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Record W2160792303 · doi:10.1098/rspb.2011.0374

Vision, touch and object manipulation in Senegal parrots <i>Poicephalus senegalus</i>

2011· article· en· W2160792303 on OpenAlexaff
Zoe Demery, Jackie Chappell, Graham R. Martin

Bibliographic record

VenueProceedings of the Royal Society B Biological Sciences · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsLockheed Martin (Canada)
FundersBiotechnology and Biological Sciences Research Council
KeywordsForagingObject (grammar)Visual fieldField (mathematics)CommunicationComputer visionCognitive psychologyComputer sciencePsychologyArtificial intelligenceHuman–computer interactionBiologyEcologyNeuroscienceMathematics

Abstract

fetched live from OpenAlex

Parrots are exceptional among birds for their high levels of exploratory behaviour and manipulatory abilities. It has been argued that foraging method is the prime determinant of a bird's visual field configuration. However, here we argue that the topography of visual fields in parrots is related to their playful dexterity, unique anatomy and particularly the tactile information that is gained through their bill tip organ during object manipulation. We measured the visual fields of Senegal parrots Poicephalus senegalus using the ophthalmoscopic reflex technique and also report some preliminary observations on the bill tip organ in this species. We found that the visual fields of Senegal parrots are unlike those described hitherto in any other bird species, with both a relatively broad frontal binocular field and a near comprehensive field of view around the head. The behavioural implications are discussed and we consider how extractive foraging and object exploration, mediated in part by tactile cues from the bill, has led to the absence of visual coverage of the region below the bill in favour of more comprehensive visual coverage above the head.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score0.287

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.043
GPT teacher head0.237
Teacher spread0.193 · 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

Citations87
Published2011
Admission routes1
Has abstractyes

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