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Record W2145784841 · doi:10.1088/0954-898x_14_2_308

Modular organization of adaptive colouration in flounder and cuttlefish revealed by independent component analysis

2003· article· en· W2145784841 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueNetwork Computation in Neural Systems · 2003
Typearticle
Languageen
FieldComputer Science
TopicBlind Source Separation Techniques
Canadian institutionsnot available
FundersCanadian Institute for Theoretical Astrophysics
KeywordsCamouflageCuttlefishIndependent component analysisModularity (biology)Modular designComponent (thermodynamics)Independence (probability theory)FastICAArtificial intelligenceComputer sciencePattern recognition (psychology)BiologyEvolutionary biologyMathematicsFisheryStatisticsBlind signal separationTelecommunications

Abstract

fetched live from OpenAlex

Flounders and cuttlefish have an impressive ability to change colouration, for camouflage and, in the case of cuttlefish, for communication. We pursue the hypothesis that these diverse patterns are created by combining a small number of distinct pattern modules. Independent component analysis (ICA) is a powerful tool for identifying independent sources of variation in linear mixtures of signals. Two versions of ICA are used, one assuming that sources have independence over time, and the other over space. These reveal the modularity of the skin colouration system, and suggest how the pattern modules are combined in specific behavioural contexts. ICA may therefore be a useful tool for studying animal camouflage and communication.

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.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.631
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.231
Teacher spread0.220 · 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