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Record W2502149142 · doi:10.19227/jzar.v4i3.231

Music as enrichment for Sumatran orangutans (Pongo abelii)

2016· article· en· W2502149142 on OpenAlexaff
Sarah Ritvo, Suzanne E. MacDonald

Bibliographic record

Venue˜The œJournal of zoo and aquarium research · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsYork University
Fundersnot available
KeywordsSilencePsychologyPreferenceMusicalPongo pygmaeusCommunicationCognitive psychologyMusic educationVisual artsAestheticsArtZoologyBiologyPedagogy

Abstract

fetched live from OpenAlex

Music is commonly employed as auditory enrichment in NHP facilities under the assumption that music is as enriching for NHPs as it is for humans (Hinds et al., 2007; Lutz & Novak, 2005). The purpose of this study was to assess the utility of music as NHP enrichment by exploring musical preference and discriminative ability in three Sumatran orangutans. In Experiment 1, orangutan preference for music vs. silence was tested. Following exposure to a sample of music belonging to one of seven musical genres, orangutans were given the choice via touchscreen to continue to listen to the music sample previously played or to listen to silence instead.  Results indicated that all three orangutans either preferred silence to music or were indifferent.  No preference for any one of the musical genres tested over others was found.  In Experiment 2, orangutans’ ability to discriminate music from scrambled music was assessed using a touchscreen-delivered standard delayed matching-to-sample (DMTS) task. Results indicated that none of the three orangutans could reliably discriminate ‘music’ from ‘scrambled music’.  Taken together, results strongly suggest that these orangutans did not experience the musical stimuli as reinforcing and that use of music as enrichment in captive NHP facilities may be more aversive than enriching for some 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 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)0.002
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.765
Threshold uncertainty score0.168

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.080
GPT teacher head0.383
Teacher spread0.303 · 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 designBench or experimental
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

Citations28
Published2016
Admission routes1
Has abstractyes

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