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Record W2150476139 · doi:10.1098/rstb.2010.0190

Underwater linear polarization: physical limitations to biological functions

2011· review· en· W2150476139 on OpenAlexaff
Nadav Shashar, Sönke Johnsen, Amit Lerner, Shai Sabbah, Chuan‐Chin Chiao, Lydia M. Mäthger, Roger T. Hanlon

Bibliographic record

VenuePhilosophical Transactions of the Royal Society B Biological Sciences · 2011
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicbioluminescence and chemiluminescence research
Canadian institutionsQueen's University
Fundersnot available
KeywordsPolarization (electrochemistry)UnderwaterEnvironmental sciencePlanktonRemote sensingPhysicsOceanographyGeologyChemistry

Abstract

fetched live from OpenAlex

Polarization sensitivity is documented in a range of marine animals. The variety of tasks for which animals can use this sensitivity, and the range over which they do so, are confined by the visual systems of these animals and by the propagation of the polarization information in the aquatic environment. We examine the environmental physical constraints in an attempt to reveal the depth, range and other limitations to the use of polarization sensitivity by marine animals. In clear oceanic waters, navigation that is based on the polarization pattern of the sky appears to be limited to shallow waters, while solar-based navigation is possible down to 200-400 m. When combined with intensity difference, polarization sensitivity allows an increase in target detection range by 70-80% with an upper limit of 15 m for large-eyed animals. This distance will be significantly smaller for small animals, such as plankton, and in turbid waters. Polarization-contrast detection, which is relevant to object detection and communication, is strongly affected by water conditions and in clear waters its range limit may reach 15 m as well. We show that polarization sensitivity may also serve for target distance estimation, when examining point source bioluminescent objects in the photic mesopelagic depth range.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.981
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.190
GPT teacher head0.351
Teacher spread0.161 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations43
Published2011
Admission routes1
Has abstractyes

Explore more

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