Authors Reply
Bibliographic record
Abstract
The article is a reply to comments by G.K. Hung (see ibid., p.2787-9, 2010). We emphasize that the primary purpose of the study was to determine if the two most commonly cited dynamic models of accommodation and vergence predicted two empirical findings taken from our investigation of near addition lenses. Our empirical investigation measured vergence adaptation and a concurrent reduction of CA associated with that process. Two commonly cited models of vergence and accommodation correctly predicted the change in vergence adaptation. Only one model (C.M. Schor, 1992) predicted the concurrent reduction in CA with vergence adaptation. In his letter, Dr. Hung uses a static model to compare the steady-state CA responses of the two models, which was NOT the intention of the study. We would argue that the framework of model discussion must be those, which are dynamic, and hence quantitatively describe the replacement of fast vergence with that of slow or tonic responses. Static models are not appropriate as vergence adaptation is not described in that format.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".