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Record W3033932541 · doi:10.1038/s41598-020-65156-0

The endocast of the Night Parrot (Pezoporus occidentalis) reveals insights into its sensory ecology and the evolution of nocturnality in birds

2020· article· en· W3033932541 on OpenAlexafffund
Andrew N. Iwaniuk, Aubrey R. Keirnan, Heather Janetzki, Karine Mardon, Stephen Murphy, Nicholas P. Leseberg, Vera Weisbecker

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsUniversity of Lethbridge
FundersCanada Research Chairs
KeywordsEndocastEcologyBiologyZoologyGeographyEvolutionary biologyAnatomy

Abstract

fetched live from OpenAlex

The Night Parrot (Pezoporus occidentalis) is a rare, nocturnal parrot species that has largely escaped scientific investigation due to its behaviour and habitat preferences. Recent field studies have revealed some insights into Night Parrot behaviour, but nothing is known of its sensory abilities. Here, we used μCT scans of an intact Night Parrot specimen to determine if its visual system shares similarities with other nocturnal species. The endocast of the Night Parrot revealed relatively small optic lobes and optic foramina, especially compared with closely related grass parakeets, but no apparent differences in orbit dimensions. Our data suggests that the Night Parrot likely has lower visual acuity than most other parrots, including its congener, the Eastern Ground Parrot (P. wallicus). We propose that the visual system of the Night Parrot might represent a compromise between the need to see under low light conditions and the visual acuity required to detect predators, forage, and fly. Based on the endocast and optic foramen measurements, the Night Parrot fits into a common pattern of decreased retinal input to the optic lobes in birds that should be explored more thoroughly in extant and extinct species.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.232
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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

Citations17
Published2020
Admission routes2
Has abstractyes

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