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Record W2160738410 · doi:10.1109/mic.2007.46

Pervasive Personal Computing in an Internet Suspend/Resume System

2007· article· en· W2160738410 on OpenAlexaff
Mahadev Satyanarayanan, Ben Gilbert, Matt Toups, Niraj H. Tolia, Ajay Surie, David R. O’Hallaron, Adam Wolbach, Jan Harkes, Adrian Perrig, David J. Farber, Michael A. Kozuch, Casey J. Helfrich, Partho Nath, H. Andrés Lagar-Cavilla

Bibliographic record

VenueIEEE Internet Computing · 2007
Typearticle
Languageen
FieldComputer Science
TopicCloud Computing and Remote Desktop Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceThe InternetOperating systemPersonalizationMobile computingVirtual machineMobile deviceState (computer science)Distributed computingWorld Wide Web

Abstract

fetched live from OpenAlex

The Internet suspend/resume model of mobile computing cuts the tight binding between PC state and PC hardware. By layering a virtual machine on distributed storage, ISR lets the VM encapsulate execution and user customization state; distributed storage then transports that state across space and time. This article explores the implications of ISR for an infrastructure-based approach to mobile computing. It reports on experiences with three versions of ISR and describes work in progress toward the OpenISR version

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.025
GPT teacher head0.274
Teacher spread0.249 · 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 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

Citations112
Published2007
Admission routes1
Has abstractyes

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