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Record W2018607360 · doi:10.1097/hp.0b013e318213be69

RAPID URINARY OUTPUT NORMALIZATION METHOD USING SPECIFIC GRAVITY

2011· article· en· W2018607360 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHealth Physics · 2011
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsAtomic Energy (Canada)
FundersInternational Atomic Energy Agency
KeywordsNormalization (sociology)UrineUrinary systemMedicineComputer scienceUrologyInternal medicine

Abstract

fetched live from OpenAlex

Spot urine samples are often taken for emergency radiobioassay to provide a quick dose assessment for contaminated individuals. The subsequent dosimetric analysis requires a urinary output normalization method to adjust spot sampling to daily urine excretion. A rapid specific gravity method that was developed for 24-h urinary output correction is described. Spot urine samples were collected from volunteers of different race, gender, and age to validate the normalization method. Results show that a specific gravity test is a fast, easy and robust method to correct the urine excretion in the event of a radiation emergency.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.929
Threshold uncertainty score0.380

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 head0.192
GPT teacher head0.385
Teacher spread0.192 · 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