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Record W4232598951 · doi:10.1145/1454609

Proceedings of the 5th ACM symposium on Performance evaluation of wireless ad hoc, sensor, and ubiquitous networks

2008· paratext· en· W4232598951 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typeparatext
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsnot available
Fundersnot available
KeywordsWireless ad hoc networkComputer scienceWireless sensor networkVariety (cybernetics)PleasureUbiquitous computingField (mathematics)Vehicular ad hoc networkWirelessWorld Wide WebTelecommunicationsData scienceComputer networkArtificial intelligenceHuman–computer interaction

Abstract

fetched live from OpenAlex

It is our great pleasure to welcome you to the 5th ACM International Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks (PE-WASUN'08) that takes place in Vancouver, Canada on October 27-28, 2008. This year's ACM PE-WASUM symposium brings together scientists, engineers, and practitioners to share and exchange their experiences, discuss challenges, and report state-of-the-art and in-progress research on all aspects of wireless ad hoc, sensor, and ubiquitous networks with a specific emphasis on their performance evaluation and analysis. The mission of the symposium is to share novel approaches for monitoring, measuring, modeling, simulating, analyzing, optimizing and case-studying the characteristics of ad hoc, sensor, pervasive and ubiquitous networks, as well as exploring and developing new ad hoc networking protocols and tools. ACM PE-WASUN gives researchers and practitioners a unique opportunity to share their perspectives with others interested in the various aspects of performance evaluation. The call for papers attracted 42 submissions from Europe, Asia, the North and South America and the Middle East. The technical program committee accepted 10 long papers and 6 short papers that cover a variety of topics, including experimentations, tools for performance evaluation, models, performance evaluation of routing and MAC protocols. We hope that these proceedings will serve as valuable references for researchers and developers in the field of wireless ad hoc, sensor and ubiquitous networks.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.001
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 head0.023
GPT teacher head0.250
Teacher spread0.227 · 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.

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

Citations1
Published2008
Admission routes1
Has abstractyes

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