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Record W2792451551 · doi:10.1017/s0952523817000335

Short periods of darkness fail to restore visual or neural plasticity in adult cats

2018· article· en· W2792451551 on OpenAlexafffund
Kaitlyn Holman, Kevin R. Duffy, Donald E. Mitchell

Bibliographic record

VenueVisual Neuroscience · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsDalhousie University
FundersCanadian Institutes of Health Research
KeywordsDarknessMonocular deprivationCATSKittenVisual acuitySensory deprivationLateral geniculate nucleusRetinaOphthalmologyMonocularSomaBiologyVisual cortexAnatomyNeuroscienceOcular dominanceMedicineInternal medicineOptics

Abstract

fetched live from OpenAlex

It has been shown that the visual acuity loss experienced by the deprived eye of kittens following an early period of monocular deprivation (MD) can be alleviated rapidly following 10 days of complete darkness when imposed even as late as 14 weeks of age. To examine whether 10 days of darkness conferred benefits at any age, we measured the extent of recovery of the visual acuity of the deprived eye following the darkness imposed on adult cats that had received the same early period of MD as used in prior experiments conducted on kittens. Parallel studies conducted on different animals examined the extent to which darkness changed the magnitude of the MD-induced laminar differences of the cell soma size and immunoreactivity for the neurofilament (NF) protein in the dorsal lateral geniculate nucleus (dLGN). The results indicated that 10 days of darkness imposed at one year of age neither alleviated the acuity loss of the deprived eye induced by an earlier period of MD nor did it decrease the concurrent lamina differences of the soma size or NF loss in the dLGN.

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: none
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)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.040
GPT teacher head0.419
Teacher spread0.379 · 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

Citations15
Published2018
Admission routes2
Has abstractyes

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