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Record W7047176295

Face fashion market hots up with arrival of new optical chain from Australia

2016· other· en· W7047176295 on OpenAlexaboutno aff

Bibliographic record

VenueResearchSpace (University of Auckland) · 2016
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)BlameReading (process)Face (sociological concept)PopulationClass (philosophy)
DOInot available

Abstract

fetched live from OpenAlex

New Zealand optometrists perform more than a million eye examinations a year and by all accounts our eyesight is worsening. Dr Philip Turnbull, a research fellow at the Auckland University Optometry School, said myopia, or short-sightedness, was increasing world wide. Based on census and driver licensing data, about a quarter of New Zealand adults need to wear glasses or contact lenses for driving. Quite apart from the normal deterioration that goes with aging, one theory is that urban lifestyles are to blame for short-sightedness because our eyes get less opportunity to focus long distance. Turnbull is researching the impact of increased screen use and said it may be a factor in rising myopia rates in children. Although there are no New Zealand statistics, he said the situation here was likely to reflect Australian trends where a recent study showed a doubling of myopia in children aged seven and in 10 to 13-year-olds. "It was 4.4 per cent six years ago, and now it's gone up to 8.6 per cent. It's unlikely to be hugely different here. "It's difficult to tease out the factors. Reading time has also been implicated, it doesn't matter whether is on a tablet or on paper, the more time a child spends reading, the more likely they are to become myopic." Children's attitude to glasses has also changed and Turnbull said it wasn't uncommon for children to fake poor eyesight to get trendy glasses. "May be someone in their class has a particularly cool pair of frames and then you get a few coming in. That whole geek thing has absolutely gone."

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.599
Threshold uncertainty score0.979

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.5990.000

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 head0.021
GPT teacher head0.267
Teacher spread0.245 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2016
Admission routes1
Has abstractyes

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