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Record W2800425114 · doi:10.26483/ijarcs.v9i2.5506

REDUNDANCY CHECKS ON ARCHITECTURE FOR CLIENT SYSTEMS

2018· article· en· W2800425114 on OpenAlex
A. P. Nirmala

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

VenueInternational Journal of Advanced Research in Computer Science · 2018
Typearticle
Languageen
FieldComputer Science
TopicSoftware System Performance and Reliability
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsComputer scienceTroubleshootingRedundancy (engineering)UnixServerProcess (computing)EthernetArchitectureOperating system

Abstract

fetched live from OpenAlex

The availability (uptime) of UNIX servers which are hosting critical patient care applications is of utmost importance. This availability is ensured by maintaining redundant network; storage connections to a physical server (Ethernet cables; Optical Fibres). The health of these redundant connections, are monitored daily, so that issues can be fixed proactively, which ensures the availability of the servers. If issues are identified with these connections, the concerned team is involved in troubleshooting the issue. Due to the critical nature of the project, perfect ITIL(Information Technology Infrastructure Library) procedures should be followed in identifying, recording and fixing the issues. Unfortunately, this requires a lot of manual work. To help reduce this manual labour and save time, a Web Application was proposed to be developed. This web application will be useful to track, monitor, and analyze the work progress and to visualize the data for easy understanding. It reduces errors by reducing human interaction in the process, thereby saving valuable labour time and resources for the organization. Thus, the generated report becomes more accurate.

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)0.006
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.891
Threshold uncertainty score0.820

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0040.001
Research integrity0.0000.000
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 head0.051
GPT teacher head0.413
Teacher spread0.362 · 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