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Record W7117893253 · doi:10.21227/vy7j-9j47

"The Waterloo Exploration Database"

2025· dataset· W7117893253 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
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

VenueIEEE DataPort · 2025
Typedataset
Language
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsImage qualityImage (mathematics)GeneralizationQuality assessmentImage processingQuality (philosophy)Digital image processingDigital image

Abstract

fetched live from OpenAlex

"The Waterloo Exploration Database is a large-scale Image Quality Assessment (IQA) database created by the University of Waterloo, Canada \"Waterloo Exploration Database: New Challenges for Image Quality Assessment Models | IEEE Journals & Magazine | IEEE Xplore\". It is designed to capture the challenges posed by the diversity of real-world digital image content and to evaluate the generalization capability of image quality assessment models. The database contains 4,744 pristine natural images and 94,880 distorted images generated from these originals. In this work, we extract the pristine images from the database for image processing tasks."

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.055
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.003
Science and technology studies0.0040.001
Scholarly communication0.0030.006
Open science0.0100.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.057

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 head0.041
GPT teacher head0.311
Teacher spread0.271 · 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

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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