INAA OF ARCHAEOLOGICAL SAMPLES AT THE UNIVERSITY OF MANCHESTER
Bibliographic record
Abstract
INAA at Manchester began almost by chance, and amid scepticism, with a request for help from the university's Department of Archaeology in the early 1970s. Over the years, the method of selecting pottery to be sampled was refined from the simple assumption that sherds found at a site were typical of those made there, to a greater focus on kiln sites and wasters. An important element in Manchester's approach was that this was a teaching department: in the quarter‐century during which the laboratory practised INAA over 6000 samples were analysed in a wide range of projects by postdoctoral researchers, Ph.D. students and even final‐year undergraduates. Although there were sometimes problems of comprehension on both sides, close collaboration with archaeologists encouraged methodological comparisons, and often INAA was seen as an additional weapon in the archaeologists’ arsenal as much as a developing scientific technique. The university's place as the inventor of the computer encouraged the development of statistical programs, which in turn facilitated ready collaboration and exchange of information with other laboratories, such as those at Bonn and Berkeley.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.014 |
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".