Microspectrophotometric measurements of vertebrate photoreceptors using CCD-based detection technology
Bibliographic record
Abstract
We have developed a charge-coupled-device (CCD)-based microspectrophotometer (MSP) system and provide the first report on the successful employment of this technology to measure the spectral absorbance properties of vertebrate photoreceptors. The principal difference between the CCD-based MSP system and wavelength-scanning MSP systems, commonly used in vision biology, is that a short duration (800-1200 ms), broad-spectrum flash is employed rather than ascending and descending wavelength scanning. Data acquisition is thus significantly faster, with the added possible advantages of less variance due to movement of target photoreceptors during measurement, reduced spectral distortion due to photoproduct interference and an ability to measure fast, transient changes in absorbance as bleaching proceeds. Rainbow trout photoreceptors, previously measured with a wavelength-scanning MSP system, were again measured using the CCD-based MSP system. Our analysis of optical recordings from 102 photoreceptors corroborated data obtained previously with rainbow trout photoreceptors on lambda(max) (wavelength of maximum absorbance), A(max) (maximum absorbance) and half maximum bandwidth (HBW) of ultraviolet-, blue-, green- and red-sensitive cones and rods. There were slight differences in lambda(max) and half-maximum bandwidth of the ultraviolet-, blue- and green-sensitive cone classes, but this was most probably due to variation in the A(1):A(2) visual pigment ratio of the trout used in the two different studies. However, we were capable of resolving the A(1) and A(2) visual pigment spectra in the red-sensitive cones and the rods.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".