Poster Session: Threshold versus intensity curves measured with a new high-brightness display system
Bibliographic record
Abstract
Classical threshold vs. intensity (tvi) curves were measured using optical systems and were generally limited to increment test stimuli and relatively simple spatial patterns. Modern displays provide more flexibility in terms of stimuli spatial profiles but are usually dim enough that there may be rod intrusion when measuring cone responses. Here we describe a high-brightness display system and present tvi's for increment and decrement achromatic tests. The system consists of a PROPixx three-chip DLP LED color projector (VPixx Technologies, Saint-Bruno, Canada) controlled via a Datapixx display driver, with 12-bit digital to analog conversion per RGB channel. Light from the projector is collected in a large diameter lens and focused on high gain rear projection screen. Retinal illuminance of the background may be varied in three ways: (a) varying the mean current supplied to the LEDs from the controller (adjustable in software); (b) using calibrated neutral density filters mounted near the eye; and (c) changing the midpoint of the RGB channels in software (e.g., making the white background as R=G=B=0.1 instead of 0.5). Method (c) is made easier by the fact that the PROPixx "gamma curve" is linear, which also means that no RGB bits are lost to gamma correction. We will show thresholds for achromatic tests on a white background varying from 0.56 to 4.03 log trolands, with preliminary results suggesting differences in the tvi curves between the increment and decrement tests.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.008 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".