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Toward Resilient, Inclusive and Vital Technological Infrastructures for the Energies of the Landscape

2016· article· en· W2567237568 on OpenAlexvenueno aff
Filippo Angelucci

Bibliographic record

VenueJournal of Technology Innovations in Renewable Energy · 2016
Typearticle
Languageen
FieldEngineering
TopicSmart Cities and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsResilience (materials science)Architectural engineeringEnvironmental resource managementEnvironmental scienceComputer scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

Today, the implementation of energy infrastructure needs a complex â"dialogue" between two apparently different aspects: technical aspects that characterize the processes of production and transformation of energy for the territory, and socio-ecological aspects related to the biological, organizational, and economic variables for cultural, creative, and productive energies of the territory. This new dimension of designing and building the energy infrastructures replaces to the classic esthetical idea of the landscape an integrated vision of human habitat in which innovations play a key role for the redefinition of relationships between téchne, bios and oikos. On these subjects, this article proposes a reflection on a methodological approach to re-think energy infrastructures as technological-environmental interfaces between land resources, energy needs, living dynamics, and inhabiting practices of territories and cities. Four possible scenarios are presented starting from some research experiences developed on Italian Abruzzo region. Through these experiences, emerges a new strategic, tactic and operational framework for the design of energy infrastructures. A new design framework in which energy infrastructures can contribute to re-build relations and connections between the acceptance/correlation of technological innovations and resilience, inclusiveness and vitality of the landscape.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score0.228

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.217
Teacher spread0.209 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2016
Admission routes1
Has abstractyes

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