Bibliographic record
Abstract
Several examples of the use of chemical methods to solve questions such as the effects of soil contamination, the use, the provenance, and the manufacturing of clay-based ceramics are presented. As illustrated by the element phosphorus, it is possible for chemical components to migrate into and out of a broken pot during the burial stage. The analysis of food residues, such as crusts on or organic substances in the ceramic piece shows that the medieval population in England ate mainly cabbage, whereas the Indians of Manitoba (Canada) relied on a diet of fish and animal fats during the 10th–16th centuries. The chemical analysis of the unusually large bricks produced by Cistercian monks in Switzerland during the 13th century gives evidence of the existence of other manufacturing places, such as Fraubrunnen and Frienisberg, in addition to the main factory located at the monastery of St. Urban. Some 18th to 19th century Swiss fayences from the Matzendorf and Kilchberg-Schooren production sites are not easy to attribute to a specific center based on stylistic arguments alone, but they can be clearly differentiated in their chemical composition. Furthermore, scientific evidence suggests that artisans moved from Kilchberg-Schooren to Matzendorf, and vice versa. As exemplified by the so-called 'glossy clay layer', surprising results show that not only the mineralogical and chemical composition of the clay layer, but also the CaO content of the ceramic body, has a significant influence on the gloss – a fact that was well known to ancient Roman potters.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".