MétaCan
Menu
Back to cohort
Record W2024279334 · doi:10.1002/mop.21947

Orthogonal projection sampling method used in reconstruction of incomplete data field

2006· article· en· W2024279334 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMicrowave and Optical Technology Letters · 2006
Typearticle
Languageen
FieldEngineering
TopicOptical Systems and Laser Technology
Canadian institutionsnot available
FundersPolytechnique Montréal
KeywordsSampling (signal processing)Projection (relational algebra)Orthographic projectionComputer scienceField (mathematics)Iterative reconstructionRange (aeronautics)AlgorithmArtificial intelligenceComputer visionMathematicsEngineering

Abstract

fetched live from OpenAlex

Abstract An orthogonal projection sampling method is proposed in this paper for the reconstruction of incomplete data field by using the prior knowledge algorithm based on the modified algebra reconstruction technology (ART) in the optical computerized tomography (OCT). Experiments based to this method are studied. The satisfying reconstruction results are obtained with less sampling projection numbers and limited sampling angular range. The results indicate that orthogonal projection sampling can improve the precision in the reconstruction of incomplete data, and the precision reaches 10%. © 2006 Wiley Periodicals,Inc. Microwave Opt Technol Lett 48:2333–2336, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.21947

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.480

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 head0.020
GPT teacher head0.253
Teacher spread0.232 · 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