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Record W3016517140 · doi:10.1039/d0ce00026d

A digital workflow from crystallographic structure to single crystal particle attributes for predicting the formulation properties of terbutaline sulfate

2020· article· en· W3016517140 on OpenAlexaff
Thai Thu Hien Nguyen, Robert B. Hammond, Ioanna Danai Styliari, Darragh Murnane, Kevin J. Roberts

Bibliographic record

VenueCrystEngComm · 2020
Typearticle
Languageen
FieldMaterials Science
TopicCrystallization and Solubility Studies
Canadian institutionsCentre for Drug Research and Development
FundersPfizer UKNovartis Pharmaceuticals UK LimitedSyngenta InternationalEngineering and Physical Sciences Research CouncilBoehringer Ingelheim
KeywordsCrystal habitWorkflowIonic bondingCrystal (programming language)Particle (ecology)SulfateTerbutalineMaterials scienceSalt (chemistry)CrystallographyChemistryPhysical chemistryComputer scienceIonOrganic chemistryDatabaseMetallurgyMedicine

Abstract

fetched live from OpenAlex

A detailed inter-molecular (synthonic) analysis of terbutaline sulfate, an ionic addition salt for inhalation drug formulation, is related to its crystal morphology, the surface chemistry of the habit faces and hence to its crystal surface energy.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.073
Threshold uncertainty score0.245

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0730.043

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 head0.053
GPT teacher head0.236
Teacher spread0.183 · 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 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

Citations14
Published2020
Admission routes1
Has abstractyes

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