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Record W1895081351 · doi:10.1139/z08-050

Visual specialization of an herbivore prey species, the white-tailed deer

2008· article· en· W1895081351 on OpenAlexvenueno aff
Gino J. D’Angelo, Adrian Glasser, Mark Wendt, Gary A. Williams, David A Osborn, George R. Gallagher, Robert J. Warren, Karl V. Miller, Machelle T. Pardue

Bibliographic record

VenueCanadian Journal of Zoology · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinal Development and Disorders
Canadian institutionsnot available
FundersGeorgia Department of Transportation
KeywordsBiologyOdocoileusStreakRetinaRetinalAnatomyZoologyOpticsNeuroscienceBotany

Abstract

fetched live from OpenAlex

To gain knowledge of visual specializations influencing the behavior of white-tailed deer ( Odocoileus virginianus (Zimmermann, 1780)), we examined gross eye characteristics, structural organization of the retina, and the density and distribution of cone photoreceptors. White-tailed deer possess ocular features similar to other ungulates including a horizontal slit pupil, reflective tapetum lucidum, typical retinal structure, and medium wavelength sensitive cone photoreceptors concentrated in a horizontal visual streak. The tapetum was found to cover the superior portion of the eye and overlapped the horizontal visual streak. Comparisons between fawns and adults did not reveal any differences in retinal thickness, retinal nuclei counts, or cone photoreceptor counts. While M-cones had increased density in the visual streak, S-cones were distributed evenly across the entire retina. Schematic eye calculations of a 0.5-year-old deer indicated a hyperopic eye (+7.96) with a F/# ranging from 5.55 to 1.39 for pupil diameters of 3 to 12 mm. As expected for a crepuscularly active prey species, the visual system of white-tailed deer is specialized for sensitivity in low-light conditions and detection of predators.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.041
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.010
GPT teacher head0.218
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 teacher head, 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

Citations34
Published2008
Admission routes1
Has abstractyes

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