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Record W1971592750 · doi:10.2967/jnmt.114.148650

Evaluation of Available In Vitro 99mTc-RBC Labeling Techniques: A Canadian Perspective

2014· editorial· en· W1971592750 on OpenAlexaffabout
Eiko Toda, Mihaela Ginj

Bibliographic record

VenueJournal of Nuclear Medicine Technology · 2014
Typeeditorial
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsToronto General HospitalUniversity Health Network
Fundersnot available
KeywordsModalitiesPerspective (graphical)Nuclear medicineCompetition (biology)Nuclear medicine imagingMedical physicsMedicineComputer scienceArtificial intelligenceSociology

Abstract

fetched live from OpenAlex

A significant challenge for the Canadian nuclear medicine community is the single sourcing for most kits and ready-to-use radiopharmaceuticals. The main causes are the small size of the Canadian nuclear medicine market and intense competition from the other imaging modalities (MR imaging, CT,

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)0.011
metaresearch head score (Gemma)0.019
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.838
Threshold uncertainty score0.321

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.019
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.003
Science and technology studies0.0030.003
Scholarly communication0.0060.003
Open science0.0030.001
Research integrity0.0120.011
Insufficient payload (model declined to judge)0.0030.002

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 head0.028
GPT teacher head0.348
Teacher spread0.320 · 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
GenreEditorial

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

Citations2
Published2014
Admission routes2
Has abstractyes

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