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Thoracic radiography of pet macaques in Sulawesi, Indonesia

2008· article· en· W2140948000 on OpenAlexaff
Michael A. Schillaci, Lisa Jones‐Engel, John E. Heidrich, Rachel Benamore, André Pereira, Narinder Paul

Bibliographic record

VenueJournal of Medical Primatology · 2008
Typearticle
Languageen
FieldPsychology
TopicPrimate Behavior and Ecology
Canadian institutionsUniversity Health NetworkThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsRadiographyMacaqueMedicineRadiologyLungIndonesianNuclear medicinePsychologyInternal medicineNeuroscience

Abstract

fetched live from OpenAlex

This report describes the results from a study of thoracic radiographs taken on a sample (n = 20) of pet macaque monkeys (Macaca) on the Indonesian island of Sulawesi. We present findings relating to general thoracic health, as well as describe our field methods and outline some of the challenges of conducting thoracic radiography in remote areas of the developing world. Examination of the radiographic images identified six possible cases of cardiac enlargement, and one case of minor lung consolidation possibly consistent with tuberculosis. The study did not identify major radiographic evidence of significant respiratory illness in the monkeys as might be expected based on the intense exposure to human respiratory diseases. These largely negative findings may be in part a consequence of image quality which seems to be influenced by power output and incomplete inspiration during film exposure.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.373
Teacher spread0.340 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations15
Published2008
Admission routes1
Has abstractyes

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