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Record W1977627410 · doi:10.4319/lo.2012.57.4.1025

Radiance fluctuations induced by surface waves can enhance the appearance of underwater objects

2012· article· en· W1977627410 on OpenAlexafffund
Shai Sabbah, Suzanne Gray, Craig W. Hawryshyn

Bibliographic record

VenueLimnology and Oceanography · 2012
Typearticle
Languageen
FieldComputer Science
TopicImage Enhancement Techniques
Canadian institutionsMcGill UniversityQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaOntario Innovation Trust
KeywordsRadianceContrast (vision)UnderwaterOpticsPhysicsFish <Actinopterygii>Spatial frequencyBiologyGeologyFisheryOceanography

Abstract

fetched live from OpenAlex

To examine the effect of wave‐induced light fluctuations on the appearance of objects to fish, we recorded the spatial and temporal fluctuations of light reflected from a diffusely reflecting target that served as a simplified proxy for the body of a fish, and of light from the water background that a fish might be viewed against. Measurements were repeated at diverse depths, viewing azimuths, distances to the substrate, and sun conditions. Two conditions that are necessary for wave‐induced light fluctuations to make objects more apparent to fish were satisfied. The contrast of light fluctuations reflected from either the object or water background was higher than the minimum contrast value that is detected by fish, or, alternatively, the contrast of light fluctuations reflected from both the object and water background was higher than the minimum contrast value detected by fish, but differed from one another. Furthermore, the frequency range where most of the power of wave‐induced radiance fluctuations matched the frequency range of maximum contrast sensitivity in fish. Thus, light stimuli having spatial and temporal characteristics similar to those of wave‐induced light fluctuations may make objects more apparent to fish. We suggest that the frequency characteristics of the visual systems of fish were likely shaped by wave‐induced light fluctuations in aquatic ecosystems.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

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

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 head0.010
GPT teacher head0.237
Teacher spread0.228 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2012
Admission routes2
Has abstractyes

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