Two firings of the bronze vessel casting moulds: Distinction between firing and casting of the Late Shang and Western Zhou silt moulds
Bibliographic record
Abstract
This study aims to differentiate between the firing and casting processes of the Late Shang and Western Zhou bronze vessel casting moulds. While previous research has primarily focused on the moulds' firing temperature and heat resistance, this study delves deeper into the distinct stages of firing and casting. By analyzing the three mould types housed at the Royal Ontario Museum (ROM) using petrography and scanning electron microscope (SEM), this research introduces a new method to distinguish between moulds that were later used and those discarded owing to some imperfections. The study also highlights the variations in firing atmosphere and technological preferences of the artisans, shedding light on the complex processes involved in bronze casting mould production. The findings provide valuable insights into the organization of bronze casting and the technological advancements of the Late Shang and Western Zhou dynasties. • Identifying firing and casting methods • Petrographic and scanning electron microscope analysis on heat alteration. • Chinese bronze casting technology • Reaction of heat to silt rich material
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".