Bibliographic record
Abstract
This 10 th volume of the Oblicza Wojny (Faces of War) series, contains 12 papers written by archaeologists and historians from Czechia, Greece, Canada, Germany, Poland, Hungary, and Italy.Such a team of researchers not only ensures an interdisciplinary approach to the issue at hand but also guarantees a multifaceted approach.All papers address the problem named in the title of this volume: The Tools of War.We can distinguish several levels of research undertaken by the Authors: themes related to the armaments of particular armies and fortification systems; references to military formations and the impact of their development on the battlefield; diplomacy as a tool during war, and less obvious issues: money, bicycle, and even a lekythos.Coming from the perspective of the classical understanding of tools as a means of warfare, Zoltan Szolnoki looked at battles fought among the members of the conflicted Cancelerii family that influenced the development of Florence and the surrounding region.By analysing the chronicles from that period, he identified not only the phases of the fighting and its intensity but also the weapons used by the parties to the conflict, concluding that as time passed, the vendetta became more and more brutal.Simone Picchianti, on the other hand, treated the war between Florence and Lucca in the first half of the 15 th century as a backdrop for his paper, in which he presented a highly organised system for the production of crossbow bolts that enabled their constant supply to the Florentine troops and thus ensured their effectiveness.Whereas Manouchehr Moshtagh Khorasani analyses a Persian manuscript (probably from the 17 th century) indicating that this source provides invaluable information on how to make crucible steel blades, how to identify and classify swords, how to make the adhesive glue for attaching the blade tang to the handle of the sword, how to make glue for
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.428 | 0.262 |
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".