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Record W2139979205 · doi:10.4161/cib.17318

Comparing geometric models for orientation: Medial vs. principal axes

2011· article· en· W2139979205 on OpenAlex
Debbie M. Kelly, Stéphane Durocher

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCommunicative & Integrative Biology · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCephalopods and Marine Biology
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsOrientation (vector space)Principal axis theoremGeometryEncoding (memory)SalientRepresentation (politics)Principal (computer security)PropositionMedial axisGeometric shapeGeometric modelingComputer scienceMathematicsArtificial intelligenceComputer vision

Abstract

fetched live from OpenAlex

Research examining the encoding of geometry for orientation has received considerable attention in the last 25 years with the proposition of a geometric module.1 Supporting the importance of geometry in the formation of a spatial representation, to date the majority of species studied show an encoding of geometry, even when presumably more salient and reliable features could be used. Although studies have shown that animals encode geometric information such as distance, direction or angular amplitude from the environment, few have tested the assumption that geometry is encoded using global properties such as the major principal axis, a strongly supported proposition. Here we present an alternative model to principal axis, specifically medial axis. In addition we describe the straight skeleton model, which may also offer insights into the understanding of geometric encoding by orienting animals.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score0.555

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.145
GPT teacher head0.309
Teacher spread0.163 · 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