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Record W4365505785 · doi:10.1515/psr-2022-0248

Computational chemistry in the undergraduate inorganic curriculum

2023· article· en· W4365505785 on OpenAlexafffund
John P. Canal

Bibliographic record

VenuePhysical Sciences Reviews · 2023
Typearticle
Languageen
FieldChemistry
TopicVarious Chemistry Research Topics
Canadian institutionsSimon Fraser University
FundersSimon Fraser University
KeywordsCurriculumChemistryGeneral chemistryOrganometallic chemistryQuantum chemistryComputational chemistryMathematics educationPhysical chemistryComputer scienceOrganic chemistryMoleculePedagogyPsychology

Abstract

fetched live from OpenAlex

Abstract The introduction of computation chemistry has increased in the undergraduate chemistry curriculum. Our method of instruction is centred on an online, self-paced approach where students interact with the material through an instructional handbook, videos, and assignments. In our inorganic undergraduate curriculum students explore computational chemistry though optimization of organometallic complexes, modelling the infrared (IR) and nuclear magnetic resonance (NMR) spectra and investigation of the shape and energy of molecular orbitals. These results are compared to experimentally determined data. The effectiveness of introducing students to computational chemistry to characterize organometallic compounds will be highlighted.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.537
Threshold uncertainty score0.750

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.051
GPT teacher head0.358
Teacher spread0.307 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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