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Record W3118205249 · doi:10.23858/fah33.2020.006

From Tower to the Bastion. Changes in Fortress Design to Accommodate Gunpowder Artillery (14th to 16th Centuries)

2020· article· en· W3118205249 on OpenAlexaboutno aff
Christof Krauskopf, Peter Purton

Bibliographic record

VenueFasciculi Archaeologiae Historicae · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEurasian Exchange Networks
Canadian institutionsnot available
Fundersnot available
KeywordsGunpowderArtilleryFortress (chess)TowerArchitectureAncient historyQuarter (Canadian coin)ArchaeologyHistoryPower (physics)AeronauticsEngineering

Abstract

fetched live from OpenAlex

The authors set out the key turning points in the evolution of defensive architecture in response to the appearance of firearms in the 1st quarter of the 14th century in Europe, for both attack and defence. Between the first adaptations to defences during the middle of the 14th century to the emergence of geometric whole defensive systems based on low-lying bastions and interconnected outworks in the 16th century, there was a long period of evolution, experimentation and development, responding to continuous improvement in the range and destructive power of gunpowder artillery. New designs of castles, fortresses and town walls focussed on the need to shield high medieval walls and towers against the power of the gun, but also on how to mount guns on defences and integrate loop holes to keep an attacker as far away as possible. Ideas diffused rapidly across Europe and the Muslim world. Factors such as the builder’s wealth and the purpose of the fortress also determined what was constructed

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.006
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.064
GPT teacher head0.297
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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