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Record W2303711714 · doi:10.1007/s13555-016-0107-8

Erratum to: New Patient-Oriented Tools for Assessing Atrophic Acne Scarring

2016· erratum· en· W2303711714 on OpenAlexaff
Alison Layton, Brigitte Dréno, A.Y. Finlay, Diane Thiboutot, Sewon Kang, Vicente Torres Lozada, Valérie Bourdès, Vincenzo Bettoli, Laurent Petit, Jerry Tan

Bibliographic record

VenueDermatology and Therapy · 2016
Typeerratum
Languageen
FieldMedicine
TopicAcne and Rosacea Treatments and Effects
Canadian institutionsWestern University
FundersGalderma
KeywordsSection (typography)EngineeringOperations researchComputer scienceBusinessAdvertising

Abstract

fetched live from OpenAlex

The Acknowledgments section incorrectly states that ‘‘The authors wish to thank Valerie Sanders, from Sanders Medical Writing, for assistance in preparing this article. Support for this assistance was provided by Galderma International. Sponsorship for this study and article processing charges was funded by Galderma International.’’ This section should read as follows: ‘‘The authors wish to thank Valerie Sanders, from Sanders Medical Writing, for assistance in preparing this article. Support for this assistance was provided by Galderma International. Article processing charges were funded by Galderma International. Sponsorship for the study was funded by Galderma R & D.’’

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)0.004
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.041
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0150.015

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 head0.024
GPT teacher head0.307
Teacher spread0.283 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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