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Record W4405335259 · doi:10.70967/2948-040x.1070

Trebuchets Were Not Siege Guns, So Why Use Them?

2024· article· en· W4405335259 on OpenAlexaff
Michael S. Fulton

Bibliographic record

VenueAtiqot · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEurasian Exchange Networks
Canadian institutionsWestern University
Fundersnot available
KeywordsSiegeCounterweightPower (physics)Value (mathematics)ArtilleryPrestigeLawEngineeringPolitical scienceSociologyForensic engineeringHistoryComputer sciencePhilosophyMechanical engineeringAncient historyArtificial intelligence

Abstract

fetched live from OpenAlex

Counterweight trebuchets were the most powerful ballistic weapons of their day, but their association with later siege guns has led to misunderstandings of their destructive capabilities. This article is an attempt to correct some misguided ideas by providing a short overview of the power and value of mechanical artillery and contextualizing its use in the Levant during the twelfth and thirteenth centuries. After addressing the origin of certain theories that these engines were far more destructive than seems to have been the reality, a brief assessment of their actual power is conducted by looking at the ways sources exaggerate the destructive capabilities of these weapons and exploring their capacity as energy systems. The notion that counterweight trebuchets were responsible for a ‘revolution’ in fortification design from the late twelfth century is then addressed. Building on the notion that these machines were less powerful than some have supposed, an assessment of their value is offered by exploring their relative strength, the psychological impact associated with the dangers they posed, and the prestige attached to employing such great engines.

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)0.003
metaresearch head score (Gemma)0.006
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.007
Scholarly communication0.0040.009
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.002

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 head0.071
GPT teacher head0.326
Teacher spread0.255 · 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
GenreEmpirical

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

Citations0
Published2024
Admission routes1
Has abstractyes

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