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Record W4400526202 · doi:10.17975/sfj-2024-008

Proceedings from the 2023 indicium conference

2024· article· en· W4400526202 on OpenAlexvenueaboutno aff

Bibliographic record

VenueSTEM Fellowship Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicInnovations in Concrete and Construction Materials
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceEnvironmental chemistryChemistry

Abstract

fetched live from OpenAlex

Indicium is an annual research competition geared towards introducing inexperienced students to the process of independent research. Through this mentorship program, undergraduate students are grouped with PhDs, professors, graduate students, medical students, etc. who are experienced in STEM research fields. Mentors actively guide and support students through a research project in their field of expertise and prepare them for the final Indicium Research Conference. The program also includes workshops focusing on various aspects of project development as well as networking opportunities within the greater scientific community. This year, Indicium was hosted at four university branches across Canada including McMaster University, York University, University of Toronto St. George, and University of Toronto Mississauga. Every branch held a university-level conference where participating teams were granted abstract publications in the National peer-reviewed STEM Fellowship Journal. Ten teams selected from these branches moved forward to participate in the National Indicium Research Conference held on August 12, 2023. Winning teams from this competition have been granted a full manuscript publication. We are pleased to be showcasing the conference proceedings from the four participating Canadian Indicium branches. Working alongside the incredible group of mentors, mentees, judging panel, and executive team on this initiative was an honour. Indicium would not be possible without the drive of those who chose to indulge in this platform in the pursuit of knowledge, mentorship, and future opportunities.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.365
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.222
Teacher spread0.199 · 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 designNot applicable
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
Published2024
Admission routes2
Has abstractyes

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