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Record W3021365599 · doi:10.1093/beheco/araa027

Juvenile socio-ecological environment shapes material technology in nest-building birds

2020· article· en· W3021365599 on OpenAlexaff
Alexis J Breen, Keren E Lovie, Chloé Guerard, Sophie C Edwards, Jasmine Cooper, Susan D. Healy, Lauren M. Guillette

Bibliographic record

VenueBehavioral Ecology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsUniversity of Alberta
FundersBiotechnology and Biological Sciences Research CouncilUniversity of St Andrews
KeywordsTaeniopygiaNest (protein structural motif)JuvenileBiologyEcologyZebra finchNest boxEcological trapZoologyHabitatPredation

Abstract

fetched live from OpenAlex

Abstract Variation in animal material technology, such as tool use and nest construction, is thought to be caused, in part, by differences in the early-life socio-ecological environment—that is, who and what is around—but this developmental hypothesis remains unconfirmed. We used a tightly controlled developmental paradigm to determine whether adult and/or raw-material access in early life shape first-time nest construction in laboratory-bred zebra finches Taeniopygia guttata at sexual maturity. We found that juvenile access to both an unrelated adult and raw material of one color led to a majority preference (75%) by novice builders for this color of material over that for either natal-nest or novel-colored material, whereas a lack of juvenile access to both an unrelated adult and raw material led to a 4- and nearly 3-fold reduction in the speed at which novice builders initiated and completed nest construction, respectively. Contrary to expectation, neither the amount of time juveniles nor their adult groupmate spent handling the raw material appear to drive these early-life effects on zebra finches’ first-time nest construction, suggesting that adult presence might be sufficient to drive the development of animal material technology. Together these data show that the juvenile socio-ecological environment can trigger variation in at least two critical aspects of animal material technology (material preference and construction speed), revealing a potentially powerful developmental window for technological advancement. Thus, to understand selection on animal material technology, the early-life environment must be considered.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score1.000

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0380.003

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 head0.023
GPT teacher head0.262
Teacher spread0.239 · 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; both teacher heads agree on what is shown here.

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

Citations37
Published2020
Admission routes1
Has abstractyes

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