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Record W4253486367 · doi:10.1007/978-3-319-30916-3_3

Molecular Mechanics

2016· book-chapter· en· W4253486367 on OpenAlex
Errol G. Lewars

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueComputational Chemistry · 2016
Typebook-chapter
Languageen
FieldChemistry
TopicChemical Reaction Mechanisms
Canadian institutionsTrent University
Fundersnot available
KeywordsDihedral angleMoleculeBiomoleculeClassical mechanicsMolecular mechanicsBendingComputational chemistryMolecular dynamicsStatistical physicsChemistryPhysicsChemical physicsNanotechnologyTheoretical physicsMaterials scienceQuantum mechanicsThermodynamics

Abstract

fetched live from OpenAlex

Molecular mechanics (MM) rests on a view of molecules as balls held together by springs, ignoring electrons. The potential energy of a molecule can be written as the sum of terms involving (at least) bond, stretching, angle bending, dihedral angles, and nonbonded interactions. Giving these terms explicit mathematical forms constitutes devising a forcefield, and giving actual numbers to the constants in it constitutes parameterizing the forcefield. Calculations on large biomolecules is a very important application of MM, and the pharmaceutical industry designs new drugs with the aid of MM. Organic synthesis now makes use of MM, which enables chemists to estimate which products are likely to be favored in a reaction and to devise realistic routes to a target molecule. In molecular dynamics MM is often used to generate the forces acting on molecules and hence to calculate their motions.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.444
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0320.001

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 head0.012
GPT teacher head0.225
Teacher spread0.213 · 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