MétaCan
Menu
Back to cohort
Record W2048384452 · doi:10.1145/2096149.2096155

Modeling on quicksand

2012· article· en· W2048384452 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

VenueACM SIGCOMM Computer Communication Review · 2012
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsUniversity of Toronto
FundersResearch EnglandMicrosoft ResearchNational Science Foundation
KeywordsComputer scienceScalabilityRobustness (evolution)Network topologyScarcityDistributed computingRouting (electronic design automation)Sensitivity (control systems)Ground truthEmpirical researchTopology (electrical circuits)Machine learningComputer networkDatabase

Abstract

fetched live from OpenAlex

Researchers studying the interdomain routing system, its properties and new protocols, face many challenges in performing realistic evaluations and simulations. Modeling decisions with respect to AS-level topology, routing policies and traffic matrices are complicated by a scarcity of ground truth for each of these components. Moreover, scalability issues arise when attempting to simulate over large (although still incomplete) empirically-derived AS-level topologies. In this paper, we discuss our approach for analyzing the robustness of our results to incomplete empirical data. We do this by (1) developing fast simulation algorithms that enable us to (2) running multiple simulations with varied parameters that test the sensitivity of our research results.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.896
Threshold uncertainty score0.783

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0040.001
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 head0.043
GPT teacher head0.288
Teacher spread0.245 · 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