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Record W4200549712 · doi:10.3389/fnbot.2021.808369

Editorial: Intelligence and Safety for Humanoid Robots: Design, Control, and Applications

2021· editorial· en· W4200549712 on OpenAlexaff
Zhaoquan Gu, Keke Tang, Zheng Wang, Yanhua Li, Zhihong Tian

Bibliographic record

VenueFrontiers in Neurorobotics · 2021
Typeeditorial
Languageen
FieldComputer Science
TopicAdversarial Robustness in Machine Learning
Canadian institutionsCarleton University
FundersBasic and Applied Basic Research Foundation of Guangdong ProvinceSouthern University of Science and TechnologyNational Natural Science Foundation of China
KeywordsComputer scienceHumanoid robotRobotControl (management)Front (military)Computer securityHuman–computer interactionArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

EDITORIAL article Front. Neurorobot., 20 December 2021 | https://doi.org/10.3389/fnbot.2021.808369

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)0.004
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.033
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0030.001
Science and technology studies0.0030.002
Scholarly communication0.0060.004
Open science0.0030.001
Research integrity0.0110.012
Insufficient payload (model declined to judge)0.0330.029

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 head0.012
GPT teacher head0.261
Teacher spread0.249 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations1
Published2021
Admission routes1
Has abstractyes

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