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Record W2003992294 · doi:10.1139/z07-033

Spectral absorbance, structure, and population density of photoreceptors in the retina of the lake sturgeon (Acipenser fulvescens)

2007· article· en· W2003992294 on OpenAlexvenueno aff
Arnold J. Sillman, E.K. Ong, Ellis R. Loew

Bibliographic record

VenueCanadian Journal of Zoology · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinal Development and Disorders
Canadian institutionsnot available
FundersNational Institutes of Health
KeywordsAcipenserLake sturgeonBiologyPopulationPigmentRodAnatomyWavelengthAbsorbanceVisual pigmentsSturgeonOpticsRhodopsinRetinalFish <Actinopterygii>ChemistryFisheryPhysicsBotany

Abstract

fetched live from OpenAlex

Lake sturgeon ( Acipenser fulvescens Rafinesque, 1817) photoreceptors were studied with scanning electron microscopy and microspectrophotometry. The retina contains both rods and cones, with cones estimated composing about 30% of the photoreceptor population. Only large single cones were identified and they are similar to those found in other species of the order Acipenseriformes. The rods are large, with long, broad outer segments, and are similar to the dominant rod found in other sturgeons and the North American paddlefish ( Polyodon spathula (Walbaum, 1792)). Mean (SD) rod packing density at 22 624 ± 3 509 rods/mm2 is low compared with those of other animals that function primarily in dim light. The visual pigment of the rods has a mean (SD) peak absorbance (λmax) at 541 ± 2 nm. Three different cone populations were identified: a long wavelength sensitive cone containing a visual pigment with λmax at 619 ± 3 nm; middle wavelength sensitive cone with λmax at 538 ± 1 nm; and short wavelength sensitive cone with λmax at 448 ± 1 nm. All the visual pigments are based on the vitamin A2 chromophore.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.004
GPT teacher head0.202
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2007
Admission routes1
Has abstractyes

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