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Record W2318514019 · doi:10.1097/opx.0b013e31825489df

Longitudinal <i>In Vivo</i> Imaging of Cones in the Alert Chicken

2012· article· en· W2318514019 on OpenAlexaff
M. L. Kisilak, Kaitlin Bunghardt, Jennifer J. Hunter, Elizabeth L. Irving, Melanie C. W. Campbell

Bibliographic record

VenueOptometry and Vision Science · 2012
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsRegional Municipality of WaterlooUniversity of Waterloo
FundersUniversity of California Berkeley
KeywordsCone (formal languages)OpticsRetinoscopyIn vivoAngular resolution (graph drawing)AchromatopsiaPhysicsBiologyMaterials scienceOphthalmologyMathematicsVisual acuityMedicineRetinalRefractive error

Abstract

fetched live from OpenAlex

PURPOSE: To demonstrate the feasibility of in vivo measurements of cones and their distributions as a function of normal growth without adaptive optics (AO) and also discuss the potential advantages and disadvantages of AO imaging in the chick, an animal model of myopia. METHODS: Chicks were obtained on the day of hatching. Axial length and retinoscopy measurements were performed on days 0 and 14. Chicks were imaged on the day of hatching and 14 days later in a custom-built confocal scanning laser ophthalmoscope. Angular densities, linear cone spacings, and cone packing arrangements were determined. RESULTS: Four subarrays of hexagonally packed cones were identified on both days and, from their angular spacings, appear to correspond to different cone types. There were no significant changes in angular cone density with growth and linear spacings of cones increased with growth. This is true for both overall densities and those of the cone subtypes. There was no change in the percent of hexagonally packed cones with growth. CONCLUSIONS: Cones can be imaged longitudinally in vivo in the awake chick. The packing arrangement of cones is 40% hexagonally packed. Although AO is not necessary to visualize the cones, including the subarrays of like cones, some closely spaced cones of different types may not be resolved. Most importantly, there is a need to use a larger pupil with growth to maintain the same linear resolution in the larger eye. Novel longitudinal imaging techniques and methods in animal models are shown here to give insights into normal development and, in future, will give insights into visual disorders and diseases, including myopia.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.245

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.035
GPT teacher head0.469
Teacher spread0.434 · 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

Citations10
Published2012
Admission routes1
Has abstractyes

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