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Record W3022270012 · doi:10.5206/mase/10221

Analysis of solutions and disease progressions for a within-host Tuberculosis model

2020· article· en· W3022270012 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.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueMathematics in Applied Sciences and Engineering · 2020
Typearticle
Languageen
FieldMedicine
TopicMathematical and Theoretical Epidemiology and Ecology Models
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaTexas Tech University
KeywordsTuberculosisDiseaseLatency (audio)Host (biology)Mycobacterium tuberculosisBifurcationPathogenInfectious disease (medical specialty)BiologyImmunologyMedicineComputer sciencePathologyEcologyPhysics

Abstract

fetched live from OpenAlex

Mycobacterium tuberculosis infection can lead to different disease outcomes, we analyze awith-in host tuberculosis infection model considering interactions between macrophages, T lym-phocytes, and tuberculosis bacteria to understand the dynamics of disease progression. Fourcoexisting equilibria that reflect TB disease dynamics are present: clearance, latency, and pri-mary disease, with low and high pathogen loads. We also derive the conditions for backwardand forward bifurcations and for global stable disease free equilibrium, which affect how thedisease progresses. Numerical bifurcation analysis and simulations elucidate the dynamics offast and slow disease progression.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.909
Threshold uncertainty score0.198

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 head0.055
GPT teacher head0.297
Teacher spread0.243 · 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