MétaCan
Menu
Back to cohort
Record W2018668294 · doi:10.1109/issst.2011.5936894

Rapid integrated life cycle assessment of building-wide IT systems

2011· article· en· W2018668294 on OpenAlexafffund
Paul Teehan, Stefan Storey, Milind Kandlikar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGreen IT and Sustainability
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsSustainabilityComputer scienceServerMandateData centerOccupancyLife-cycle cost analysisSystems engineeringTelecommunicationsArchitectural engineeringEngineeringReliability engineeringWorld Wide WebComputer network

Abstract

fetched live from OpenAlex

An integrated life cycle environmental and cost analysis of building-wide IT systems was performed for the University of British Columbia's Centre for Interactive Research on Sustainability (CIRS), a new green building currently under construction and scheduled for occupancy in summer 2011. Because the CIRS project has a mandate to choose environmentally preferable alternatives wherever possible, the CIRS team commissioned this study in order to enumerate possible technology systems to be installed and to assess them in terms of environmental performance, cost, and suitability for the building occupants. The team was particularly interested in investigating the possible environmental benefits of thin client computing devices, which replace desktops and laptops by hosting computing sessions on remote servers, and of all-wireless network systems which would obviate network cabling. Several potential building-wide IT systems were assessed, with each system comprising a different collection of products compatible with these high-level technology decisions. The analysis included production and use impacts based on studies in the Ecolnvent database; end-of-life was excluded because it represents a minimal share of the impact in these studies.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.735
Threshold uncertainty score0.558

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)0.0010.000

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 head0.020
GPT teacher head0.238
Teacher spread0.218 · 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 designSimulation or modeling
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

Citations2
Published2011
Admission routes2
Has abstractyes

Explore more

Same topicGreen IT and SustainabilityFrench-language works237,207