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Record W31457750 · doi:10.1021/acsomega.7b00366

Διαρθρωτικές μεταβολές στη διακύμανση και η πρόσφατη χρηματοοικονομική/οικονομική κρίση : μια εφαρμογή στο Χρηματιστήριο Αθηνών

2011· dissertation· en· W31457750 on OpenAlexfundno aff
Αναστασία Χονδρού

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicItaly: Economic History and Contemporary Issues
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaFonds Wetenschappelijk Onderzoek
KeywordsEconometricsStock exchangeStructural breakEmpirical researchEconomicsFinancial economicsComputer scienceStatisticsMathematicsFinance

Abstract

fetched live from OpenAlex

Thermal processing conditions, pH, and salt content affect the formation of egg white ovalbumin amyloid, which was investigated using high-precision measurements of ultrasonic velocity and attenuation. These were related to fluorescence and particle size measurements. Fluorescence changes indicated the formation of amyloid-like aggregates that was enhanced by increasing time-temperature treatments. The ultrasonic velocity of ovalbumin after heating at neutral pH (60 min at 70 or 80 °C) was lower than that of unheated ovalbumin, whereas the attenuation was higher. The decrease in the velocity represents increased compressibility associated with a change in the compactness of the protein, whereas changes in attenuation are due to protein conformational changes. Heating ramp studies revealed transitions at approximately 58 and 73 °C. During heating at a constant temperature, the ultrasonic velocity decreased slowly with increasing heating time, indicating an increase in ovalbumin compressibility. It is suggested that the obtained amyloid-like ovalbumin aggregates contain a compact core surrounded by loosely packed protein segments.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.736
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0020.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0310.035

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 head0.046
GPT teacher head0.200
Teacher spread0.154 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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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