Ultraviolet Polarization Vision and Visually Guided Behavior in Fishes
Bibliographic record
Abstract
Teleost fishes are capable of detecting and behaviorally responding to linearly polarized light. Fish exhibit free-swimming spatial orientation to imposed and natural polarized light fields, and the fidelity of this spatial orientation depends heavily on UV and short wavelength content of the polarization field. Fish make fine-scale behavioral discriminations between stimuli that differ in e-vector orientation, independent of brightness. The detection of polarized light by photoreceptors is based on specializations of the disk membrane in the outer segment of cones that permit preferential absorption of axial and transverse polarized light. Differential polarization detectors that have overlapping spectral sensitivity in the UV short wavelength spectrum mediate polarization sensitivity. These differential detectors are based on cone photoreceptors that share spectral sensitivity in the UV short wavelength spectrum: the alpha-band of UV-sensitive cone mechanism as the vertical detector, and the beta-band of mid- and long-wavelength sensitive cone mechanisms as the horizontal detector. Negative feedback of horizontal cells on cones govern opponent interactions between differentially sensitive polarization detectors. Polarization opponency functions to enhance e-vector contrast under conditions that vary in degree of polarization and ambient intensity. Ontogenetic changes in the cone mosaic, resulting from programmed cell death and regeneration of UV-sensitive cones, alter the retinal location of polarization sensitivity. These developmental changes greatly influence behavioral responses to polarized light.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".