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Record W7134514747

Ammaedara (Haïdra, Tunisia) and its Territory : Study of an Ancient North Africa City

2012· article· fr· W7134514747 on OpenAlexaboutno aff
Elsa Rocca

Bibliographic record

Venuenot available
Typearticle
Languagefr
FieldArts and Humanities
TopicArchaeology and Historical Studies
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)ESPACEAntiqueQuarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

La ville antique d’Ammaedara (aujourd’hui Haïdra) est située dans le centre ouest de la Tunisie, à proximité de la frontière algérienne. Nous nous proposons d’étudier dans cette thèse l’évolution de la colonie d’Ammaedara, issue du camp de la IIIe Legio Augusta, depuis sa fondation au Ier siècle après J.-C. jusqu’à la conquête arabe à la fin du VIIe siècle, à partir des données archéologiques et historiques. L’examen de la documentation ancienne (plans, clichés aériens) et l’acquisition de nouvelles données de terrain (relevés topographiques, prospections sur le site et la campagne), nous permettrons d’étudier l’évolution de l’occupation urbaine et rurale ; l’analyse s’appuie sur un SIG (Système d’Information Géographique), qui permet le traitement et l’analyse des données spatialisées. L’évolution de la topographie urbaine (contexte de l’implantation de l’agglomération, occupation et évolution de l’espace urbain, réseau hydraulique, limites urbaines) et le rapport entre la ville et sa proche campagne (limites du territoire, occupation des faubourgs, approvisionnement) constituent nos principales thématiques d’étude. Nous livrons une synthèse sur la longue durée qui dresse l’état des connaissances sur le site et replace dans son contexte régional et historique l’évolution de la ville aux périodes romaine, vandale et byzantine.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.283

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0050.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.079
GPT teacher head0.246
Teacher spread0.167 · 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 designObservational
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
Published2012
Admission routes1
Has abstractyes

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