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Record W7061575306

Program and the Book of abstracts / Eighteenth Young Researchers' Conference Materials Sciences and Engineering, December 4-6, 2019, Belgrade, Serbia

2019· article· en· W7061575306 on OpenAlexaboutno aff

Bibliographic record

VenueDigital Archive of SASA (Serbian Academy of Sciences and Arts) · 2019
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSerbianFrontierScience and engineeringBasic research
DOInot available

Abstract

fetched live from OpenAlex

Young Researchers' meetings are held annually late in December since 2002 and they are organized by the Materials Research Society of Serbia. Originally conceived as seminars, since 2007 these meetings were transformed into conferences. The previous eleven meetings featured presentations based on the research of various young scientists from Serbia, Bosnia and Herzegovina, Montenegro, Slovenia, Brazil, Germany, United States of America, China, Poland, Belgium, Spain, Romania, United Kingdom, Austria, Italy, Hungary, Russia, Canada, etc. At the Conference, young researchers, students of doctoral, master and undergraduate studies, are given the opportunity to make an overview of their research into materials science and engineering through oral and poster presentations. As for the scientific content of the conference, we have given full priority to research topics that are currently considered as being on the frontier of the field. Nanotechnology and Advanced Materials, Synthesis and Engineering of Biomaterials, Application of Biomaterials, Theoretical Modeling of Materials and Advanced Methods for Synthesis and Processing present only some of those exciting topics that will be given the central stage and most attention during this meeting. The 18th Young Researchers' Conference Materials Science and Engineering was held in Belgrade, Serbia on December 4-6, 2019, Belgrade, Serbia. It was organized by the Materials Research Society of Serbia and Institute of Technical Sciences of the Serbian Academy of Sciences and Arts.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.495
Threshold uncertainty score0.823

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 studies0.0000.002
Scholarly communication0.0000.001
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 head0.022
GPT teacher head0.261
Teacher spread0.239 · 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 designObservational
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
Published2019
Admission routes1
Has abstractyes

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