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Record W6987314671

Simulation of Probabilistic Averaging in Ice Load Estimation

2006· article· en· W6987314671 on OpenAlexvenueno aff

Bibliographic record

VenueNPARC · 2006
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsProbabilistic logicAutoregressive modelEstimationStatistical modelProcess (computing)
DOInot available

Abstract

fetched live from OpenAlex

This paper mainly focuses on establishing a probabilistic model to explore the relationship between local pressures and the global pressure. Both spatial and temporal correlations among the local pressures are considered in the study. A vector autoregressive model (VAR) is applied in this research to include these correlations. The global contact area is divided into a group of local areas. The model is used to illustrate the probabilistic averaging effect from local pressure to global pressure. The duration effect in which a higher load is likely to result from a longer interaction process is also showed using this model. The model is also used to explore the pressure-area relationship and pressure-aspect ratio relationship. The results imply that using the pressure-area relationship and the pressure-aspect ratio relationship alone may not be sufficient for global load estimation.

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 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.197

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 head0.009
GPT teacher head0.219
Teacher spread0.211 · 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 teacher head, 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
Published2006
Admission routes1
Has abstractyes

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