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Record W3210117085 · doi:10.23977/acss.2021.050118

Research on Server Installation Direction Based on Newton's Heat Transfer Law

2021· article· en· W3210117085 on OpenAlexvenueno aff
Hao Zhang, Guanghua Li

Bibliographic record

VenueAdvances in Computer Signals and Systems · 2021
Typearticle
Languageen
FieldComputer Science
TopicComputational Physics and Python Applications
Canadian institutionsnot available
Fundersnot available
KeywordsServerContainer (type theory)DissipationHeat transferComputer sciencePremiseMechanicsEngineeringMechanical engineeringComputer networkPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Nowadays, the storage of Seabed Data Center has become a new data storage technology, which has attracted much attention all over the world. Firstly, through the study of Newton's heat transfer law, it is analyzed that the premise of heat dissipation is the temperature difference inside and outside the solid container. On the basis of meeting this condition, the maximum installable quantity caused by the different arrangement of different servers in the container is calculated. The results show that when the server is placed in a horizontal form inside the container, the maximum number of 330 servers of the same type can be placed at the same time, while when it is placed in a vertical manner, the number of servers is only 269. From the comparison of the above conclusions, it can be seen that the horizontal installation method is more suitable for the internal heat dissipation of the server.

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)0.003
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 score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.046
GPT teacher head0.335
Teacher spread0.289 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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