MétaCan
Menu
Back to cohort
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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.565
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.071
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; both teacher heads agree on what is shown here.

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

Explore more

Same venueAtiqotSame topicEurasian Exchange NetworksFrench-language works237,207