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Record W3094390336 · doi:10.1071/ma20052

Tuberculosis: yesterday, today and tomorrow

2020· article· en· W3094390336 on OpenAlex
Christopher Lowbridge, Anna P. Ralph

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

VenueMicrobiology Australia · 2020
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsCarbon Engineering (Canada)
Fundersnot available
KeywordsYesterdayTuberculosisPandemicDisadvantagePublic healthDiseaseMedicineEconomic growthCoronavirus disease 2019 (COVID-19)Environmental healthPolitical scienceInfectious disease (medical specialty)NursingEconomicsPathology

Abstract

fetched live from OpenAlex

Tuberculosis (TB) remains an important public health challenge globally and in Australia. For the more than 10 million people who become sick with TB each year, the disease can cause immense personal and economic hardship, including loss of income and education through ill health, prolonged and arduous treatment, and stigmatisation – perpetuating a cycle of disadvantage. Past efforts to control TB have taught us much about modern disease control and public health. As the world grapples with the coronavirus (COVID-19) pandemic, the response to TB provides valuable lessons which can inform our response to COVID-19.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.616
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.061
GPT teacher head0.336
Teacher spread0.274 · 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