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.
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to develop a simple method for cross-calibrating instruments that measure corneal thickness. METHODS: Fourteen rigid lenses of different thicknesses were manufactured using a material with refractive index of 1.376. Center thickness of the lenses (CT) was measured using a computerized optical pachometer (OP), two optical coherence tomographers (OCTs), and a confocal microscope (CM). Accuracy of measurements was compared between the four instruments. RESULTS: Before calibrating the machines, there was a significant effect of the measurement device (p < 0.05). The differences between instruments were eliminated (p > 0.05) after applying calibration equations for each device. In addition, after each instrument was calibrated with lenses of 1.376 refractive index, there was no significant difference (p > 0.05) between measured values of lens center thickness by OP, each OCT, CM, and the physical center thickness of the lenses. CONCLUSIONS: Using calibration lenses with the same refractive index as the cornea (1.376) allows rapid and simple calibration of the pachometers so that corneal thickness measurements from different devices can be used interchangeably.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it