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Record W2995485462 · doi:10.1101/2019.12.16.878702

Serum biochemistry suggests grey squirrels ( <i>Sciurus carolinensis</i> ) have poorer physiological condition in urban settings

2019· preprint· en· W2995485462 on OpenAlexafffund
Chloé Schmidt, Jason R. Treberg, Riikka Kinnunen, Colin J. Garroway

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicRabbits: Nutrition, Reproduction, Health
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsSciurus carolinensisWildlifePopulationBiologyGlobulinMalnutritionEcologyEnvironmental healthMedicineEndocrinologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Human food waste in cities presents urban wildlife with predictable, easily accessible high-calorie food sources, but this can be both beneficial and harmful for individual health. We analyzed body condition and serum chemistry (electrolyte levels, markers of kidney and liver function, protein, glucose, and cholesterol) in an urban and rural population of eastern grey squirrels ( Sciurus carolinensis ) to assess whether proximity to the human food waste that is associated with urban habitats had ill effects on health. We found no differences in body condition between habitats and no evidence of malnutrition at either site. However, urban squirrels had higher blood glucose, lower potassium, phosphorus, chloride, and albumin:globulin ratios. These results align with previous findings of increased dietary sugar in cities, and suggest that urban populations of grey squirrels are under greater environmental stress than rural populations.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
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)0.0020.000

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 head0.014
GPT teacher head0.222
Teacher spread0.208 · 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 designObservational
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

Citations7
Published2019
Admission routes2
Has abstractyes

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