MétaCan
Menu
Back to cohort
Record W4414279840 · doi:10.1029/2025gl119254

Four-dimensional generalization of ensemble singular vector: Formulation and experiments with the Lorenz 96 model

2025· preprint· en· W4414279840 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGeophysical Research Letters · 2025
Typepreprint
Languageen
FieldEngineering
TopicControl Systems and Identification
Canadian institutionsEnvironment and Climate Change Canada
FundersJapan Society for the Promotion of ScienceNational Central UniversityResearch Organization of Information and Systems
KeywordsGeneralizationSensitivity (control systems)Measure (data warehouse)Singular valueSimple (philosophy)

Abstract

fetched live from OpenAlex

Abstract Motivated by the need to extend sensitivity analysis beyond spatial variations to include temporal evolution, we propose a four‐dimensional generalization to the ensemble singular vector approach, termed 4DEnSV. This generalization enables user‐defined norms that flexibly target spatiotemporal evolutions of interest. Experiments with the Lorenz '96 model demonstrate that 4DEnSV successfully identifies perturbations yielding the largest response under a user‐defined norm. By defining norms to reflect temporal objectives, 4DEnSV can extract initial perturbations responsible for specific temporal changes, such as shifts in peak timing. The proposed method offers a novel framework for sensitivity analysis and related applications, particularly for understanding the temporal evolution of weather systems.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.516
Threshold uncertainty score0.470

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.043
GPT teacher head0.297
Teacher spread0.254 · 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