et al., 2010) as well as their composition (e.g., Wei, 2012), dating (e.g., Aitken, 1999) and provenance (e.g., Tochilin et al., 2012; Pensabene et al., 2012; Thulman, 2012). In this paper, we consider how the application of a simple magnetic susceptibility measurement may be used to discriminate between potsherds from different sources as well as showing that some aspect of the manufacture or use of the pots leads to the inner and outer surfaces having statistically different magnetic susceptibilities.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it