Classical and melanopsin photoreception in irradiance detection: negative masking of locomotor activity by light
Bibliographic record
Abstract
Studies in mice lacking either classical or melanopsin photoreception have been useful in describing the photoreceptor contribution to irradiance detection in accessory visual responses. However, application of these findings to irradiance detection in intact animals is problematical because retinal degeneration or manipulation can induce secondary changes in the retina. Among responses dependent on irradiance detection, the suppression of activity by light (negative masking) has had limited study. To further understand the function of classical and melanopsin photoreceptors we studied irradiance and spectral sensitivity of masking by light, primarily in mice with intact retinae. The sensitivity of negative masking was equivalent for medium ( approximately 500 nm) and short wavelengths ( approximately 365 nm) in three strains of wild-type mice, identifying a marked short-wavelength-sensitive-cone input. At medium wavelengths, spectral sensitivity above 500 nm had closest fit to the nomogram for the medium-wavelength-sensitive-cone, but a combined input of cone and melanopsin photoreceptors in wild-type mice seems likely. Under white light a decompression of the irradiance range of masking in C3H rd/rd cl mice, lacking rods and cones, identified a functional deficiency presumably resulting from the absence of classical photoreceptor input. Together the evidence demonstrates a pronounced and sustained classical photoreceptor input to irradiance detection for negative masking, and suggests one role of classical photoreceptor input is to constrain dynamic range.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; 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".