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Record W4386594605 · doi:10.3791/65804-v

JoVE Video Dataset

2023· article· pt· W4386594605 on OpenAlexaff
Julian Wolf, Teja Chemudupati, Aarushi Kumar, Ditte K. Rasmussen, Karen M. Wai, Robert T. Chang, Artis A. Montague, Peter H. Tang, Alexander G. Bassuk, Antoine Dufour, Prithvi Mruthrunjaya, Vinit B. Mahajan

Bibliographic record

Venuenot available
Typearticle
Languagept
FieldMedicine
TopicRetinal Imaging and Analysis
Canadian institutionsUniversity of Calgary
FundersNational Institutes of HealthH. Lundbeck A/SLundbeckfondenVitreoRetinal Surgery FoundationResearch to Prevent Blindness
KeywordsWorkflowBarcodeComputer scienceInterface (matter)Protocol (science)Identification (biology)Human–computer interactionDatabaseOperating systemMedicinePathologyBiology

Abstract

fetched live from OpenAlex

3.1K Views. Stanford University. Molecular profiling of liquid biopsies from the human eye can capture locally enriched fluids containing thousands of different molecules from highly specialized ocular tissues. They allow molecular characterization of ocular diseases in living humans and of further potential to identify novel diagnostic and therapeutic strategies. Here we developed protocol for the standardized collection and biobanking of high quality aqueous humor and vitreous liquid biopsies during intraocular surgery.

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 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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.271
Threshold uncertainty score0.908

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.2710.210

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 head0.037
GPT teacher head0.351
Teacher spread0.314 · 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 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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