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What is the Maximum Power Output of NEPTUNE?

2024· article· en· W4404688597 on OpenAlexaff
Deg Hembroff, Shane Kerschtien

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsOcean Networks Canada Society
Fundersnot available
KeywordsNeptunePower (physics)Computer scienceElectrical engineeringPhysicsEngineeringAstronomyPlanet

Abstract

fetched live from OpenAlex

The NEPTUNE cabled observatory commenced operations in 2009 as the world's first regional-scale seafloor infrastructure providing long-term continuous power and data connectivity for ocean instrumentation. The observatory's power system was designed for resiliency to faults and with sufficient capacity to allow expansion to new locations with higher power loads. In its lifetime there has been a steady increase in quantities of oceanographic instrumentation, but these devices are typically designed to consume minimal power. The impacts of a concentrated power load are not well understood because the observatory has historically only used small fraction of available power, and because the network is not a trivial linear DC circuit. The Pacific Ocean Neutrino Experiment project (P-ONE) proposes adding a neutrino detector to the observatory which will create substantial power demands at one instrumented site. This paper investigates the theoretical power limit for P-ONE and the stability impact on NEPTUNE by exploring systemic failure mechanisms.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.011
GPT teacher head0.226
Teacher spread0.215 · 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 designBench or experimental
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
Published2024
Admission routes1
Has abstractyes

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