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Inference of ancestral protein-protein interactions using methods from algebraic statistics

2011· dissertation· en· W10699555 on OpenAlexfundno aff
Ashok Rajaraman

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldComputer Science
TopicBayesian Modeling and Causal Inference
Canadian institutionsnot available
FundersDivision of Mathematical SciencesTechnische Universität DarmstadtWestern Canada Research Grid
KeywordsInferenceComputer scienceParametric statisticsBayesian inferenceStatistical inferenceMachine learningBayesian networkArtificial intelligenceBayesian statisticsFiducial inferenceTheoretical computer scienceBayesian probabilityMathematicsStatistics

Abstract

fetched live from OpenAlex

Studies have shown an increased muscle activation at the opposite passive side during unilateral contractions. The purpose of the present study was to examine the influence of pain on muscle activation in the passive shoulder during unilateral shoulder abduction. Ten patients with unilateral rotator tendinosis of the shoulder and nine healthy controls performed unilateral maximal voluntary contractions (MVC) and sustained submaximal contractions with and without subacromial injections of local anaesthetics of the afflicted shoulder. Muscle activation was recorded by electromyography (EMG) from the trapezius, deltoid, infraspinatus and supraspinatus muscles in both shoulders. During MVCs, the EMG amplitude from muscles of the passive afflicted side was not different in patients and controls, and was not influenced by pain alterations. In contrast, the EMG amplitude from the muscles of the passive unafflicted side was lower in the patients and increased after pain reduction. During the sustained submaximal contraction the EMG amplitude increased gradually in the passive shoulder to 15-30% of the EMG amplitude observed during MVC. This response was not influenced by differences in pain. We conclude that muscle activation of the passive shoulder was closely related to the activation of the contracting muscles and thus related to central motor drive, and not directly influenced by changes in pain.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.322
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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.105
GPT teacher head0.405
Teacher spread0.300 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreMethods

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
Published2011
Admission routes1
Has abstractyes

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