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Record W17444570 · doi:10.1002/smll.200600727

Characterizing Computer Systems' Workloads

2002· article· en· W17444570 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware System Performance and Reliability
Canadian institutionsQueen's University
Fundersnot available
KeywordsWorkloadComputer scienceProcess (computing)Set (abstract data type)Characterization (materials science)Distributed computingOperating system

Abstract

fetched live from OpenAlex

There are a variety of methods for synthesizing or fabricating one-dimensional (1D) nanostructures containing heterojunctions between different materials. Here we review recent developments in the synthesis and fabrication of heterojunctions formed between different materials within the same 1D nanostructure or between different 1D nanostructures composed of different materials. Structures containing 1D nanoscale heterojunctions exhibit interesting chemistry as well as size, shape, and material-dependent properties that are unique when compared to single-component materials. This leads to new or enhanced properties or multifunctionality useful for a variety of applications in electronics, photonics, catalysis, and sensing, for example. This review separates the methods into vapor-phase synthesis, solution-phase synthesis, template-based synthesis, and other approaches, such as lithography, electrospinning, and assembly. These methods are used to form a variety of heterojunctions, including segmented, core/shell, branched, or crossed, from different combinations of semiconductor, metal, carbon, and polymeric materials.

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.

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 categoriesInsufficient payload (model declined to judge)
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 score0.927
Threshold uncertainty score0.999

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.019
GPT teacher head0.208
Teacher spread0.189 · 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