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Record W7011320597

Magnetic Resonance and its Applications. Abstracts book. Saint Petersburg State University. April 23-29, 2017

2017· book· en· W7011320597 on OpenAlexfundno aff

Bibliographic record

VenueResearch Repository Saint Petersburg State University (Saint Petersburg State University) · 2017
Typebook
Languageen
FieldEnvironmental Science
TopicAdvanced Scientific Techniques and Applications
Canadian institutionsnot available
FundersGebze Teknik ÜniversitesiNatural Sciences and Engineering Research Council of CanadaKazan Federal UniversityBilim, Sanayi ve Teknoloji BakanliğiRussian Science FoundationCentre National de la Recherche ScientifiqueRussian Foundation for Basic ResearchGerman-Russian Interdisciplinary Science Center
KeywordsSaint petersburgSt petersburgState (computer science)Magnetic resonance imagingResonance (particle physics)Electromagnetic radiationObject (grammar)Reputation
DOInot available

Abstract

fetched live from OpenAlex

The International Youth School-Conference “Magnetic resonance and its application” SPINUS-2017 will be held at the St. Petersburg State University in the 14th time. In modern physics, the term “magnetic resonance” refers to a set of phenomena accompanied with the emission or absorption of electromagnetic waves of the radiofrequency diapason by quantum systems (nuclei, electrons, atoms, molecules, etc.). These phenomena, the physical nature of which is of independent interest, provided the basis of radiospectroscopic methods for studying the structure of matter and physico-chemical processes in it. These methods, being contactless, do not destroy an object under a study, that makes them unique and in demand not only in physics and chemistry, but also in medicine, geology, biology, archeology. Now, any medical center with high reputation has a magnetic resonance imaging (MRI).

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.163
Threshold uncertainty score0.546

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1630.153

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 head0.021
GPT teacher head0.248
Teacher spread0.227 · 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 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
Published2017
Admission routes1
Has abstractyes

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