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Record W2029562268 · doi:10.3390/microorganisms2010001

Sad State of Phage Electron Microscopy. Please Shoot the Messenger

2013· article· en· W2029562268 on OpenAlexafffund
Hans‐W. Ackermann

Bibliographic record

VenueMicroorganisms · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsUniversité Laval
FundersUniversité Laval
KeywordsElectron microscopeMagnificationElectron micrographsNegative stainScanning electron microscopeMicroscopyMicrographMicroscopeChemistryBiologyOpticsMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Two hundred and sixty publications from 2007 to 2012 were classified according to the quality of electron micrographs; namely as good (71); mediocre (21); or poor (168). Publications were from 37 countries; appeared in 77 journals; and included micrographs produced with about 60 models of electron microscopes. The quality of the micrographs was not linked to any country; journal; or electron microscope. Main problems were poor contrast; positive staining; low magnification; and small image size. Unsharp images were frequent. Many phage descriptions were silent on virus purification; magnification control; even the type of electron microscope and stain used. The deterioration in phage electron microscopy can be attributed to the absence of working instructions and electron microscopy courses; incompetent authors and reviewers; and lenient journals. All these factors are able to cause a gradual lowering of standards.

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)0.010
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.044
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.002
Science and technology studies0.0020.002
Scholarly communication0.0070.007
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0440.028

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 head0.003
GPT teacher head0.219
Teacher spread0.216 · 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
GenreCommentary

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

Citations29
Published2013
Admission routes2
Has abstractyes

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