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Record W7143985400 · doi:10.15083/0002005315

ニューラル機械翻訳のための文構造を考慮した潜在変数モデル

2020· dissertation· en· W7143985400 on OpenAlexfundno aff
Z. Q. Zhu

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2020
Typedissertation
Languageen
FieldEngineering
TopicMilitary Technology and Strategies
Canadian institutionsnot available
FundersYork University
KeywordsProcess (computing)Work (physics)Product (mathematics)Set (abstract data type)Information system

Abstract

fetched live from OpenAlex

UTokyo Repositoryは本学で生産されたさまざまな学術成果を電子的形態で集中的に蓄積・保存し、世界に発信することを目的としたインターネット上の発信拠点です。 The UTokyo Repository is the system to store and provide digital resources created by members of the University of Tokyo. Its main purpose is to develop digital collections, make them available online, and preserve them for long-term access.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.073
Threshold uncertainty score0.244

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.002
Scholarly communication0.0070.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0730.037

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 head0.010
GPT teacher head0.228
Teacher spread0.217 · 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
Published2020
Admission routes1
Has abstractyes

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