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Record W2052349413 · doi:10.3354/dao056049

In vitro nutritional requirements and metabolic products of pathogenic and nonpathogenic strains of Cryptobia salmositica: essential carbohydrates and amino acids

2003· article· en· W2052349413 on OpenAlexafffund
Bernadette F. Ardelli

Bibliographic record

VenueDiseases of Aquatic Organisms · 2003
Typearticle
Languageen
FieldImmunology and Microbiology
TopicParasitic Infections and Diagnostics
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyBiochemistryMaltoseSucroseFructoseAmino acidMonosaccharideGalactoseMicrobiologyAlanineXyloseTrehaloseCarbohydrateMannoseFermentation

Abstract

fetched live from OpenAlex

Pathogenic and nonpathogenic strains of Cryptobia salmositica cultured in minimum essential medium (MEM) with several monosaccharides, disaccharides and amino acids were observed for differences in multiplication and motility. Metabolic end products (i.e. alanine, aspartate, carbon dioxide, lactate and pyruvate) were measured for logarithmically growing cells under aerobic conditions. The pathogenic strain of C. salmositica multiplied more readily in MEM supplemented with D(-)ribose, D(+)xylose, D(+)galactose, D(+)glucose, D(+)mannose and D(-)fructose. However, there were no significant differences in multiplication when the strains were cultured with the monosaccharide D(-)arabinose. The nonpathogenic strain multiplied significantly better than the pathogenic strain in the presence of the disaccharides alpha-lactose, maltose and sucrose. It also multiplied more readily when the amino acids L-glutamine and D(-)proline were added to MEM. The end products of carbohydrate catabolism under aerobic conditions were alanine, aspartate, carbon dioxide, lactate and pyruvate.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.237
Teacher spread0.229 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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
Published2003
Admission routes2
Has abstractyes

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