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Record W3081598582 · doi:10.1128/mra.00763-20

Best Practices for Successfully Writing and Publishing a Genome Announcement in <i>Microbiology Resource Announcements</i>

2020· editorial· en· W3081598582 on OpenAlexfundno aff
Julie C. Dunning Hotopp, David A. Baltrus, Vincent M. Bruno, John J. Dennehy, Steven R. Gill, Julia A. Maresca, Jelle Matthijnssens, Irene L. G. Newton, Catherine Putonti, David A. Rasko, Antonis Rokas, Simon Roux, Jason Stajich, Kenneth M. Stedman, Frank J. Stewart, J. Cameron Thrash

Bibliographic record

VenueMicrobiology Resource Announcements · 2020
Typeeditorial
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsnot available
FundersCommon FundNational Institute of Allergy and Infectious DiseasesNuclear Safety and Security CommissionJoint Genome InstituteOffice of ScienceNational Aeronautics and Space AdministrationU.S. Department of DefenseU.S. Department of EnergyNational Institute of General Medical SciencesCanadian Institute for Advanced ResearchNational Cancer InstituteNational Institutes of HealthNational Science Foundation
KeywordsBest practicePublishingResource (disambiguation)Scientific writingScientific publishingLibrary scienceBest evidenceBusinessPublic relationsPolitical scienceComputer scienceMedical educationMedicineArtLawLiterature

Abstract

fetched live from OpenAlex

(MRA) provides peer-reviewed announcements of scientific resources for the microbial research community. We describe the best practices for writing an announcement that ensures that these publications are truly useful resources. Adhering to these best practices can lead to successful publication without the need for extensive revisions.

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)0.037
metaresearch head score (Gemma)0.114
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.963
Threshold uncertainty score0.194

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.114
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.002
Science and technology studies0.0040.002
Scholarly communication0.0140.006
Open science0.0030.003
Research integrity0.0080.010
Insufficient payload (model declined to judge)0.0440.062

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 head0.023
GPT teacher head0.276
Teacher spread0.254 · 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.

Study designNot applicable
DomainReporting
GenreEditorial

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

Citations3
Published2020
Admission routes1
Has abstractyes

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