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Record W4406668892 · doi:10.2460/ajvr.24.09.0276

Amino acid profiles for red wolves (Canis rufus) managed under human care are significantly different compared to the profiles of domestic dogs (Canis familiaris)

2025· article· en· W4406668892 on OpenAlexaboutno aff
Ashley R. Souza, Kimberly Ange‐van Heugten, Elizabeth G. Duke, Tara M. Harrison

Bibliographic record

VenueAmerican Journal of Veterinary Research · 2025
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsnot available
Fundersnot available
KeywordsCanisLabrador RetrieverConfidence intervalDomestic animalAnimal scienceVeterinary medicineBiologyZoologyMedicineInternal medicineEcologySurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: To establish a reference interval for amino acid profiles for healthy red wolves (Canis rufus). METHODS: Heparinized plasma of 48 red wolves was collected between August 2023 and April 2024 and sent to the University of California-Davis Amino Acid Laboratory for analysis. Reference intervals were created using the published American Society for Veterinary Clinical Pathology reference interval guidelines. Data were analyzed via Gaussian data distribution, and parametric statistical methods were used to produce a 90% CI of reference limits. The means of the red wolf intervals created were compared to those of the domestic dog using a z test. RESULTS: Reference intervals were created for red wolves (n = 48). Upon completion of the z test, 11 of 21 amino acids were found to be statistically significantly different compared to those of the domestic dog. CONCLUSIONS: A reference interval was created for red wolves. The red wolf amino acid profiles are different than those of the domestic dog, with 52% (11/21) of the profiles being statistically different. CLINICAL RELEVANCE: Red wolf amino acid profiles should not be compared to those of the domestic dog reference intervals due to the significant difference between profiles.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.872
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.164
GPT teacher head0.442
Teacher spread0.278 · 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 teacher head, not a consensus.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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