Bibliographic record
Abstract
Mining and metallurgy experienced decisive changes linked to available geological possibilities and the political, economic, and cultural transformation of Europe. Extraction of lead, iron, silver, copper, gold, and other metals led to continual adaptations of water-pumping machines and blast furnace technology. With successes and failures, miners adapted wind-blown hearths, bellows-blown furnaces, and cupellation techniques to a whole range of crude ores. Techniques spread from one mining district to the next, expanding metal production in towns of the Black Forest, Devon, Alsace, the Alps and the Dinaric Alps, the Carpathians, the Ore Rich Hills of Tuscany, Mediterranean islands, the Pyrenees, and the Iberian Baetic Cordillera. Over the last decades, historians have emphasized the role of demographic and income expansion in the success of mining and metallurgy. Increasing demand for African gold, Chinese silk, and other Asian goods by European ruling classes and merchants created stimulus for intensifying mining and metallurgical activities. After the Spanish conquest of the Americas, a new method for treating silver ores emerged in Mexico, radically expanding the metallurgical possibilities of mining districts. A corpus of knowledge of mining and metallurgy emerged, distilling, absorbing, and clashing with alchemical views that refused to die easily. Mining bureaucracies, royal mine officers, and mining financiers appeared everywhere competition for mines and metals emerged in a European economy increasingly dependent upon mines, metals, and a changing mining and metallurgical industry.
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".