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Record W7075997315

tCURLoRA: tensor CUR decomposition based low-rank parameter adaptation and its application in medical image segmentation

2025· other· en· W7075997315 on OpenAlexfundno aff

Bibliographic record

VenueePrints Soton (University of Southampton) · 2025
Typeother
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchNational Institutes of HealthH. Lundbeck A/SServierShaoxing UniversityEisaiGenentechIXICONational Natural Science Foundation of ChinaNorthern California Institute for Research and EducationPfizerNovartis Pharmaceuticals CorporationBiogenEli Lilly and CompanyBioClinicaAlzheimer's Disease Neuroimaging InitiativeMeso Scale DiagnosticsAlzheimer's AssociationFoundation for the National Institutes of Health
KeywordsTensor (intrinsic definition)Representation (politics)LimitingComputational complexity theoryArtificial neural networkSegmentationImage (mathematics)Adaptation (eye)Decomposition
DOInot available

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.005
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: Methods · Consensus signal: Methods
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.010
GPT teacher head0.225
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
GenreMethods

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
Published2025
Admission routes1
Has abstractyes

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