MétaCan
Menu
Back to cohort
Record W2392955499

ON A FREE BOUNDARY PROBLEM MODELLING THE GROWTH OF TUMORS

2005· article· en· W2392955499 on OpenAlexaff
Shangbin Cui

Bibliographic record

VenueChinese Annals of Mathematics,series A · 2005
Typearticle
Languageen
FieldMathematics
TopicMathematical Biology Tumor Growth
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBoundary (topology)Boundary value problemFree boundary problemMathematicsVolume (thermodynamics)Applied mathematicsBoundary problemMathematical economicsMathematical analysisCalculus (dental)MedicineThermodynamicsPhysicsOrthodontics
DOInot available

Abstract

fetched live from OpenAlex

This paper studies a free boundary problem modelling avascular tumor growth. This free boundary problem is an improvement of the tumor growth model proposed by Byrne and Chaplain. The authors study the asymptotic behavior of the solution, and prove that in the case where c1 and c2 are sufficiently small, the volume of an avascular tumor cannot expand unlimitedly, and it will either disappear or evolve to a dormant state as t→∞.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0060.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.056
GPT teacher head0.311
Teacher spread0.255 · 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 designSimulation or modeling
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

Citations0
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueChinese Annals of Mathematics,series ASame topicMathematical Biology Tumor GrowthFrench-language works237,207