MétaCan
Menu
Back to cohort
Record W2992201974

Secondhand Is First-Rate: Don't Be Deterred by the Stigma of Buying Used. Refurbished Computers Can Offer Better Value and Performance Than New Units, While Lessening IT's Environmental Footprint

2010· article· en· W2992201974 on OpenAlexaboutno aff
Jennifer Demski

Bibliographic record

VenueT.H.E. Journal Technological Horizons in Education · 2010
Typearticle
Languageen
FieldEngineering
TopicGreen IT and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsEngineeringFactory (object-oriented programming)Life-cycle assessmentEcological footprintProduct (mathematics)Operations managementProduction (economics)Computer scienceSustainabilityEconomics
DOInot available

Abstract

fetched live from OpenAlex

CONSIDER THIS: BEFORE YOU PRESS the power button a brand-new computer for the first time, it has already used almost i percent of the energy it will consume over its lifetime. Eric Williams, an assistant professor at the Center for Earth Systems Engineering Management at Arizona State University, has been researching life cycle assessments of IT machines since 2000. Life cycle assessments measure the total environmental impact of a product or service, from the resources used in its manufacturing to the energy consumed during its intended operation, all the way through to the method of its disposal. you do a life cycle assessment of an automobile, Williams says, what you'll find is that 95 percent of the energy consumed will be from driving it. other 5 percent will be in the manufacturing--the steel, the plastic, the different parts that need to be made assembled. So gas mileage is going to be a huge factor in how environmentally friendly it is. Computers operate differently, Williams explains. require a far more intensive process in the manufacturing stage. Part of this is due to the high-tech components scrupulous environmental conditions required to build one. Anything that gets near semiconductors microprocessor chips has to be immaculate. It takes energy, chemicals, processing to make the chemicals, gasses, water that touch the unit that pure, Williams says, and to keep them that pure. [ILLUSTRATION OMITTED] you've ever seen the Intel commercials where the workers are wearing clean suits dancing around in a mock factory setting, you should know that those clean suits aren't worn to protect the workers, but to protect the product from the workers. Williams says research shows that the care put into this manufacturing phase accounts for 70 to 80 percent of a computer's energy use over its life cycle. You could, like buying a car with better gas mileage, try to buy an Energy Star Williams says. The problem is, that only addresses 20 to 30 percent of the computer's energy consumption, you haven't done anything to address that larger 70 to 80 percent of consumption that occurs during manufacturing. If you take the strategy of extending the life of the computer--instead of buying a new computer, buy a used one--then you've eliminated the need to manufacture that new computer, at least for a while. energy savings are significant. idea of reusing a computer may bring to mind struggling with a dusty old Commodore 64 that's bogged down by the previous user's data. But that association no longer sticks. Saar Pikar, senior vice president general manager of Ontario, Canada-based CDI Computers, one of the largest providers of refurbished computers to the education market, sold more than 300,000 refurbished computers to school districts in the US, Canada, the UK in 2009 alone. The computers that we sell are usually between six 24 months old, Pikar says. They come from more than 250 sources--leasing brokers, original manufacturers, large Fortune 500 companies. have a lot of life left in them. When a computer arrives at CDI, it is put through a 26-step process during which a team of certified technicians cleans the data from its hard drive, cleans the hardware, removes all stickers identifying marks from the previous owner, then tests audits the unit. Any issues that arise are immediately fixed, bringing the computer back up to its initial factory specifications. Once sold, the unit is returned to the assembly line, where the hardware is again cleaned then upgraded to the specs requested by the customer--at an extra charge, Pikar says, only if those specs require more hardware. Most schools that work with us in the US give us a full software image of their existing computers, Pikar says. We load that image onto the new unit, so when they receive it they just have to plug it in off they go. …

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.637
Threshold uncertainty score0.596

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 integrity0.0000.001
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 head0.012
GPT teacher head0.213
Teacher spread0.201 · 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 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
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueT.H.E. Journal Technological Horizons in EducationSame topicGreen IT and SustainabilityFrench-language works237,207