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Record W3171302313 · doi:10.21428/bf6fb269.8b56b095

Design Aspirations for Energy Autarkic Information Systems in a Future with Limits

2021· article· en· W3171302313 on OpenAlexaff
Brian Sutherland

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGreen IT and Sustainability
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSustainabilitySet (abstract data type)Energy systemEnergy (signal processing)Renewable energyValue (mathematics)Environmental economicsEconomic systemEconomicsBusinessComputer scienceEcologyMathematics

Abstract

fetched live from OpenAlex

An information system is energy autarkic if its operation is integrally sustained by an ambient energy source, typically renewable, for long periods of time. An energy autarkic system's sustainability increases if the environmental cost for its design, material, construction, repair and recycling is minimal, the social value of its creation, use and disposal is widely justifiable over time and diverse contexts, and it has further potential as a usermodified, upcycled, or salvage system. In this essay I discuss a number of past solar-powered information system designs, comparing them with contemporary solar autarkic prototypes, reflecting on their actors, their histories and their socialtechnical trajectories. I conclude by proposing a set of aspirational design characteristics for sustainable autarkic computing systems, in as much as they represent a class of transitional computing aligned to a future with limits.

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)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.015
Scholarly communication0.0100.012
Open science0.0010.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.012
GPT teacher head0.184
Teacher spread0.172 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations1
Published2021
Admission routes1
Has abstractyes

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