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Record W4233667338 · doi:10.33137/jns.v2i1.36269

Spotlights

2021· article· en· W4233667338 on OpenAlexaffvenue
Yu Bin Tan

Bibliographic record

VenueUTSC s Journal of Natural Sciences · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEarthquake and Disaster Impact Studies
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

What research and teaching are you currently conducting at UTSC?My research is in Geodynamics.I am interested in the evolution of planetary interiors and study this using high performance computing and computer modelling of the mantle.In the case of the Earth, mantle convection drives plate tectonics, continental drift, and turns heat flow from the core.It affects the magnetic field, the mountains, and even biological evolution!Without mantle convection, we would not have continents, or even human beings!I have a lab in the GTA, myself and my grad students work remotely. How has the COVID-19 lockdown impacted your research and teaching at UTSC?My research has not been impacted because it is conducted online.Every virtual experiment done is submitted to the computer at the main lab which gives back results.As for teaching, I have not done any teaching last winter (winter 2020) or summer (summer 2020).I will be teaching in fall 2020 and I will be giving online lectures, which will also be uploaded onto the portal for students who want to rewatch/have missed the lectures.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.617
Threshold uncertainty score0.576

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 studies0.0010.001
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 head0.037
GPT teacher head0.361
Teacher spread0.324 · 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
Published2021
Admission routes2
Has abstractyes

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