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Record W2898480344 · doi:10.3233/jcm-180882

A modified ADMA linear scaling macromolecular method for enhanced detection of induced molecular shape changes

2018· article· en· W2898480344 on OpenAlexaff
Zoltán Antal, Paul G. Mezey

Bibliographic record

VenueJournal of Computational Methods in Sciences and Engineering · 2018
Typearticle
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsLinear scaleIntramolecular forceIntermolecular forceScalingMacromoleculeAb initioQuantum chemistryMoleculeComputational chemistryElectron densityChemistryChemical physicsStatistical physicsMaterials scienceElectronPhysicsMathematicsQuantum mechanicsSupramolecular chemistryStereochemistry

Abstract

fetched live from OpenAlex

By adding the option of the so-called “Walker Pseudo-Density Scheme”, a non-additive but interaction-enhancing electron density fragmentation scheme to the standard ADMA linear-scaling macromolecular quantum chemistry approach (where ADMA = Adjustable Density Matrix Assembler, an exactly additive s cheme), generating ab initio quality electron densities and other calculated molecular properties for large molecules, it is possible to obtain an enhanced diagnostic tool for the detection and comparison of shape changing effects of intramolecular, as well as intermolecular interactions. The required algorithmic modification of the original ADMA method is only minor, using only quantities which are already generated for the original ADMA method itself. In this study some of the required conditions are investigated and actual approaches are presented.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.046
GPT teacher head0.401
Teacher spread0.355 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations4
Published2018
Admission routes1
Has abstractyes

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