Chromatic parameters derived from increment spectral sensitivity functions
Bibliographic record
Abstract
We propose a mathematical model to derive the chromatic parameters from increment spectral sensitivity functions. This model was applied to determine the effective red, green, blue, and yellow mechanism contribution to the detection of the spectral stimuli of five normal trichromatic subjects. Detection thresholds were measured for a 300 ms, 1.2 degrees circular test flash presented on a 100 cd/m2 white background for spectral wavelengths between 410 and 660 nm. The model analysis confirmed that in the red-green wavelength area, the detection of our chosen stimuli was mediated by two distinct (L-M) antagonistic mechanisms: a red-green and a yellow, from the blue-yellow system. We inferred that the red-green mechanism receptive fields consisted of a single L- or M-cone center with a homogeneous or heterogeneous surround devoid of S-cone projections. For the receptive fields of the yellow half of the blue-yellow mechanism, we propose a similar configuration but with S-cone projections present in the surround. This proposal is not concordant with what is currently understood regarding retinal physiology. However, two L-M antagonistic mechanisms in the red-green wavelengths as proposed by our results predict what would appear as an intuitive yellow mechanism with a maximal sensitivity at the 578 nm wavelength, where the red-green mechanism sensitivity is null.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".