MétaCan
Menu
Back to cohort
Record W2559702426 · doi:10.1107/s2053273314086860

Agilent Technologies: Proud Global Sponsors of the IYCr2014

2014· article· en· W2559702426 on OpenAlexaboutno aff
Oliver Presly

Bibliographic record

VenueActa Crystallographica Section A Foundations and Advances · 2014
Typearticle
Languageen
FieldMaterials Science
TopicMachine Learning in Materials Science
Canadian institutionsnot available
Fundersnot available
KeywordsCONTESTAmateurEngineeringLibrary scienceComputer scienceWorld Wide WebPolitical science

Abstract

fetched live from OpenAlex

"The International Year of Crystallography 2014 represents a unique chance to highlight crystallography to a wide audience of researchers, students, school children and members of the public. It also offers a platform for boosting the capabilities of research laboratories in less well developed parts of the world, with instruments and expertise helping a new generation of scientists to learn theory and techniques, to generate data and to form collaborations with experts in various crystallographic fields. Agilent is proud to support IYCr2014, to assist the International Union of Crystallography (IUCr) and its partners in achieving some of the key goals of the project. To this end, Agilent has teamed up with the IUCr in the IYCr2014 ""Crystallography in Everyday Life"" photo contest. A highlight of the IUCr Congress, this contest has been open throughout 2014, inviting all amateur and professional photographers to submit stunning images that capture the spirit of crystallography in the places, objects and experiences of everyday life. Entries have been submitted from across the world from both scientists and non-scientists alike. Two winning entries will each receive bursaries to attend the IUCr Congress, and their entries will be on display in the main exhibition area. These and 14 further notable entries will make up the Agilent-IUCr IYCr2014 academic calendar (2014-15), and copies will be available from the Agilent booth in Montreal. Agilent is also proudly supporting the IUCr-UNESCO OpenLab initiative; aiming to provide facilities and teaching to young scientists without access to their own instrumentation. Agilent and the IUCr have been working with a number of Agilent system users, specifically in Argentina, Hong Kong and Turkey, to develop OpenLab workshops organized by local researchers and supported by Agilent sponsorship and Application Scientists. This poster will highlight Agilent's IYCr participation, focusing on these initiatives."

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.950
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.006
GPT teacher head0.249
Teacher spread0.244 · 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 designBench or experimental
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
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueActa Crystallographica Section A Foundations and AdvancesSame topicMachine Learning in Materials ScienceFrench-language works237,207