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Record W3147279483 · doi:10.1080/714028139

Use of the Gram stain in microbiology

2001· article· en· W3147279483 on OpenAlexaff
TJ Beveridge

Bibliographic record

VenueBiotechnic & Histochemistry · 2001
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPeptidoglycanGram stainingStainBacteriaCell wallNegative stainStainingMicrobiologyGram-negative bacteriaCrystal violetBiologyChemistryBiochemistryEscherichia coliElectron microscope

Abstract

fetched live from OpenAlex

The Gram stain differentiates bacteria into two fundamental varieties of cells. Bacteria that retain the initial crystal violet stain (purple) are said to be ''Gram-positive,'' whereas those that are decolorized and stain red with carbol fuchsin (or safranin) are said to be ''Gram-negative.'' This staining response is based on the chemical and structural makeup of the cell walls of both varieties of bacteria. Gram-positives have a thick, relatively impermeable wall that resists decolorization and is composed of peptidoglycan and secondary polymers. Gram-negatives have a thin peptidoglycan layer plus an overlying lipid-protein bilayer known as the outer membrane, which can be disrupted by decolorization. Some bacteria have walls of intermediate structure and, although they are officially classified as Gram-positives because of their linage, they stain in a variable manner. One prokaryote domain, the Archaea, have such variability of wall structure that the Gram stain is not a useful differentiating tool.

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)0.003
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0110.010

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 head0.016
GPT teacher head0.222
Teacher spread0.207 · 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
GenreMethods

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

Citations52
Published2001
Admission routes1
Has abstractyes

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