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Record W4393722193 · doi:10.5281/zenodo.8432593

Code and trained model for Keeping an "Eye" on the Experiment: Computer Vision for Real-Time Monitoring and Control

2023· dataset· en· W4393722193 on OpenAlexaff
Rama El-khawaldeh, Mason Guy, Finn Bork, Nina Taherimakhsousi, Kris Jones, Joel M. Hawkins, Lu Han, Robert Pritchard, Sébastien Monfette, Jason E. Hein

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typedataset
Languageen
FieldEngineering
TopicImage Processing Techniques and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceCode (set theory)Control (management)Artificial intelligenceComputer graphics (images)Computer visionProgramming language

Abstract

fetched live from OpenAlex

This repository includes the dataset for the HeinSight 2.0 model, comprising 823 training images and 103 testing images. It encompasses the pretrained model weights along with its code and the 3D design for the camera enclosure used to capture the images within a chemical reactor in a laboratory setting. Each image in the dataset is annotated, providing information about the regions of different material phases and their types within the experiments. The pretrained model is ready to use without requiring additional training. For further details, please refer to: 10.26434/chemrxiv-2023-3rcmh-v2

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 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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.463
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0010.000
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 head0.038
GPT teacher head0.290
Teacher spread0.251 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2023
Admission routes1
Has abstractyes

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