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Record W2801401754 · doi:10.1017/s0952523817000244

Animal models of amblyopia

2018· review· en· W2801401754 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueVisual Neuroscience · 2018
Typereview
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsDalhousie University
FundersBiotechnology and Biological Sciences Research Council
KeywordsTimelineAnimal speciesComputer sciencePsychologyAnimal modelCognitive psychologyData scienceCognitive scienceBiologyGeographyEvolutionary biology

Abstract

fetched live from OpenAlex

Unquestionably, the last six decades of research on various animal models have advanced our understanding of the mechanisms that underlie the many complex characteristics of amblyopia as well as provided promising new avenues for treatment. While animal models in general have served an important purpose, there nonetheless remain questions regarding the efficacy of particular models considering the differences across animal species, especially when the goal is to provide the foundations for human interventions. Our discussion of these issues culminated in three recommendations for future research to provide cohesion across animals models as well as a fourth recommendation for acceptance of a protocol for the minimum number of steps necessary for the translation of results obtained on particular animal models to human clinical trials. The three recommendations for future research arose from discussions of various issues including the specific results obtained from the use of different animal models, the degree of similarity to the human visual system, the ability to generate animal models of the different types of human amblyopia as well as the difficulty of scaling developmental timelines between different species.

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.

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)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.585
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.389
GPT teacher head0.455
Teacher spread0.066 · 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