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Record W2039998895 · doi:10.1139/z99-243

The use of visual cues for spatial orientation in the sleepy lizard (<i>Tiliqua rugosa</i>)

2000· article· en· W2039998895 on OpenAlexvenueno aff
Ido Zuri, C. Michael Bull

Bibliographic record

VenueCanadian Journal of Zoology · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsnot available
FundersAustralian Research Council
KeywordsLizardSkinkBiologyOrientation (vector space)RugosaZoologyEcologyGeometry

Abstract

fetched live from OpenAlex

The sleepy lizard (Tiliqua rugosa) is a large, long-lived terrestrial Australian skink. In the present study we investigated the ability of sleepy lizards to use different visual cues for spatial orientation. The lizards were trained to locate shelters in certain places and then trained to certain signals associated with their shelters. In the absence of surrounding visual cues the lizards preferred familiar sites that were previously associated with their shelters. However, when presented with signals that had been associated with their shelters, they chose the vicinity of these familiar signals, even after their displacement to new sites. The lizards discriminated between black and white signals and between triangular and circular signals but not between red and green signals. Previous studies had shown that sleepy lizards exhibit home-range fidelity, raising the question of which environmental cues are important for them for spatial orientation within their home ranges. We suggest that the ability of sleepy lizards to discriminate between visual signals of different shapes and degrees of brightness enables them to "memorize" certain fixed landmarks in their large home ranges and to orient accordingly.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.709
Threshold uncertainty score0.996

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 science0.0000.000
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 head0.037
GPT teacher head0.258
Teacher spread0.221 · 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 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

Citations29
Published2000
Admission routes1
Has abstractyes

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