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Record W4289885347 · doi:10.1039/d2sc90145e

Correction: Expanding medicinal chemistry into 3D space: metallofragments as 3D scaffolds for fragment-based drug discovery

2022· erratum· en· W4289885347 on OpenAlex

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueChemical Science · 2022
Typeerratum
Languageen
FieldMaterials Science
TopicMachine Learning in Materials Science
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of General Medical SciencesNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsFragment (logic)Drug discoveryChemical spaceChemistryCombinatorial chemistryComputational biologyNanotechnologyComputer scienceMaterials scienceBiologyAlgorithmBiochemistry

Abstract

fetched live from OpenAlex

Correction for ‘Expanding medicinal chemistry into 3D space: metallofragments as 3D scaffolds for fragment-based drug discovery’ by Christine N. Morrison et al. , Chem. Sci. , 2020, 11 , 1216–1225, https://doi.org/10.1039/C9SC05586J.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.520
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.003
Scholarly communication0.0010.001
Open science0.0040.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.000

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 head0.008
GPT teacher head0.282
Teacher spread0.275 · 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