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Livorno città d’acqua e di cultura. Riqualificazione e recupero dell’area del Forte San Pietro d’Alcantara e del Depuratore Rivellino

2024· article· en· W4394865519 on OpenAlexaboutno aff
Erica Princiotta, Laura Simonelli, Luisa Santini, Caterina Calvani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Planning and Valuation
Canadian institutionsnot available
Fundersnot available
KeywordsPort (circuit theory)ArchaeologyFront (military)Quarter (Canadian coin)DemolitionPlan (archaeology)Ancient cityPeninsulaGeographyEngineering

Abstract

fetched live from OpenAlex

The project addresses the topic of reclassifying an urban area of the city of Livorno caracterised by the presence of the Forte San Pietro and the Rivellino di San Marco built at the end of the 17th century to complete Livorno’s northern defensive front protecting the residential quarter of La Venezia. In particular, Forte San Pietro, constructed on the orders of Governor Dal Borro according to the designs of the architect, Baldi, surrounded on one side by the sea and on the other by the Fosso Reale, it was the site of the public slaughterhouse (which was in operation until 1996) from 1889 and this saved it from demolition in the following years, ensuring links with the nearby San Marco’s station and free land for the city’s expansion. This transformed the Rivellino, which was the object of a residential development plan in the early 1800s and the location of the city’s sewage treatment plant. The project area is located in the north-west of the municipal region of Livorno and is configured as a passage area, in which the city environment abruptly changes from the industrial port to the urban and where traces of the ancient fortifications are evident (in addition to Forte San Pietro there are the constituent parts of the Lorenesi city walls) and where a system of canals and cellars is testimony to this particular area of the city’s characteristic special relationship with water. The study was initially focused on the analysis (historical, functional and regulatory) of the regional context so that the characteristic elements of the area being studied were clear and this enabled the objectives to be pursued in the masterplan to be identified.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.003

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 head0.035
GPT teacher head0.261
Teacher spread0.226 · 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

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