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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 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.972
Threshold uncertainty score0.475

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.046
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