MétaCan
Menu
Back to cohort
Record W2913311784

Proceedings of the Poster Session and Student Colloquium Symposium

2015· article· en· W2913311784 on OpenAlexaff
Salim Chemlal, Mohammed Moallemi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsCarleton University
Fundersnot available
KeywordsSession (web analytics)BrainstormingComputer scienceWork (physics)VisibilityPoint (geometry)Mathematics educationPsychologyWorld Wide WebEngineeringArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

Building on a key goal of the Multi-conference -to bring Modeling and Simulation practitioners and researchers coming from various domains together- the Poster Session offers a single point of encounter and discussion for scientific ideas in embryonic state and with high potential. Interesting novel results, original ideas or works-in-progress that are not quite ready for a regular full-length paper find their place in the Poster Session. Presenters and attendees have the opportunity to engage in enriching discussions about their work in a cross-domain environment. This year we are very excited to introduce also a Student Colloquium. The objective of the Colloquium is to give an opportunity for students (in particular Ph.D. students) to showcase and discuss their work in progress during a session of short oral presentations. Any student attending the conference can participate, including students with accepted papers at the Multi-Conference. For those students with an accepted paper, they can choose to give a short version of such paper during the Colloquium, including new results or research advances since the original submission. Also, all Colloquium students will participate in the Poster Session. This way, students at both early and advanced stages of their careers will find the occasion to network with colleagues, give their work a wider visibility, and brainstorm ideas strengthening their fitness in scientific discussion.

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)0.003
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 score0.374
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.123
GPT teacher head0.386
Teacher spread0.263 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicScientific Computing and Data ManagementFrench-language works237,207